It is well-known that the analog FM radio channels in suburban areas are underutilized. Before reallocating the unused channels for other applications, a regulator must analyze the spectrum occupancy. Many researchers proposed the spectrum occupancy models to find vacant spectrum. However, the existing models do not analyze each channel individually. This paper proposes an approach consisting (i) a spectrum measurement strategy, (ii) an appropriate decision threshold, and (iii) criteria for channel classification, to find the unused channels. The measurement strategy monitors each channel’s activity by capturing the power levels of the passband and the guardband separately. The decision threshold is selected depending on the monitored channel’s activity. The criteria classifies the channels based on the passband’s and guardband’s duty cycles. The results show that the proposed channel classification can identify 42 unused channels. If the power levels of wholebands (existing model) were analyzed instead of passband’s and guardband’s duty cycles, only 24 unoccupied channels were found. Furthermore, we propose the interference criteria, based on relative duty cycles across channels, to classify the abnormally used channels into interference sources and interference sinks, which have 16 and 15 channels, respectively. This information helps the dynamic spectrum sharing avoid or mitigate the interferences. The radio spectrum is part and parcel of wireless communication systems, such as television and radio broadcasting, satellite communication, mobile communication, a global positioning system (GPS), BlueTooth, and wireless local area network (WLAN). Federal government agencies in various countries organize these limited resources to meet users’ demands. Mainly, the spectra for radio broadcasting and mobile communication are more extensive than other activities due to their utilization. Many campaigns have been monitoring the broadcasting spectrum ,7,8,9,10Many researchers have investigated advanced technologies to access unused spectrums for FM radio broadcasting. Reference  proposedThe researchers have developed techniques for sensing the occupying state of a frequency band. The measurement campaigns based on those sensing techniques pointed out that many frequency bands were underutilized and encouraged the regulators to modernize spectrum sharing by replacing the static spectrum access with the dynamic spectrum access. The spectrum utilization efficiency has been heavily studied since 1977  is one oWe observed that there are two knowledge gaps in the literature. First, although many methods to monitor radio spectrum occupancy have been proposed, Reference  implies to modify the spectrum measurement strategies based on the concepts of References ,21 and tto devise the implementation of Reference ’s concepto improve the accuracy in evaluating the spectrum occupancy of FM radio public bands by the proposed measurement strategies and threshold designation, as well as to gain insights into why the proposed strategies offer higher accuracy than the existing strategies,to gain insight into the pattern of spectrum occupancy of FM radio public bands in suburban areas, andto identify the channels, which cause interference, and the channels, which are affected by the interference.The essential information for the regulators to begin the FM radio spectrum reallocation is the visualized current utilization of the spectrum. A general-purpose spectrum occupancy model gives a less reliable utilization information because it ignores the different characteristics of the application in each frequency band. Specifically, the interference generated from older transmitters of the broadcasting stations is only found in the FM radio frequency band and is not taken into account by general models. Usually, the obtained utilization information fails to help the regulators distinguish the spectrum occupied by a primary user from that occupied by an interference. Therefore, the regulators need a new model that is particularly developed for the FM radio spectrum to obtain the more reliable utilization information. This paper aims to develop the spectrum occupancy model that is composed of the focus-purpose measurement strategies, decision threshold technique based on empirical data, occupancy classification, and interference classification. Thus, the paper’s central research objectives are:To demonstrate the proposed spectrum occupancy model, we select a suburban area where the conventional radio broadcasting system is still used but not 24 h a day. Moreover, the transmitting system in the suburban area frequently causes interference in some FM channels and other adjacent bands. Thus, the measurement campaign was conducted in Nakhon Si Thammarat province, Thailand. Most broadcasting stations are located densely, and a few of them are near the airport. When visualizing the spectrum occupation from the measured data with the conventional model, it is found that the number of the spectrums are being used more than licensed users. This is in contrast to our expectation that the spectrum occupation is underutilized, like in other countries ,7,8,9,10The remainder of this article is structured as follows. To improve the spectrum utilization of FM radio broadcasting, it is necessary to classify the occupied channel’s state, which can be either user occupied or interference occupied, by using the proposed spectrum occupancy model. To evaluate the model’s performance, we conducted the Thailand measurement campaigns, which operated analog FM radio broadcasting throughout the country. It serves as a mass communication channel where the government can send information to people more efficiently. It has been prevalent because of the low cost and availability since the 1940s. The passive urbanization and the severe inequality problem in most developing countries, such as India and Thailand, necessitate public broadcasting rather than mobile communication. Although the suburban and rural populations in Thailand are decreasing by 10 million people over the previous decade, there are still more than 30 million people. These people cannot access data on the internet via a smartphone, which has a high initial cost, as well as a monthly expense. Therefore, half of the Thai population living in suburban and rural areas have demanded public FM radio broadcasting.The public FM radio broadcasting network in Thailand covers over 76 provinces, and all of them are conventional analog systems. The frequency band used for FM radio is between 88–108 MHz and is split into 81 licensed channels. Each channel has a bandwidth of 250 kHz, including a passband of 150 kHz and two guardbands of 2 × 50 kHz. The whole country area is divided into 14 regions to reuse those 81 licensed channels. Each region has one radio monitoring control center. Nakhon Si Thammarat, which is an exception, has its own radio monitoring control center because its area is large (9.9 million square kilometers). Another reason is that the populations are dense at the center of the region since their living space locates between the Kao Lung mountain and the sea in the Gulf of Thailand. Previous studies in other countries pointed out that the FM spectrum was underutilized. However, when we used the conventional measurement strategy and the ITU threshold, our preliminary experimental results revealed that the FM spectrum in Thailand was not underutilized. This contradiction indicated that the threshold depends on the environment. It is necessary to analyze a new threshold to evaluate the spectrum occupancy. We hypothesize that some channels observed to be used might actually be occupied only by interference. Accordingly, it is of interest to be able to distinguish broadcasting activities from interference. In general, there are many noise sources, such as intermodulation, harmonic noise, and adjacent channels. These noise effects are more serious when the broadcasters keep a high signal to noise ratio by increasing the transmitter’s power. It affects the FM broadcasting network and disturbs other communication networks, such as the aeronautical system in the airband (108–137 MHz). Within a 20-kilometer radius of the airport, more than ten channels in the airband are frequently interfered. Moreover, within a 10-kilometer radius of the city center, intermodulation will occur with more than 70 channels in FM bands if all radio stations are active.The existing spectrum measurement strategies in the literature are either for mobile communication  or for FTo obtain the information for characterizing the pattern of spectrum occupancy and forming the criterion, which is used to distinguish the occupancy state between user occupied and interference occupied, we need the effective measurement strategies that are formulated with the focused-purpose technique, instead of the existing technique. This information containing the power level represented over time and frequency is essential for detecting the used spectrum and interference. The approach focusing on the time and frequency dimensions can provide sufficient information to estimate the spectrum occupancy due to the fixed spatial location of the FM radio broadcasting station.When focusing on the frequency dimension strategies, Reference  points oFor the time dimension strategies, References ,30 definThe designed measurement strategy requires a low noise figure and high sensitivity measurement system, as well as a large number of frequency points per span. To meet the requirements, we must employ a measurement system that works autonomously and quickly detects the spectrum occupied by the interference. Generally, the measurement system comprises the measurement devices and software for data processing. The measurement system can be developed based on either Software-Defined Radio (SDR) platforms or high-performance spectrum analyzers. For example, considering the FM band exploration, References ,34 formuThe implementation of the SDR platform is not a choice of the requirements of the proposed measurement strategies because their sensitivity is too low when capturing the power level among high interference. Then, we filter the high-performance spectrum analyzers which have the specifications in frequency and time dimensions, as well as the ability to process the spectrum automatically. For the strategies in the frequency dimension, the detailed specifications are (i) able to capture the weak signals among the noise in the frequency range of measurement study with marker tracking function, (ii) able to filter the signal in the frequency range of measurement study with the high-performance bandpass filter, and (iii) able to observe the spectrum of all the radio channels in an entire band. For the strategies in the time dimension, the detailed specifications are (i) able to simultaneously detect the presence of signals with very different power levels, (ii) able to routinely send collected data to the computer, and (iii) able to make a pre-processing for a large number of measured data. Accordingly, we choose a high-performance spectrum analyzer in the series of the Keysight NB9340B to construct the measurement device.With the performance of the Keysight NB9340B, it can estimate the spectrum over 87.375 to 107.875 MHz by using the proposed strategies and can process the large amount of power level data which represent the activities of all 81 channels. At a sample point of the measurement period, it directly monitors all the activities from 405 segments (81 channels × 5 segments) where each activity is investigated from the 50 kHz-width segment . Within a channel, the passband and guardband have three and two acting activities, respectively. While monitoring the activity within a segment, it collects the 461 trace points of power levels, and the bandwidth between two consecutively measured frequency points, which is referred to as the frequency bin, equals 109 Hz. It spends approximately 1.003 s to capture 121,704 power level samples when setting the RBW at 3 kHz. When finishing one routine, the system determines the average value before recording it in the memory.To obtain the measured data from the measurement system, we made the command script which was used to control the pre-processing on the high-performance spectrum analyzer and was used to characterize those data on the laptop before analyzing with the spectrum occupancy model in the next step. The first part in the command script built an interface between spectrum analyzer and laptop with serial number and established communication with the GPIB/USB port and VISA library. Next, the script had 410 loops which equaled the number