Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance. In traditional active tracking schemes, the target has to hold cooperative devices for being tracked, such as RFID tags or wireless transceivers, to name a few. However, such tracking schemes have a limitation in specific scenarios; for example, criminals and intruders try their best to avoid carrying any devices for the system to track them, or the device of an uninvited guest may be unable to provide necessary information for the tracking work; moreover, the elderly and children are incapable of effectively operating their wireless device. Targets in such scenarios are called device-free targets. Tracking device-free targets is termed passive tracking or device-free localization (DFL) ,2.In wireless sensor networks (WSNs), the received signal strength (RSS) between pairs of wireless nodes is easy to measure when data packages are being delivered between them. The RSS is generally the most practical information for localization works, thanks to its merits of ubiquity and ease of accessibility . GeneralThe performed experiment in reference  is underHowever, a common drawback of references ,6 is thaIn this paper, we will pay attention to the energy consumption of the passive tracking system, which was partly introduced in reference . We buil(1)Using the foundation of the passive tracking schemes based on geometric constraint methodology, we demonstrate the feasibility of identifying RFT phenomenon by utilizing the spatial diversity receiving technique;(2)To extend the application of the passive tracking schemes based on the geometric constraint method, a method for multiple device-free targets tracking is presented;(3)We first build a quantifiable power consumption model for RSS-based passive tracking WSNs, and an adaptive networking scheme is proposed with the mechanism of the tracking performance adjustment, as well as energy conservation.In general, the contribution of this paper can be summarized as follows:The rest of the paper is organized as follows. In following i-th-triggered LOSL is detected; the intersection of straight lines i-th-triggered LOSL is detected.The clarification is made as follows: Recently, the passive tracking scheme adopting the RFT has been addressed as an emerging issue. Among existing passive tracking schemes, the solution based on the radio tomography imaging (RTI) is a popular trend ,11,12,13Besides the global RSS imaging, another trend is applying RSS measurement to the individual LOSLs. However, it is common knowledge that RSS measurement is extremely fragile due to the wireless environment; hence, in reference , TalampsSome researchers pay attention to outlier LOSL rejection using probabilistic approaches, rather than improving the RFT detection of the LOSLs. An example is presented in , in whicHowever, above schemes all rely on high-density wireless nodes to guarantee tracking accuracy and neglected the power consumption issue. Here, in this paper, we specifically focus on the CP estimation of individual LOSLs and apply the methodology of geometric constraints to track device-free targets by using the information of the WSN topology.(1)The first method is by using the diversity reception techniques. For example, the frequency diversity is proven to be efficient for detecting the appearance of the device-free target in reference , and spa(2)The second way is to increase the operating radio signal frequency is to weaken the diffraction ability of signal propagation, such as the emerging Millimeter Wave technology , or even(3)Moreover, the propagation time can also be adopted for detecting the RFT on the LOSL, since the time delay will be increased with the multi-path signal caused by the shadowing of the obstacle, which of course has a harsh requirement on the synchronization accuracy.Before applying the geometric constraint, the first issue that should be addressed is whether the LOSL can perfectly sense the RFT phenomenon and get “triggered” once a target is passing through it, considering the RSS is so fickle to be predicted due to the multipath effect. Actually, several methods may help here:Here, we conduct a practical experiment to verify the effectiveness of the spatial diversity technique on RFT detection.To verify the effectiveness of the diversity reception techniques for RFT detection, 4 XBEE S2C Modules working in 2.4 GHz are adopted, among which one is set as the transmitter, and the receiver consists of three modules. We performed the experiment at an indoor corridor at a general office building of our university under multipath environments, and the experiment sketch is shown as In general, the RFT means the RSS fluctuation of the LOSL caused by the passing-by target, and it is shown up as the increase of the RSS variances during the specific time-period. Thus, we synchronously calculate the RSS variances of the three modules RSSI values in The RFT experimental result shown in However, it is currently difficult to guarantee that the RFT phenomenon on the LOSL can be detected by 100 percent under all kinds of radio frequency environments. Several constraints should be noticed for feasible implementation.The first issue is the operating frequency of the WSN. Actually, the LOSL is the approximation of the first Fresnel zone, which is an elongated ellipsoid, and the obstacle in it can easily affect the RSS . AccordiThe speed of the traversing target is an empirical factor affecting the RFT detection. Assuming that the target