of the segments in one measurement period. At the beginning of the loop, the script enabled the VISA Open Block to open a session for the device. Next, the command script adopted and sent the center frequency and frequency span to the instrument through VISA Agilent Write Block. The command script transferred the measurement parameters to the spectrum analyzer with following parameters: Attenuation 0 dB, RBW 3 kHz, VBW 3 kHz, Averaged Mode, and enabled Marker. They were encoded into the SCPI commands to set up the initial configuration of the instrument via VISA Write Block. After that, the device swept across the 461 frequency bins to capture the power levels. It took approximately 3.8 ms during one sweep. After capturing the data at the last frequency bin, the command script controlled the device repeatedly for 263 more times with the Time-Delay Block to achieve the next segment’s power level. After all 264 sweeps were complete, the system collected these data and saved them in an array  when the instrument received the SCPI command from VISA Read Block. Then, the script enabled the VISA Close Block. After finishing one loop, the system restarted the next loop immediately and so on until the last loop. Next, the command script exported the arrays of values from all measurement loops to a spreadsheet file. Finally, the communications of the spectrum analyzer and the instrument session were closed, respectively.This section determines a decision threshold, which is used to be a criterion for classifying the status of the spectrum occupancy. To improve the evaluation of spectrum utilization, numerous researchers pay attention to study the decision threshold, which is a function of the frequency band and the environment. Reference  categoriThis subsection collects the decision thresholds used in earlier measurement campaigns to know the possible range of the thresholds for evaluating FM radio spectrum utilization. Almost all campaigns that survey and compare the spectrum usage over different frequency bands, such as FM radio band, radar band, maritime navigation band, TV broadcasting band, digital cellular services band, unlicensed Industrial, Scientific and Medical (ISM) band, wireless broadband access (WBA), and satellite band. They choose to use the single decision threshold either by following the ITU recommendation or by estimating noise characteristics in their radio environments. For example, the ITU recommendation in Reference  suggestsThe decision threshold analysis that is specific to the environment for mobile communications was proposed in Reference . Howeverc and time instance s is the index of the segment in each channel. In The spectrum density map, a type of a heat map, is a technique of visualizing three-dimensional data where the intensity of color presents the measured signal power varying with frequency and time. First, we arrange the measured data in matrix form, as shown in Equation :(1)P=P˜cSecond, to search the threshold, it should be lower bounded by the noise floor to distinguish signal from noise. The noise floor can be estimated from the matrix In addition, the threshold should be upper bounded by the peak of the power level over all sampled data. The peak value can be estimated the matrix The obtained lower bound and upper bound determine the range for searching the particular decision threshold, as will be shown in s is the index of the segment in each channel, and s of channel c and time instance The channel occupancy map visualizes three dimensional data where the black/white color presents the occupancy state, varying with the frequency and time. First, we arrange the state of occupancy in matrix form, as shown in Equation :(5)Q=Q˜cSecond, to achieve the interval of the particular threshold according to the usage of the channels in the environment, we should exploit the prior knowledge about those occupied channels. For example, given that the occupied channel is a full-time case, the power signal is time-varying. Therefore, we calculate the total of the channel occupancy state The interval of the particular threshold should be the interval of power signal levels that all channels have a constant occupation percentage. To be more efficient when classifying the type of activities, we select some channels instead of using all channels to get a mirror of diary activity, such as active and no activities, by using the obtained average value T, the duration of time instance c is the index of channel in Equations  to  to T, thtions γ. :(10)Dc=1Second, we initialize the search for the decision threshold by randomly picking up a decision threshold Equation , and makEquation  as a funThird, before determining the interval of possible decision threshold, we select all channels with high SNR (insignificantly affected by interference) by observing two characteristics: (i) having a constant percentage over a long interval in the middle of the curve and (ii) transiting abruptly to zero near the end of the curve. Accordingly, we classify the channels, based on their duty cycle curves observation, into four classes: strong activity, good activity, no activity, and weak activity by the following criteria, as shown in A channel has strong activity if its curve has constant occupation percentage while the value of A channel has a good activity if its curve has constant occupation percentage while the value of A channel has no activity if its curve has constant occupation percentage while the value of A channel has weak activity if its curve has constant occupation percentage over many intervals.Fourth, we determine the interval on which all curves, classified as strong or good or no activity, are constant. In other words, when observing all the curves, classified as strong or good or no activity, the intersection of the constant intervals of all curves is the interval of particular threshold. Notice that we exclude the curves, classified as weak activity, from considering the interval of particular threshold since those curves are presenting the power level near the interference. Denote the obtained interval by [Since the obtained interval has a constant duty cycle for the strong, good, and no activities, it is not guaranteed to be constant for the weak activity. Fifth, considering all channels classified as weak activity, we define This section analyzes the appropriate decision thresholds with the proposed technique in We wrote the random signal power of all 81 channels, having a frequency of 87.375 MHz to 107.875 MHz, in matrix form, c with an c with an We determined the occupancy states of all segments in each channel by comparing the power levels with the standard threshold at 10 dB, recommended by ITU, and wrote them in matrix form, Equation , of 12 wTo achieve the appropriate decision threshold, we accessed the prior knowledge of noise properties and the environment through the duty cycle curves of all activities’ channels, including strong, good, weak, and no activities. We plotted the Equation  and by sEquation . These cIt can be noticed that only the channels with strong activity have the occupation percentage curves with triple slopes, i.e., two breakpoints. The proposed model analyzes the characteristics of spectrum occupancy by using both breakpoints as the boundary of the particular threshold. Thus, the proposed model will perform well if there are some channels that have strong activity. It should be careful to arrange the measurement setup in order to confirm that the channels having the strong activity are available. For example, the number of the measurement periods should be enough to obtain meaningful measurement data. As another example, the resolution bandwidth should be narrow enough so that the detected signals have high SNR.Theoretically, the evaluation of spectrum usage will not have an error if the broadcasting station emits the signal that occupies only the licensed spectrum. But, in practice, the broadcasting station might illegitimately emit the signal that interferes with the licensed spectrum of other broadcasting stations. For example, when the broadcasting station increases the SNR of its channel by emitting a signal with a higher power level, the unwanted emission that interferes with the licensed spectrum of the adjacent channels is not negligible. Then, based on conventional spectrum occupancy evaluation, the interfered channels will be mistakenly considered occupied. To improve the accuracy in evaluating spectrum occupancy, we use the measured power level over the channel’s passband instead of the power level over a whole bandwidth (both passband and guardband) of the channel while determining the occupancy status in Equation . In pracc, denoted by c, denoted by Firstly, we exploit the measured power level of passband and guardband within the same channel by representing them in two terms: (i) the passband’s duty cycle and (ii) the guardband’s duty cycle. The passband’s duty cycle of Channel Equation , where tEquation  are deteEquation , where tEquation  are deteA channel is used normally if A channel is used abnormally if A channel is unused normally if A channel is unused abnormally if In practice, the used abnormally and the unused abnormally channels are the channels in which the guardbands are occupied by unwanted emitting signals. Based on the proposed criteria in c and those of two adjacent channels .In order to search the occupied channels with the results from the evaluation of channel occupancy, we use the power density in a whole channel to estimate all channels’ duty cycles with the conventional technique. We estimate the duty cycles by using Equations –12) and and12) aTo further classify the non-zero duty cycles into two groups, namely the used channels and the unused channels, we estimate the duty cycle again, where the passband and guardband of each channel are considered separately. The passband’s duty cycle for each channel quations –12) and andDcP cquations –(12) andTo find all unused channels, we use the proposed criteria to classify the used and unused channels as described in To further identify the channels that are unused but are occupied by interference, we use the proposed criteria in To further identify the channels that interfere with adjacent channels and cause an error when evaluating the spectrum occupancy, we use the proposed criteria in This paper presents the spectrum occupancy model for suburban environments to investigate the unused channels in the spectrum of FM radio broadcasting. The model is composed of three components: (i) the spectrum measurement method, (ii) the appropriate decision threshold, and (iii) the criteria for channel classification. To validate the proposed model, the obtained results were compared with that obtained from the conventional model. Both conventional and proposed models were experimented in the FM radio broadcasting network in a province of Thailand to classify the unused channels from all 81 licensed channels. The selected area has suburban environments, and interferences are frequently found according to our preliminary measurement results. Conventionally, the channels are simply classified as occupied or unoccupied channels. The opportunities for spectrum sharing are quite limited because some channels, classified as occupied, are actually unused. This can happen when the unused channels get interfered from adjacent analog broadcasting channels. On the other hand, the proposed criteria for channel classification can distinguish the channels that are really used from the channels that are actually unused. For example, in the demonstrated case, the conventional classification technique found 24 unoccupied channels, while the proposed classification technique found 42 unused channels. Since the proposed model can distinguish the unused channels from the channels that are occupied only by interference, they can be adopted in sensing systems of the networks with spectrum sharing, for example, digital audio broadcasting (DAB) and dynamic spectrum access (DSA).Furthermore, this paper presents interference criteria to classify the abnormally used/unused channels into the channels, interfered from adjacent channels, and the channels that are obviously the sources of interference. The criteria found 15 interfered channels and 16 channels that are the sources of interference. These 15 channels are unused but have interference. Thus, before assigning them for secondary users, we should prepare a plan to mitigate the interference. In addition, the knowledge of those 16 channels, which are the sources of interference, are useful information for cognitive radio networks to avoid assigning their adjacent channels to secondary users.


Ⓡ is one of the leading tools used by the Li-ion battery modeling community to simulate the electrochemical dynamics of the Li-ion batteries. However, in its current implementation of the underlying model equations, the Li-ion battery module in COMSOL 5.4 cannot be used to simulate the electrochemical behavior of an electrode with anisotropic tortuosity. In this work, we show how the current implementation can be modified to simulate the anisotropic case.•The existing Li-ion battery model in COMSOL 5.4 was extended to account for the anisotropy in the electrode tortuosity•Highly ordered laser-patterned electrode (HOLE)The extended model is necessary to accurately simulate the electrochemical dynamics of 3D architectures such as •The testing and validation results are included in this workLi-ion battery electrodes, such as widely used graphite anodes, may have anisotropic tortuosity due to the non-equiaxed shape of the active material particles and the post-casting calendaring process. Such anisotropy can be ignored in conventional electrodes because all the macroscopic ion transport occurs along the electrode thickness, making the ion transport effectively one dimensional. However, the anisotropy becomes important to consider with three-dimensional architectures, such as those generated by laser-patterning. COMSOL Multiphysics  Specifications tableLi-ion batteries have become immensely popular mobile energy storage devices in our modern lives because of their high energy density and moderately long life. To gain further insights into the underlying mechanisms and to further improve the performance of these devices, electrochemical simulations are widely used. These simulations are based on the model developed by Doyle at al. Standard Li-ion battery electrodes exhibit anisotropic tortuosity in the electrolyte phase, owing to the non-equiaxed shape of the active material particles and the manufacturing technique, post-casting calendaring, used to make them. For example, a graphite anode was reported to have a through-plane  tortuosity factor that is four times the in-plane value, for an electrode porosity of 40% Ⓡ is a widely used tool by the Li-ion battery modeling community for solving the coupled partial differential equations of the Doyle model COMSOL MultiphysicsThe existing implementation of the model equations in COMSOL can be accessed from the porous electrode sub-node in the Li-ion battery module by enabling the “Equation View” option. It can be noted from 3) and the electrolyte potential (V), respectively. The reaction source term is denoted by For a porous medium, the effective transport property of the medium is a function of the intrinsic value of the property, the phase volume fraction, and the tortuosity Thus, it can be seen that the correction factor is a function of the electrode tortuosity. Now for an electrode with anisotropic effective diffusivity (due to an anisotropy in the microstructure), the correction factor would have different values along different axes. As mentioned before, the current COMSOL implementation assumes the electrode to have isotropic tortuosity, and thus, it only uses one correction factor We note that the suggested modification assumes that the off-diagonal components in the ionic conductivity tensor Entry 5,  are zeroThe equations for the electronic current density Our proposed modification in the model equations affects only the diffusion and migration dynamics of ions within a porous electrode. Therefore, the modification can be tested and verified by comparing individually the diffusion and migration dynamics obtained from the modified model with the solutions of the diffusion and migration equations, respectively, as described below.Two tests were performed to examine our proposed modification, namely, the diffusion dynamics test and the migration dynamics test. Since our modification is valid for any porous electrode, we carried out the proposed modification only in the anode for the purpose of these tests. In each test, we first simulated the anisotropic dynamics, under certain boundary conditions, using the modified battery model.Thereafter, we compared the spatial distribution of the relevant physical quantity (electrolyte concentration for the diffusion dynamics test and electrolyte current density for the migration dynamics test) with those obtained from numerical solutions of the diffusion/migration equation in a three-dimensional domain with the same dimensions as the anode and subjected to the same boundary conditions.The model geometry used in the modified battery model is shown in For the diffusion dynamics test, we compared the evolution of the electrolyte concentration obtained from the modified battery model under a specific set of boundary conditions with that obtained from the numerical solution of the diffusion equation in a three-dimensional domain with a corresponding setup.The conditions for the anisotropic diffusion test are as follows. The initial electrolyte concentration in the cell was set at 1 M. Dirichlet boundary conditions  and anisotropic (right column) cases in ogeneous d, which In conclusion, the results discussed in this section highlight the importance of considering the effect of anisotropic tortuosity while studying the electrochemical performance of electrodes with three-dimensional architectures.In this work, we have shown how the existing implementation of the Doyle model in COMSOL 5.4 can be enhanced to account for anisotropic tortuosity in porous electrodes. We described the implementation details of our proposed modification and provided verification results that were compared to numerical solutions. Furthermore, we demonstrated the application of the modified implementation to simulate the 4C performance of a laser patterned (HOLE architecture) graphite anode with and without anisotropic tortuosity.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information. Recently, location-based services (LBS) have become increasingly popular in indoor environments . With thAt present, the research on the PDR method has become a hot topic, but there is still a lack of comprehensive evaluation. PDR method provides an indoor positioning method including three aspects: heading estimation, stride detection and step length estimation ,8. Due tThe traditional pedestrian dead reckoning can achieve two-dimensional positioning, and the current research on three-dimensional pedestrian dead reckoning is relatively limited . At presAs a result, to improve the tracking performances of pedestrian indoor positioning, this paper proposes a 3D indoor positioning method based on the built-in MEMS sensors of the smartphone. A robust adaptive cubature Kalman filtering based on the observability of the parameters is used to estimate the heading of the pedestrian. Moreover, the step length estimation can be calculated by the outputs of the accelerometer. The optimal step length and heading can be calculated by the KF. A 16-wind rose map calculation strategy is used to further optimize the pedestrian heading. Moreover, the pitch angle, total accelerometer and barometer of the smartphone are used to distinguish different types of behavior patterns. According to the characteristics of each behavior pattern, the pedestrian three-dimensional position is calculated for each effective step. When pedestrians walk, not only the barometer is used to measure height, but also the changes of pitch angle, accelerometer and barometer are used as the judgment basis of behavior mode. At the same time, the step width of the prior building information is taken as the step length, and the step height is taken as the height change value of each step in the step frequency detection method. So the stair information and the effective step length are used to calculate the position information. Combined with the differential barometric altimetry and step frequency detection method, the optimal height solution can be computed by the robust adaptive Kalman filter (RAKF) algorithm. By extracting different types of effective strides, the three-dimensional coordinate values of pedestrians with different walking patterns are calculated to realize the three-dimensional indoor positioning of pedestrians. The proposed method can easily be used at any site because the method does not require any anchor or physical map information. Based on the movement law of pedestrians, the proposed 3D indoor positioning method can effectively reduce the influence of sensor cumulative error on position calculation, and improve the positioning accuracy. Our contributions are as follows:Firstly, a robust adaptive cubature Kalman filter algorithm is proposed for attitude and heading estimation based on MARG sensors. Based on the fading memory weighted method and the limited memory weighted method, the model noise parameters are adaptively corrected. A robust M-estimator is used to identify and control measurement outliers and an adaptive factor is used to overcome the influence of the filter model errors and abnormal disturbances. Second, the heading and step length of each step are optimized by a Kalman filter to reduce positioning error. Based on the indoor map vector information, this paper proposes a calculation strategy for the heading angle of the 16-wind rose map to further improve the pedestrian positioning accuracy and reduce the accumulation error.Finally, combined with the differential barometric altimetry and step frequency detection method, the optimal height solution can be computed by the robust adaptive Kalman filter algorithm.The organization of this paper proceeds as follows: the proposed 3D indoor positioning method is described in detail in A proposed 3D indoor positioning method is used to estimate the three-dimensional coordinate of pedestrians, which uses the MEMS sensors  of a smartphone. As There are several parameterizations that can represent attitude and heading, such as Euler angle, Rodrigues parameter and quaternion, etc. Since quaternion has the advantages of less computation burden and global non-singularity, quaternion has been used widely. Generally, quaternion has four elements:b coordinate system to n coordinate system is used to calculate the heading and attitude angle, which can be described as  :(9)u=. In this paper, an M-estimator-based robust estimation of the equivalent weight matrix is paper . Then, tlated as :(26)r¯kiEstimate the cross-covariance matrix:The Kalman gain The state update kL is the step length; S is the personalized parameter that needs to be calibrated for each pedestrian.The speed estimation research mainly includes step frequency detection and step length estimation . Many stAt present, the step frequency detection algorithm based on MEMS uses the peak detection method to find the maximum acceleration of fixed time window according to the periodic change of pedestrian acceleration. The main purpose of step frequency detection is to identify the starting point of the stride from the continuous sensor data, so as to facilitate data processing in the unit of a single step when calculating the subsequent step length and direction. When performing pedestrian step frequency detection, it is best to use the total acceleration in three directions during pedestrian walking. The numerical fluctuation of the total acceleration can reflect the human walking law to a large extent. The total acceleration can be expressed as:With the rapid development of the economy and technology, the floors of many large buildings are getting higher and higher, so it is necessary to study