is moving at faster speed and the RSS sampling rate is not high enough, we will fail to recognize the RFT detection. As shown in Actually, it is feasible to further guarantee the RFT detection by combining the spatial diversity technique with frequency diversity and the utilization of higher frequency waves; hence, in this paper, we prefer to focus on our passive tracking algorithm, assuming that the RFT phenomenon on all the LOSLs can be perfectly detected once the target passes through.The geometric constraint method for passive tracking work utilizing RFT phenomenon is briefly presented as follows: Once the wireless nodes are deployed in the WSN area with known and fixed coordinates, the area is partitioned by the LOSLs into regions whose boundaries are the segments on LOSLs . When a target moves in the area of interest and traverses the LOSLs, the triggering sequence of the LOSLs is recorded by the system. With the known networking topology information and the triggering sequence, the tracking work based on geometric constraint method is performed to decide in which segment the target is located; then, the middle point of the segment with highest probability is estimated as the CP to minimize the mean-square-error of CP estimation. Finally, the CPs reveal the target’s trajectory. A centralized entity is put to receive the LOSL triggering information from the wireless nodes, and it takes charge of the LOSL triggering sequence storing, analyzing the sequence to determine the CPs; distinguish the behaviors of device-free targets and the requirements of tracking accuracy; and, accordingly, adjust the density of active nodes in the WSN.A, B, C, D, and E), which form a LOSL web, monitoring a corridor. The wireless nodes are fixed with known coordinates as A typical trajectory identification scenario is shown in the When the first LOSL, The second triggered LOSL is recorded as The third triggered Algorithm 1. The CP Determination for Tracking a Single TargetInitialization: Get information of the WSN topology, including the node positions, LOSL formulas Step 1: get the first CP as the middle point of the edge LOSL  Step 2: Once   Select the highest probability segment   Estimate CP as the middle point of  Step 3: Go back to Step 2, until the target walks out of the monitoring area.Generally, the framework of proposed CP estimation for the single target can be briefly summarized as Algorithm 1:If two or more targets are walking in the web, the problem becomes multiple targets tracking problem. Here, we present the divide-and-conquer approach to identify the trajectories of two device-free targets, although the maximal number of targets that can be simultaneously tracked is more than two and can be further extended by increasing the density of wireless nodes.Two cases for tracking two targets are presented as an example in Explanation 1: Explanation 2: Explanation 3: Explanation 4: When the WSN system yields data that So, we move on to the next step and collect further data from the system on the next triggers in LOSLs. From the following measurements, we can see that From the prior topology knowledge shown in So, we can determine that the triggers on As discussed above, when the targets are located in separate areas in the monitoring area and they trigger the same LOSL, it is possible to calculate the trajectories by considering the later LOSL triggering sequence and eliminating the impossible possibilities. Moreover, we deduct the trajectory by step-wise calculation and get the final result which that fits in and explains all other criteria. However, when the targets are in close proximity to each other, it is sometimes impossible to find the exact trajectories. For an example, in Case 2 of Algorithm 2. The CP Determination for Tracking Multiple Targets.Initialization: Get the coordinates of all wireless sensor nodes, as well as topology information of all LOSLs.Step 1. Decide the number of targets by triggering LOSL in separated zones  if the targets do not meet at the neighbor regions. Step 2.   Divide the triggering LOSLs into groups belonging to different targets.Step 3.    Algorithm 1;Individually perform tracking work on each target by using the geometric constraint method presented in Step 4.    List all the possible explanations when multiple targets are triggering the same LOSL and conflict occurs.Step 5.    Further adopt the later trigging LOSL measurements to eliminate the other possible explanations to get the unique solution.  else    Give warning sign and increase the LOSL density to solve the meeting event.  endIn general, the approach for tracking the multi-targets can be briefly presented by Algorithm 2:In wireless communication scenarios, the RSS can be measured only when the wireless node pairs are undergoing communication, which will result in power consumption. Under the ideal wireless propagation model, the relationship between RSS  follows :(5)Pr(dIf the minimum receiving power is fixed as In a WSN operating at 2.4 GHz, Suppose the WSN contains In the CP estimation scheme mentioned in 2 area. For energy conservation, not all of the nodes are communicating with other nodes; instead, we just choose parts of them to form a LOSL web. For example, in Here, we provide a level classification approach for the popular experimental scenarios in In the proposed scheme, the WSN is initialized by remaining in the lowest Level 3, as the “standby state” with a minimum number of LOSLs, where it waits for the target. When