the height measurement method of pedestrians in high-rise buildings. There are many height measurement methods, including laser altimeter, barometric, accelerometer and GPS . Since the GPS signal may not be received in a room and the laser is susceptible to complex environmental interference in a room, the two methods cannot be used for indoor height measurement. Due to the cumulative error caused by the integration of accelerometer data, an accelerometer cannot be used for indoor height measurement alone. The barometer is widely used because of its simple equipment and its accuracy can meet the requirements of certain indoor positioning. A mean filtering is applied to smooth the measured pressure of the barometer in this paper. The height value relative to the standard barometric pressure is calculated as follows: :(35)Hk=4H is the relative elevation from the initial elevation; k.In practical indoor positioning applications, the areas we need to locate are often small. The closure of indoor space often makes the atmospheric environment in the vertical direction of the positioning area basically equal, and there is a state of fluid static balance. Therefore, we can use the method of differential barometric altimetry to reduce the influence of the atmospheric environment on barometric height measurement and improve the accuracy of height measurement. In daily life, people usually calculate the height of the ground floor and describe the height of the floor. Therefore, to reduce the influence of the actual environment on the measurement and ensure the readability of the measurement results, the relative height of the indoor positioning point can be calculated by taking the first floor of the indoor building as the height calculation surface. The relative elevation of the measuring point and the reference point is obtained by using the differential barometric altimetry method. The differential barometric altimetry model is as follows:In addition, since the pedestrian trajectory is closely related to height value, pedestrian behavior patterns can be distinguished by detecting the changes of pitch angle, accelerometer and barometer of the smartphone in the duration of effective stride frequency. In the actual situation, the barometer values always increase or decrease when pedestrians go up and downstairs. However, barometer values are volatile after a long time of movement on the same floor. Fortunately, there is a trend of continuous barometer values increasing or decreasing. The barometer value showed a decreasing trend when upstairs, and an increasing trend when downstairs. Moreover, there are always mutations in the pitch angle, total accelerometer and barometer values of the smartphone when going up and downstairs, which can be used to distinguish the moving mode and improve the ability to distinguish the mode. Therefore, the scenario of pedestrians upstairs and downstairs can be judged by the pitch angle, total accelerometer and barometer values. In addition, there is a certain degree of swing and jitter in the walking process of pedestrian-held smartphones, which will bring a small range of shake to the pitch, accelerometer information and barometer value, resulting in interference. So considering the number of steps between the floors, three continuous effective steps are selected to identify the motion mode. If there are mutations in the pitch angle, total accelerometer and barometer values in the three consecutive valid strides, the mode of going upstairs and downstairs is determined.v is the behavior pattern marker; when walking in two-dimensional plane, v is ‘0’; when going upstairs and downstairs, v is ‘1’; u is the upstairs or downstairs marker, u is recorded as ‘1’, when upstairs and when downstairs, u is ‘−1’, The pedestrian motion state can be judged by combining the pitch angle, total accelerometer and barometer of the smartphone. In order to increase the accuracy of pedestrian motion mode judgment, this paper proposes a step frequency detection method to calculate the relative elevation information of pedestrians combined with the geometric information of the stair.Pedestrian height variations are derived from effective step-by-step and building stair information when upstairs and downstairs. So the height is calculated by combining step frequency detection method based on the geometric information of the building stairs with the differential barometric altimetry method. Two kinds of height measurements are fused by using robust adaptive Kalman filter (RAKF) fusion. The process and observation models can be described as:The robust adaptive KF algorithm details are presented as follows:kQ is the state model noise covariance matrix.The predicted state c0 is a constant which can be tuned depending on the practical implementation; tr(·) stands for the trace of a matrix, In the robust adaptive Kalman filter algorithm, the gain matrix turbance . The adaturbance :(42)∂k={The appropriate gain matrix kR. c is a constant, and it is usually within the range of . In the measurements, this paper uses an M-estimator-based robust estimation of the equivalent weight matrix is paper . Then, tlated as :(46)r¯kiComputing the corrected state: Updating the state error variance matrix:At present, the research on PDR has garnered a large amount of attention, but there is still a lack of comprehensive evaluation . PDR metIn addition, when a person walks normally, the difference in step length and heading between the previous step and the next step is very small. Therefore, the step length and heading of the previous step can be used as the prior estimation of the step length and heading of the next step. Some research about context detection has already been started, however, most lacks comprehensive consideration. Based on the prior estimation and the output results of the step size and heading estimation algorithm, this paper uses KF to reduce the estimation error. The state equation and measurement equation are established as follows:To improve the estimation accuracy of heading angle and reduce the error accumulation in the PDR method, this paper proposes a heading calculation strategy of a 16-wind rose map based on the vector information of the indoor map. The general wind rose map adopts eight or sixteen directions. This paper uses 16-wind rose map to optimize heading angle estimation. In this paper, the pedestrian position is mapped to the indoor map, and 16 possible equidistant directions are virtually designed. The starting direction is the north of the map. The center direction of the nearest interval is used as the heading. If the difference between the estimated heading angle and the nearest map direction is less than 5 degrees, the estimated heading angle is used as the pedestrian’s heading, which can limit the gradual increase of heading angle error.k is the current k effective step; L is the step length; v is a behavior pattern marker, marked as ‘0’ when the pedestrian is detected as two-dimensions motion and as ‘1’ when the pedestrian is detected as three-dimensional motion.The conventional PDR only estimates the position in 2D space and cannot locate the pedestrian’s position in 3D space . For thrTo verify the accuracy of the proposed approach, the experiments were conducted on the sixth and seventh floors in a research building. The floor plans are presented in The height results of the test are the relative height presented in This paper proposed a 3D indoor positioning method based on the built-in MEMS sensors of smartphones for pedestrian positioning. Although the proposed method can realize stable and accurate positioning results, there are still some problems to discuss.(1)Unlike most works, the proposed model is based on smart phone multi-sensor fusion to achieve indoor three-dimensional positioning. Considering filtering adaptability and robustness, a RACKF algorithm is proposed to estimate pedestrian heading. At the same time, the 16-wind rose map is introduced to improve the heading accuracy and weaken the PDR error accumulation. In addition, the accelerometer, pitch angle and barometer information are fused to identify the motion mode of pedestrians. Combined with differential barometric altimetry and step frequency detection method, this paper proposes a RAKF algorithm to estimate the relative elevation of pedestrians when upstairs and downstairs. However, the proposed method is only suitable for pedestrians holding the smart phone mode with their hands, to maintain the level. It is not suitable for phone calls, pockets and swing modes, which will be our future research work. The proposed method needs to know the height and width of the stairs in advance, which also limits the application to a certain extent. In addition, the proposed elevation estimation method is only applicable for pedestrians upstairs and downstairs, and is not suitable for elevator and escalator mode. Based on these constraints, our future work will focus on a more comprehensive positioning model. In addition, because the RACKF algorithm comprehensively considers the robustness and adaptability, the complexity of the algorithm is increased. Compared with the traditional PDR algorithm, the proposed algorithm increases the computation time. In elevation calculation, due to the integration of the results of the two algorithms, the complexity and calculation cost are increased compared with those of the traditional barometer.(2)Due to the indoor complex environment, there were multiple sources influencing pedestrian positioning. Although the proposed 3D indoor positioning method can weaken the influence of PDR positioning error accumulation to a certain extent, the PDR positioning error is still likely to accumulate, so it is necessary to further improve the performance of the algorithm to reduce the PDR positioning error.(3)In laboratory conditions, participants are required to walk normally, so the accuracy of pedestrian step frequency detection is high. However, in the real scenarios, the walking habits of pedestrians, the ground slope of the site and other factors, especially the conversion between walking and non-walking movements in the actual walking process, will affect the accuracy of pedestrian step frequency detection. In view of the pedestrian positioning requirements under complex walking conditions, a step frequency detection fusion algorithm with strong robustness is needed.(4)This article considers the static, uniform movement of the state of going upstairs and downstairs, but the pedestrian movement state is varied, such as in situ walking, fast walking, slow walking, turning and so on. In a specific time window, the statistical characteristics of sensor signals, such as mean, variance, and kurtosis can be used to form feature vectors to analyze and identify different motion modes. Moreover, this paper focuses on the hand-held mode which is the most common mode for pedestrian navigation. Other carrying modes include in-pocket, swinging-hand, etc. Some methods need to be used to identify and calculate the mode such as principal component analysis.This paper proposes a 3D indoor positioning method fused with the outputs of smartphone MEMS sensors for pedestrian positioning. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is used to estimate the heading of pedestrians. The RACKF algorithm reduces the weight of stale data and adaptively modifies the model noise parameters based on MARG sensors. The fading memory weighting method and the limited memory weighting method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. An adaptive factor is based on prediction residual construction to overcome the Kalman filter model error and the influence of abnormal disturbance. Additionally, a robust M (maximum likelihood-type) estimator) is used to identify and control measurement outliers. The step size estimation is achieved by using the accelerometer data in the smartphone. The heading and step length of each step are optimized by the Kalman filter to reduce positioning error. The pitch angle, total accelerometer and barometer values of the smartphone are used to distinguish the pedestrian behavior patterns in the duration of effective step frequency. When upstairs and downstairs, according to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained. Combined with the differential barometric altimetry and step frequency detection method, the optimal height solution can be computed by the robust adaptive Kalman filter algorithm. In addition, based on the indoor map vector information, a heading calculation strategy of the 16-wind rose map is used to further improve the pedestrian positioning accuracy. Based on the movement law of pedestrians, the proposed 3D indoor positioning method effectively reduces the influence of sensor cumulative error on position calculation, and improves the positioning accuracy.The experiments were conducted in an indoor environment conducted to verify the superiority of the proposed method. Height experimental results illustrate that the robust adaptive Kalman filter can improve height accuracy. Three-dimensional experimental results indicate that the proposed algorithm can provide more stable and accurate position estimation information. Therefore, the experimental results show that the proposed 3D indoor positioning method can provide an optimal model for pedestrian indoor location and navigation estimation. It is noticeable that, compared with other methods, the error of the proposed method is smaller and more stable. Therefore, it can be concluded that the proposed method can achieve better accuracy making it more suitable for indoor positioning with low-cost MEMS sensors of the smartphone.In the future, we will focus on enhancing filter performance to improve the accuracy of the position. Moreover, different carrying modes are the key points of our research. For pedestrian elevator mode, identifying the pedestrian movement state and the change of barometer will be explored.


The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space.Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE.https://cran.r-project.org/package=sivs.The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via Bioinformatics online. Due to more and more cost-efficient data generation and collection methods, we have seen a substantial rise in the data volume of database submissions during the past decade. To put it in perspective, only in the Gene Expression Omnibus (GEO) database alone, there has been about a 9-fold increase in the number of omics datasets submitted from 2009 to 2019 compared to its preceding decade (1999–2009). The complexity of biological data and the high-dimensionality of the datasets impose a challenge in the analysis and interpretation of these datasets . FurtherFeature selection is a crucial part of machine learning in which the features that are most informative in relation to the response value are selected, while irrelevant and redundant features are discarded , which eLASSO and Elastic Net both fall into the category of embedded feature selection approaches in which the feature selection is made as a part of the classification algorithm , and its implementation in R to effectively reduce the feature space to a small subset without decreasing the accuracy. This is achieved by considering multiple configurations of cross-validation sample binning, aggregation of the results for feature ranking, and ultimately shrinking the feature space.The general idea of SIVS is to encapsulate methods with embedded feature selection that are not robust in converging to the same feature space, thus resulting in inconsistent model performance. This is done via performing model construction multiple times and aggregating the resulting selected features. By repeatedly constructing models using different cross-validation sample binnings, we ensure that we have covered most, if not all, sample-binning compositions. The overall workflow of SIVS is summarized in The SIVS algorithm starts with preprocessing of the data, which includes removing redundant features and standardizing numeric values for the following steps. First, the numeric features with zero variance or categorical features containing a single class are removed from the data. Finally, all numeric features are standardized to have a mean of zero and a standard deviation of one. This is to make the models' coefficients comparable.At this point, we perform method-specific preprocessing if required by the internal method. For instance, glmnet prefers to have the input matrix as a ‘data.matrix’ object. Furthermore, glmnet is sensitive to missing values and, therefore, any sample with missing values is removed. Alternatively, imputation could be applied before applying SIVS by the user to retain more samples in the analysis.At the second step, a predefined number datasets .Based on the set of models built during the iterative step, VIMP score is calculated for each feature. The main idea is to assign a higher score to features that are selected by the majority of the models and are contributing the most in the model to predict the response value. Let us denote the features by For the last step, all features with nonzero VIMP scores based on In the implementation of the SIVS R package, the output of all the aforementioned steps is returned as an S3 R object. There are also helper functions to assist in the interpretation and plotting of the results.To assist in choosing an appropriate cutoff for the features on the basis of their decreasing VIMP scores, we have implemented a method using the RFE output in the R package . This meTo evaluate the performance of SIVS for feature selection, we applied it on three different types of biomedical data in a binary classification setup using glmnet with 10-fold cross-validation and binomial family as the internal method. The goodness of the resulting features was assessed by comparing the models built using features suggested by SIVS, with corresponding models built without SIVS. For each of the training data, 100 different logistic regression models were built using the binomial family of glmnet with different cross-validation random seeds to assess the consistency of the resulting performance in the independent validation data. All used datasets are summarized in As a performance metric, we used AUROC. To test whether the observed difference in AUROC was significant, we performed a pair-wise AUROC comparison between each of the ROC curves of (1) the standard glmnet model and (2) the glmnet model that was built using SIVS suggested features when the same cross-validation seed was used. The pair-wise statistical comparison was performed using the DeLong method  database, namely Lung Adenocarcinoma (TCGA-LUAD) and Lung Squamous Cell Carcinoma (TCGA-LUSC). Both datasets were downloaded in the Fragments Per Kilobase of transcript per Million mapped reads upper quartile (FPKM-UQ) normalized  as the training set.The TCGA-LUAD and TCGA-LUSC contained 594 and 551 samples, respectively. Considering that these two datasets contained samples from different subtypes of Lung cancer, we used their combination and built a model to differentiate the two subtypes. To form the training and validation sets, we randomly selected 100 samples from each subtype to create the validation set and used the rest of the samples (https://biolincc.nhlbi.nih.gov/). Here, we used similar variable preprocessing as described before  and the Action to Control Cardiovascular Risk in Diabetes Blood Pressure (ACCORD-BP) trial, both of which compared two antihypertensive treatment strategies and their effects on cardiovascular outcomes  without significantly affecting the AUROC values of the final models   in the 100 different runs on the full feature space, whereas SIVS built models with median AUROC of 0.61 by constantly using 41 features . On lungctively) . By lookctively)  which inctively) . AdditioWhile SIVS produced a smaller feature space than the standard glmnet, the overlap of the selected features by SIVS across the different runs was markedly higher . In addiComparisons with Boruta and RFE revealed that SIVS performed consistently as well as them or even slightly outperformed them in terms of the number of selected variables . Most imThis study introduces SIVS, a novel feature selection method that can effectively reduce the feature space, especially in high-dimensional data, and provides insight into each feature's impact with regards to the response value. SIVS starts from aggregating the results of multiple multivariable modeling runs using different cross-validation random seeds. As a result, it provides feature importance scores for the features and consequently orders them accordingly. This score is then utilized in an RFE step to inspect the effect of each feature's removal on the stability and predictive power of the resulting model, which is ultimately used in narrowing down the list of important features to a much smaller subset.To assess the performance of SIVS and the goodness of the selected features, 100 models built using SIVS’ features were compared with 100 models built using plain glmnet. These models were compared based on their predictive power on a separate test set, considering the number of features they have and the variability of these features among the 100 models. This procedure was applied to three different datasets. The presented results demonstrate the effectiveness of SIVS as a feature selection method on various high- and low-dimensional biomedical data, where SIVS reduced the feature space down to 38% of the features that LASSO can typically select, without having any significant drawback in the predictive power of the model over independent test sets. Moreover, the features selected by SIVS were markedly more stable over multiple runs than those selected by standard glmnet.Feature selection methods can be divided into three categories: filter methods, wrapper methods, and embedded methods , but theAt the time of writing this article, there are 51 packages dependent on glmnet, out of which seven address feature selection [BioMark , elasso SIVS is shipped with a method to suggest an appropriate cutoff for the exclusion of features with lower importance. The strictness of this suggestion method can be tuned (default = 0.01), and it is important to note that there is no one size fits all solution. The strictness threshold is subjective, and we encourage users to choose the threshold based on the RFE plot. To stay fair in this study, we consistently used the default parameters without modifications or tuning, but this is not to undermine the fact that the SIVS should not be treated as a blackbox feature selection method, and the parameters should be tuned according to the specification of data and the question in hand.A major strength of this study is that we have focused on real-world datasets instead of synthetic data to demonstrate the practical utility of the method. Moreover, we have used independent validation datasets to show how much the selected features generalize to other datasets. Lastly, we provide a ready-to-use implementation