the target gets into the monitored area, the level of LOSL density will be dynamically adjusted depending on the situations for the purpose of energy conservation while guaranteeing the tracking performance.The system adaptively increases the LOSL density level under two representative situations: when we want to get better tracking accuracy of the target, or when the trajectories of targets are not clear enough. As for the first situation, if we just want to get higher accuracy, we can make it by increasing the number of LOSLs between node pairs. By changing WSN from Level 3 to Level 5, then, we can get more CPs, and we can track the trajectory of the target more accurately. The second scenario specifically means that the meeting event happens under multi-target scenario, or abnormal LOSL sequence emerges; then, we can assume that there will be a tracking error. The tracking error is not easily corrected with only the low level of web. On the contrary, for energy saving, the system will automatically put some of the nodes into sleep mode when the tracking accuracy requirement is lowered. This action will also be performed when no indecipherable LOSL sequence emerges within a predetermined time period.2 area. The level of the adaptive network ranges from Level 2 to Level 9 according to the adapting node number in After acquiring the prior knowledge of the RFT identification through practical experiment in Results of the proposed scheme based on the geometric constraint method are shown in Our proposed method is compared with existing RTI, as well as the NOOLR method in 2 at Level 3, around several square meters at Level 5, and less than 1 m2 at Level 9, see in As we discussed in the multiple target scenario, the targets can be tracked using the single target tracking method and the subsequent LOSL triggering measurements when they are moving in separated regions. However, when the targets meet at the neighbor regions, the estimation work for multiple trajectories can have manifold forms of solution, which results in tracking failure. Increasing the web level can mitigate this problem. After 100 independent Monte Carlo runs, To show the energy efficiency of the adaptive networking scheme, simulations are conducted as follows. Assuming that two device-free targets are passing through the monitoring area simultaneously from left to right, an adaptive networking scenario is illustrated in In the traditional network for tracking, the networking level has to be constantly set as 9 to guarantee multi-target tracking success, whereas the adaptive network can adjust the networking level in real time. As shown in In this paper, we proposed a passive tracking scheme exploiting adaptive networking LOSL webs. In the proposed scheme, the LOSL density level between wireless nodes pairs can be constructed flexibly to address the energy conservation while guaranteeing the tracking performance. Firstly, as fundamental knowledge, we explore the feasibility of applying the RFT for moving target tracking. For the quantifiable performance evaluation of the proposed tracking scheme, we mathematically presented the CP estimation under both single-target and multi-target scenarios, as well as the power consumption model in a specific WSN environment. The simulation results show that the proposed passive tracking method based on CP estimation can produce rather high accuracy with lower hardware-cost; moreover, the power consumption of the adaptive networking scheme in a specific tracking scenario can decrease by around 60 percent.As for the future work, the effectiveness verification of the proposed tracking scheme in realistic experiments is the main task for the moment. The biggest challenge is improving the effectiveness of RFT detection, because the passive tracking scheme will be out of question if we have no accurate measurement of the LOSL triggering. Although we have demonstrated that the RFT on the LOSL can be detected in the


Surgeons are looking to use computer computer-assisted surgery (CAS) in total hip arthroplasty (THA) in order to quantify leg length measurement, angular cup placement, and enhance stability to provide enhanced accuracy in implant placement. As a result, CAS in THA is gaining popularity. This technology employs the use of pins and provides the surgeon with real-time feedback on positioning intraoperatively. Previous total knee arthroplasty (TKA) literature has reported pin-associated complications such as infections, neuropraxia, and suture abscess. To our knowledge, there have been reports of tibial stress fracture after CAS TKA, but this is the first report of a pin causing fracture of the greater trochanter leading to dislocation in THA. Further studies may be warranted to optimize pin placement for trackers to prevent fractures of the greater trochanter. Total hip arthroplasties (THA) are routinely performed and have a positive impact on patients' lives –3. In adHowever, as with any new technology, there is a risk of potential complications. The CAS camera tracker requires several pins to be placed in the iliac crest and greater trochanter of the femur. There are several reports of complications associated with the pelvic pins; however, there are no known reports related to pins placed in the femur , 11.We report this complication of CAS not to discourage the use of navigation but instead alert surgeons to potential risks when using CAS. In addition, this case report serves as a reminder to be careful in the placement