of the method. The method is implemented in R language and in compliance with the Comprehensive R Archive Network (CRAN) standards and regulation and is published on CRAN. Therefore, SIVS can be freely accessed, installed, and tested. Additionally, the SIVS source code is published under General Public License v3.0 (GPL3) and is publicly available on Github.k = 100 by default). Additionally, considering that SIVS is wrapping the internal method, consequently it will inherit the limitation of that method as well . For example, due to L1 regularization, LASSO is known to have issues with colinear features which was not explored here. However, the aim of this study was to show the feasibility of the proposed method with a working implementation. For further optimization in the future, various possible alternatives could be considered. For example, the effect of replacing LASSO with a smooth function or exploring the effect of colinear features on SIVS performance as well as implementing alternative performance metrics and testing other prediction scenarios, such as multilabel classification, will be left as next steps for future research.A drawback of SIVS is that despite the multithreaded implementation of the method, it is relatively slow to compute due to multiple iterations  curve calculation in pROC package. This limitation could also be loosened by the addition of other performance metrics into SIVS. In addition to glmnet, SIVS also naturally extends to other forms of modeling with embedded feature selection or shrinkage methods, such as random forest. Implementing these additional internal methods and other performance metrics will be done in the next versions of the SIVS R package.This study shows how a single run of glmnet is not an optimal solution for finding the best feature space in terms of consistency in performance and the number of incorporated features in the final model. SIVS, the method presented in this article, is a feature selection method that can drastically reduce the feature space without substantially sacrificing the performance and produces consistent results across multiple runs. This indicates that the ‘true signal’ is more effectively captured by SIVS compared to the standard glmnet.All the scripts for data preprocessing and analysis are available upon request. The SIVS can be directly installed from CRAN, and the source code can be accessed through the following webpage:https://cran.r-project.org/package=sivsM.M. participated in the study design, conducted the analyses, developed the method, wrote the manuscript and developed the SIVS R package. M.S.V. participated in method development, participated in study design, preprocessed the clinical data, participated in the analyses and participated in writing the manuscript. R.K. participated in the development of the method, participated in study design and edited the manuscript. L.L.E. supervised the study, participated in the study design and participated in writing the manuscript.btab501_Supplementary_DataClick here for additional data file.


Translucency is an optical and a perceptual phenomenon that characterizes subsurface light transport through objects and materials. Translucency as an optical property of a material relates to the radiative transfer inside and through this medium, and translucency as a perceptual phenomenon describes the visual sensation experienced by humans when observing a given material under given conditions. The knowledge about the visual mechanisms of the translucency perception remains limited. Accurate prediction of the appearance of the translucent objects can have a significant commercial impact in the fields such as three-dimensional printing. However, little is known how the optical properties of a material relate to a perception evoked in humans. This article overviews the knowledge status about the visual perception of translucency and highlights the applications of the translucency perception research. Furthermore, this review summarizes current knowledge gaps, fundamental challenges and existing ambiguities with a goal to facilitate translucency perception research in the future. How different objects and materials appear to human observers is important not only in commerce, where customer choice and satisfaction are often influenced by the visual look of the product, but also in trivial daily tasks performed by humans. For instance, we use the visual appearance information to judge whether materials are fragile or elastic, whether food is spoiled or edible. By their appearance, we can effortlessly identify materials within seconds . Accordisubsurface light transport. Translucency is impacted by multiple intrinsic and extrinsic factors. The intrinsic factors are the physical parameters found in the radiative transfer equation (The etymology of the term is related to the Latin words “trans” (through) and “lux” (light) — implying light penetration inside the body of the material . A transequation , such asequation . How thiequation , object equation , and theequation . The humtranslucency, not transparency — a better understood concept and visual attribute. While the two concepts are sometimes used interchangeably Summarize the state-of-the-art about the perception of translucency and the review of the recent developments in the field.(2)Discuss the different applications that could benefit from the translucency perception research and overview of the importance of the topic in and across different disciplines.(3)Outline the major knowledge gaps and research challenges in order to facilitate future work.The contribution of this article is three-fold:The article is organized as follows. We briefly summarize the motivation for translucency perception research in the next section. In four subsequent sections, we review the state of the art and demonstrate the findings on the example of real and synthetic stimuli. First, we provide a historical discourse on how the knowledge status has developed over time. Second, we overview the role of transparency in translucency perception. Third, we discuss which factors impact perceived translucency. Fourth, the potential cues for translucency perception are analyzed. Afterward, we discuss the current challenges in the translucency perception research and outline the most important questions remaining open, which is followed by a concluding section.Translucency plays a significant role in a multitude of fields and applications. Thus, it is a research interest in different disciplines. In this section, we provide an overview of the applications and the interdisciplinarity of the problem. Afterward, we discuss the gap between the optical and the perceptual properties of a material — motivating the research from the human vision point of view.To highlight the importance of understanding underlying visual mechanisms of translucency perception, we summarize the major applications where the translucency perception research can make impact.food industry, not only impacting customer satisfaction  printing. Three-dimensional printing technologies have reached a level of development where translucency has become an important visual attribute, increasingly attracting an attention in the 3D printing community. The recent advances in multimaterial 3D printing enable generation and reproduction of material translucence by mixing transparent and colored opaque printing materials, which expands the appearance gamut of the 3D printing hardware  and their physical correlates are not identified. Even though the techniques of material property acquisition have advanced and the photorealism of the computer-generated imagery is impressive, the link between the measured physical properties of the materials and their visual appearance is far from being fully understood. Photosensitive measurement instruments might not be able to capture the appearance perceived by the HVS and cannot provide a quantitative correlate of visual sensation . The Kubelka-Munk theory was one of the most widespread as well as simplest among those . The internal scattering gradually makes a perfectly transparent medium more translucent and eventually opaque . The boumax − Imin)/(Imax + Imin), where Imax and Imin are the maximum and minimum luminances, respectively, Internal scattering affects the clarity of the background image. Blur of the see-through image produces a translucent look refer to . It has additive color mixture . This means that, when the background is visible, translucency can be observed even without any internal scattering. This can be ascribed to the decreased contrast and the color shift in the see-through image . If the Translucency as a visual attribute is impacted by different intrinsic and extrinsic factors. We provide an overview of the knowledge status on them.s) and absorption (a) coefficients signify the scattering and absorption events per unit distance traveled by a photon, respectively. The sum of the absorption and scattering coefficients is called extinction or attenuation coefficient (t). The extinction coefficient t is given as a sum of the scattering and absorption coefficients . The t for perfectly transparent material is equal to zero. A high a means that fewer photons escape the material and the object gets a darker shade; per contra, a high s is responsible for blurry and shiny appearance. It is worth mentioning that in addition to volume scattering (scattering inside the medium), a scattering event can also take place at the surface (as discussed elsewhere in this article). Wavelength-dependent absorption and scattering coefficients are fundamental parameters that describe the radiative transfer through a medium. Scattering , whereas in the remaining range human observers were sensitive to colorant ratios, as the transmittance and the perceptual correlate were well-explained with the Stevens’ power law , but also emission . To the Although the likelihood and the number of scattering events are essential, the direction a scattered photon is redirected to can also be important. If multiple scattering is assumed , where dg to define the directionality of the scattering. In the front-lit illumination geometry (top row), backward scattering resulted in brighter and more diffuse look, as the photons were scattered back toward the camera. On the contrary, forward scattering redirects photons away from the camera, resulting in dark opaque-looking appearance . In contrast, the impact is negligible for the back-lit illumination condition (bottom row), because strong directional backlight results in high direct transmission and the magnitude of scattering change has weak impact on the resulting appearance. The index of refraction is one of the most understudied intrinsic material properties in the context of translucency perception. At the boundary of the media, the difference between their refractive indices defines the angle the light ray is refracted with. Therefore, the refractive index has a strong impact on the background distortion in see-through images , which impacts the amount of radiant energy emerging from the material — thus, also translucency cues. The extreme case is the total internal reflection — when the light traveling from a medium with a higher refractive index is fully reflected backward — thus, no refraction happens and no light emerges from that medium to the medium with lower refractive index. The total internal reflection takes place when the angle of incidence is larger than the critical angle. Therefore, it is more likely to happen on complex surface geometries, rather than smoother ones. This could be one of the reasons for the appearance difference between the smooth and the complex Lucy shapes in polarization of the incident light can also play a role in the Fresnel reflection and transmission. Finally, Alpha — a psychophysics-based perceptually uniform translucency metric. However, the authors highlight that the metric should be scaled with the object size and provide a proper implementation of this. How object scale impacts appearance for a fixed optical material properties is illustrated in If the object is enlarged, the distance a photon needs to travel increases. This means that, for a given extinction coefficient, the number of absorption and scattering events goes up and fewer photons escape the material unscattered. The opposite is true, if the object is smaller. Therefore, object scale has an impact on the translucent appearance . This haDepending on the structural thickness, the translucency appearance of a given object made of a homogeneous material can vary considerably. Refer to the Micro- and macro-scale surface geometry, although both scatter light, have qualitatively different effects on appearance. The microfacet-level surface roughness impacts refraction , blurs tAccording to the literature, translucency can impact perceived macro-scale surface geometry of the object — translucent objects appearing less sharp . InteresFinally, a complex surface geometry might generate more specular highlights, caustics and interreflections — making more difficult to see-through and yielding illusion of subsurface scattering . Think o“transillumination” of the light through the material is a strong cue for translucency. Most of the materials look more translucent when the light source and the observer are located in different hemispheres, that is, when a sample is back-lit from the observer's viewpoint. This effect was first illustrated by Fleming and Bülthoff and has been further substantiated experimentally by p value Illumination direction has one of the most striking effects on the magnitude of perceived translucency. If you have ever taken your food and looked through it toward the sunlight, you should have noticed that it starts glaring see . This efback-lit  — havingback-lit  noted thback-lit , the parback-lit  observedThe impact of illumination structure on the perception of translucency is not well-explored. Although Although all previous research e.g.  attempteIt has been shown that appearance perception is not a one-way pipeline, but rather a loop where the low-level vision is not simply an input for mid- and high-level vision, but also gets impacted by them . ChadwicThe high-level cognitive information seemingly plays a role in the perception of painterly translucency. It has been shown that depiction and perception of translucency is related with the perceived realism and “convincingness” of the artworks . WijntjeA fundamental problem in translucency perception is separating reflected and transmitted energy in the proximal stimulus on the retina. In this process, the HVS can obviously benefit from understanding the distal stimulus — the scene and the ambience. Motion has been demonstrated to be important for gloss perception and gloss constancy , especiaThe impact of motion is demonstrated in the video available in To the best of our knowledge, When talking about color, it is crucial not to mix up the chromatic and achromatic components. Lightness or brightness are directly correlated with the absorption and scattering, which make materials look darker or brighter, respectively  see Fig. As manyIn contrast, little is known about how chromaticity contributes to translucency. It has been shown that translucency impacts apparent gloss , 2021a. bust renderings from the Plastique Artwork Collection , highly absorbing somewhat specular black opaque material (“black opaque”) and a Lambertian-looking white diffuse opaque material (“white diffuse”) in back-lit and front-lit illumination conditions. These images are shown in These intrinsic and extrinsic factors are impacting the proximal stimulus in a way that the HVS can deduce subsurface scattering and light transmission in the images. Although the scene dynamics and the temporal aspects enhance translucency detection, it is possible to perceive translucency from still images, which makes the researchers conclude that there should be some diagnostic features and statistics in the 2D images, which separate translucent media from the opaque ones. For example, it has been proposed that the skewness of the luminance histogram might be correlated with perceived gloss  but see. There hllection , which illection . Using tllection , 2021b, llection . In the They also observed that the contrast between the specular and nonspecular regions is smaller for translucent objects and on the example of a simple torus image, they demonstrated that the histogram of an opaque object is more skewed  are predictors of translucency. In order to test this observation, we have rotated a bust figure with 180° from back- to front-lit condition and visualized the summary statistics of a luminance histogram as a function of the rotation angle refer to . SimilarBecause neither histogram nor spatial information alone are enough for predicting translucency, we tried whether simple histogram matching between front-lit and back-lit conditions of the same translucent material could affect their appearance. Histogram matching affects the magnitude of intensities, but is also to some extent “spatially aware.” The resulting images are shown in Furthermore, Furthermore, Motoyoshi also noted similar to One particular instance of thin regions, the edges, generally have been observed to be informative about material translucence . TherefoThe authors argued that the edge radiance profiles are robust to the real-world artifacts and can be reliable indicators for edge detection and material identification algorithms in computer vision systems. While they seem a robust indicator for machines, it remains unknown whether the HVS relies on similar edge profiles for translucency perception. Psychophysical experiments need to be conducted in the future to explore this question. It is interesting to observe whether image manipulations and mapping textures of different radiance profiles near the edges of different surfaces affect observers' estimations of the subsurface scattering properties. Additionally, proper eye-tracking measurements could also reveal the saliency of the edge profile components when subjects are performing translucency-related visual tasks.First, the authors demonstrate that the intensity gradient produced by the subsurface scattering is affected by the 3D shape. In opaque materials the location of the luminance extrema depends on the surface orientation in space, as the luminance extrema are located on the sides of the convex and concave regions whichever side faces the illumination is brightest and whichever faces away from it is in the shadow. Contrastingly, in translucent materials, the intensity gradient is related with the local surface curvature; the decrease in the extinction coefficient usually smoothens the gradient, decreases the luminance contrast (which is consistent with other works ) and movInformation about 3D shape can facilitate the estimation of the material properties and vice versa. However, in real-life scenario, neither is hardly ever known to the observer. The HVS somehow manages to recover both 3D shape and properties of a material that according to This work opens a new avenue for translucency perception research. Although previous work concluded that information on 3D shape is important for translucency , this wotranslucency-related regions, similarly to (1)It seems that neither luminance nor spatial information alone is enough for estimating perceived translucency. The HVS seemingly uses some sophisticated combination of the both.(2)The spatial regions where a photon can go through easily look brighter and contain rich information about material translucence. Examples of this kind of regions are edges, thin parts and sharp fine details of a surface geometry.(3)The regions which are usually shadowed in opaque objects are also informative about translucency, as they look brighter in translucent materials.(4)Points 2 and 3 can be generalized as follows: if, in the absence of subsurface light transport, a considerably smaller amount of light could have reached a particular region, this region can be diagnostic for material translucence.(5)Understanding how much light could or could not have reached a particular region inherently involves understanding the surface geometry and global correlation among different spatial regions.(6)It is not known how the HVS segments an image, how it identifies the informative regions and how it calculates the surface geometry. These calculations are not unique and vary across individuals. There can be multiple translucency cues in a proximal stimulus and different people can rely on different ones for yet unknown reasons.A full perceptual model of translucency which could simply take scene and material properties as an input and provide an estimation of a perceptual correlate remains beyond reach nowadays. The possibility of coming up with this kind of model anytime soon ranges from unlikely to impossible. Nevertheless, some partial models have collected interesting observations about translucency perception cues. These works complement each other and can be summarized as follows.We believe that, in addition to standard psychophysics, where experimenters attempt to find a correlation between the varying physical parameters and the observer responses, it is also important to study translucency perception process from a behavioral perspective. The first step toward this has been done by translucency might or might not differ from that of transparency. For example, transparent and translucent in the Japanese language, which might have impacted his experimental results. However, he reports that observers assess translucent and transparent stimuli differently from each other, seemingly understanding the semantic difference between the two visual phenomena. This makes the author propose that the two concepts might be orthogonal. Scaling translucency remains a challenging and confusing task. To the best of our knowledge, more translucent in their experiments as having stronger subsurface scattering. strength and milkiness of the tea images. However, the association between the strength, milkiness, and translucency is not clear either. extent of visibility scale of “more translucent” and “less translucent.” However, the scale is intended for assessing the appearance of the mungbean noodles in a plastic cup and for quantifying the visibility of the objects behind the noodle strands — thus, it is not readily applicable to the solid non-see-through materials. Furthermore, the inconstancy of translucency across different shapes makes it challenging to clearly separate translucency as a property of a given object and as a property of a material the object is made of. We observed (The exact meaning of translucency is not universally accepted and remains subjective . This baobserved , 2021b tobserved ; second,observed , a heterobserved  pointed One of the pivotal limitations of the experimental methods are the constraints related to the visual stimuli selection. Real objects, photographs, or computer-generated imagery can be used to study translucency perception psychophysically. All of these methods come with their advantages and drawbacks, which are summarized in Appendix 1 of A further aspect which is problematic from the experimental point of view is the lack of standardization. Normal conditions for observing translucency and a standard observer are not defined. For instance, the contrast sensitivity and the visual acuity might have a significant impact on the experimental results. However, the viewing conditions, such as the distance and the size of the visual field varies across different experiments which complicates the comparative analyses of their findings.#TheDress anytime soon, which could expose these individual differences?And last but not least, unlike color vision , the kno•Which image cues and regions does the HVS rely on and how does it identify, calculate and weight them?•What is the role of shape and geometry perception and how does the HVS calculate them?•To what extent is perceived translucency impacted by other appearance attributes, such as color, gloss, texture and fluorescence?•What role do the identification of the familiar materials and other psychological priors play in translucency perception?•How does the HVS use motion and scene dynamics to assess translucency?•What is the physiology of translucency perception from the retinal to the cortical level and how much does it vary across individuals?The exact mechanisms of translucency perception remain largely unidentified. After compilation of the state-of-the-art works, we came up with the several important questions which we believe should be addressed in future works.We envision that the future work can develop in three directions: the eye-tracking experiments can facilitate identification of the respective cues and key image regions; behavioral analysis (similar to Eye-tracking can potentially reveal the most salient cues to translucency and whether different observers rely on different cues, as noted by We have discussed translucency as one of the pivotal appearance attributes, which is increasingly important in a broad range industries and disciplines, including 3D printing, cosmetics, the food industry, and the arts, among many. Translucency results from the subsurface transport of light. Although the techniques for measuring and modeling the optical properties of a material are relatively well-established, our understanding how they link to their perceptual correlates remains limited. The advance in translucency perception research is attributed to the development of computer graphics techniques which permit easier generation of the translucent visual stimuli. Although the initial studies were limited to transparency perception, transparency models could not explain the perception of highly scattering media. The visual cues and perceptual mechanisms seem to be fundamentally different between the transparency and translucency of the see-through filters and translucency of highly scattering, non-see-through media. This resulted in emergence of a separate research topic, namely, the perception of translucency in highly scattering media. In the past 20 years, multiple factors have been identified to be contributing to perceived translucency, such as the illumination direction, structural thickness of the object, as well as subsurface scattering properties. It is believed that the luminance distribution around the edges and in the shadowed regions, and its covariance with the surface geometry, might be used by the HVS to infer translucency in highly scattering, non-transparent materials, while the HVS relies on apparent contrast and blur when the background is visible. Nevertheless, overall translucency perception research is still in its infancy. We argue that the problems with the conceptual understanding and comprehension of the term impede the advance of the research and complicate the reproducibility of the tasks. We argue for the better standardization in this domain. Finally, we believe that eye tracking experiments could reveal which image regions and cues are significant, and advance in neuroscience could provide a deeper insight in the corresponding anatomical mechanisms for translucency perception.


In the present study, the fatigue behavior and tensile strength of A6061-T4 aluminum alloy, joined by friction stir spot welding (FSSW), are numerically investigated. The 3D finite element model (FEM) is used to analyze the FSSW joint by means of Abaqus software. The tensile strength is determined for FSSW joints with both a probe hole and a refilled probe hole. In order to calculate the fatigue life of FSSW joints, the hysteresis loop is first determined, and then the plastic strain amplitude is calculated. Finally, by using the Coffin-Manson equation, fatigue life is predicted. The results were verified against available experimental data from other literature, and a good agreement was observed between the FEM results and experimental data. The results showed that the joint’s tensile strength without a probe hole (refilled hole) is higher than the joint with a probe hole. Therefore, re-filling the probe hole is an effective method for structures jointed by FSSW subjected to a static load. The fatigue strength of the joint with a re-filled probe hole was nearly the same as the structure with a probe hole at low applied loads. Additionally, at a high applied load, the fatigue strength of joints with a refilled probe hole was slightly lower than the joint with a probe hole. Lightweight metals such as aluminum alloys are widely used in the automobile and aerospace industries. Assembling such metal structures is a challenging process in the industry. Resistance spot welding (RSW), laser spot welding, and riveting are widely used to assemble aluminum alloy panels. However, the noted conventional methods have several disadvantages, including high structural weight and weak joint strength. Therefore, a new method is required for joining aluminum panels in order to overcome these shortcomings. Friction stir welding (FSW) is a revolutionary joining method developed by TWI  in 1991. This method has various advantages, such as small thermal deformation, high-quality welds with superior mechanical properties, fine and uniform weld microstructures, and high welding efficiency [Friction stir spot welding (FSSW) is a newly generated solid-state joining process . In thisStructures joined using FSSW are subjected to different loading conditions during their life service. Therefore, it is vital to elucidate the tensile and fatigue strengths of these structures. Several studies have ,7,8,9,10One of the disadvantages of FSSW joints is the development of holes on the surface at the central part of the weld nugget due to the FSSW process. The presence of the holes causes several problems, such as corrosion vulnerability, in the structure. In fact, water could remain in the holes for a long time, and corrosion occurs in such areas . To addrThe finite element method (FEM) is a cost-effective method to study FSSW joints. By performing the FEM, the effect of different FSSW process parameters on the tensile strength and fatigue life could be investigated. In this study, the fatigue behavior of Al-Mg-Si aluminum alloy  joints, joined by friction stir spot welding (FSSW), is investigated numerically. Effect of re-filing the probe hole on the tensile strength and fatigue life is examined and compared to the experimental results of Uematsu et al. . It is wThe sheets used in this study were made of A6061-T4 aluminum alloy  with a 2 on top of each other with a 30 × 30 mm2 overlap area. The hole geometry is demonstrated in The aluminum plates, joined by friction stir spot welding (FSSW) subjected to static and cyclic load, were analyzed using Abaqus software . The finite element model included three parts: two aluminum sheets and the weld zone .The contact between the sheets was assumed to be a perfect bond. Tie constraint types were used to define the perfect bond between the parts (near the weld zone). The boundary conditions used for static and fatigue simulations are listed in The strain-based approach is being widely used to predict fatigue life of different materials. In the present study, stress-life fatigue curves for high-cycle fatigue, as well as stress-strain curves hysteresis loop, are plotted. Total strain amplitude is divided into elastic and plastic strain components based on data from the steady-state hysteresis loops . In thisd Manson  found thIn the present study, to calculate the strain, homogenized variables (strain) are determined. In this regard, the strain is calculated by taking volume average of the strain from all elements in the cylindrical-shaped volume around the probe hole. This volume is determined to gain more accurate results as the area near the probe hole has a significant effect on both tensile and fatigue failure of the structure.This specified volume has a diameter equal to the outer clamping ring diameter (10 mm); the height equals the overlap thickness (4 mm), where the hole (or refilled probe hole) is located at the center of the cylindrical-shaped volume. The total strain is calculated as:mε is the local strain in each element, and mV is the total volume of the specified cylindrical-shape section. After calculating the volume averaged total strain by Equation (3), the steady-state hysteresis loop is plotted for different applied loads, and then the plastic strain amplitude is determined. By employing Equation (2), the number of cycles to failure is determined for different applied loads and joints with a probe hole as well as with refilled probe holes.In Equation (3), After calculating fatigue life, the results are compared with the experimental data available in the literature . In thisThe tensile strength of the joints with probe hole and re-filled probe holes obtained from the current FEM model are reported in In order to predict the fatigue life of FSSW joints, first, the hysteresis loop is determined to calculate the plastic strain amplitude. Afterward, the fatigue life is predicted employing the Coffin-Manson Equation, and then it is compared to experimental data . Figure Available experimental data  is reportal data  have thetal data . This aptal data  may be dtal data  have theThe tensile strength of the FSSW joint is improved by re-filling the probe hole because of the effective cross-sectional area. In fact, the fracture path is increased, and therefore stress in the fracture path decreases.Fatigue strengths of joints with both probe hole and re-filling probe hole are almost the same at low applied load levels.At high applied load levels, the joint with a probe hole has higher fatigue strength than the joint with a re-filled probe hole.Using a strain-based approach, by calculating the strain from volume averaging of the strains of the elements near the welding zone , is a reliable approach to determine the fatigue life.In the present study, the static tensile strength and fatigue life were computed by performing 3D FEM based on a strain-based approach. The results were validated against the available experimental data in the literature. The results showed that:In this paper, a model based on FEM analysis and strain-based model was proposed to study the FSSW joints and the effect of re-filling probe holes on the fatigue life improvement of the FSSW joints. The results were in good agreement with available experimental data. Therefore, the proposed model is a reliable model and it could be used in future studies or even in the industry to examine the effect of other parameters  on tensile strength and fatigue life of FSSW joints.