of navigation pins to prevent occurrence of fractures.Patient consent was obtained for this case report. The authors declare that there is no conflict of interest regarding the publication of this paper.The patient is a healthy 66-year-old male that presented with a five-year history of restricted motion and pain of the left hip associated with Kellgren-Lawrence stage IV arthritis .As a result, the patient had a left THA utilizing a posterior approach and CAS under general anesthesia. Navigation for the total hip was utilized to optimize radiographic alignment position offset and lengthening parameters. This was done by first acquiring predetermined landmarks to determine the center of the joint after stabilization of the camera in the anterior superior crest with two pins. Once the femoral head center was established, the CAS was digitized to acquire the femoral shaft to triangulate the pelvis, acetabulum hip center, and femur. While the femoral tracker was placed for the best camera visualization, effort was also taken to place the tracker on the flat lateral side of the trochanter. The CAS camera and tracker placement consists of a camera mounted on the superior crest of the ileum. The femoral tracker is fixed to the lateral greater trochanter. Usual placement is three cm below the tip of the greater trochanter on the posterior lateral border. Of note, intraoperatively additional retraction around the tracker mount on the femoral side of the joint at the level of the iliotibial band was needed to prevent tracker interference during rotation of the hip joint to measure alignment. Because the fascia is strong enough that it can alter the tracking accuracy, it was necessary to utilize retraction, which may have added additional stress on the tracker base.An intraoperative pelvis X-ray  was takePostoperatively, the patient received apixaban (Eliquis), hydrocodone-acetaminophen for pain control, and bilateral sequential compression devices. The patient remained restricted to the bed and placed on fall precautions until occupational therapy visited the patient on postoperative day one. The patient did not report a fall, but did note a “grinding sensation” on weight bearing. Physical exam revealed shortening of the left lower extremity. An X-ray demonstrated an anterior dislocation of the left femoral head prosthesis and fracture of the greater trochanter . ConsequPostoperatively, X-ray findings confirmed correct alignment . PostopeComputer-assisted navigation technology is utilized intraoperatively to provide surgeons real-time feedback on the position and orientation of implants , allowinThis is the first reported case of periprosthetic fracture in THA that is associated with the pin sites. Conversely, in TKA, there are many previous studies that have demonstrated tibia fractures (0-15%) postoperatively –25. ThouThere is little information on when the bone is most at risk for infection or fracture after the removal of the screw, and experts are still debating in what time frame after pin placement the bone is most at risk for fracture . This caMoreover, the construct of the base of the greater trochanteric tracker disc (2.4 cm by 7 cm) utilized in this case poses potential additional complications. The base mount includes 4 3/32 inch diameter pins in variable lengths from 1/4 to 3/8 inch. The tracker base is the affixed with a single cancellous screw, which creates an additional stress riser . Though Furthermore, in this case, it is most likely that the fracture of the greater trochanter led to the anterior dislocation of the femoral head prosthesis. Intraoperatively, it was determined that the acetabular component was aligned. CAS values for the cup and femur were 43 abduction and 20 degrees anteversion, respectively. Due to the alignment obtained intraoperatively, the components of the revision were not changed from the original surgery. Lewinnek et al. previously reported that dislocation of the hip if the acetabular was in 5-25 degrees of anteversion and 30-50 degrees of abduction was four times less likely than the CAS measurements in this reported case . As the Limitations of this case report include that it is unknown if the fracture occurred first or the hip dislocation of the hip occurred first. However, if we assume normal position and hip dynamics, it can be presumed that if the fracture had not occurred, the dislocation would have been prevented. In addition, the patient had no significant trauma postoperatively, and even if the patient did dislocate their hip due to poor component position, the periprosthetic fracture should not have occurred. Furthermore, the stresses due to the navigation pins are strong enough to cause the fracture of the femur, because of the stress riser from the pin site placement. Therefore, this report suggests that the tracker should possibly be placed more posteriorly, where there are less tensile forces from abductors in order to avoid fractures of the femur. Moreover, additional research and development may be needed to determine the placement of pins to allow CAS to have utility, but decrease the amount of biomechanical stress on the bone. This may include development of an alternative strategy to mount the camera and tracker without the use of pins to avoid additional stress to the femur or injecting a gel or glue into pinholes following the removal of the pins to prevent the propagation of potential fractures.As computer-assisted navigation is more commonly used by surgeons, complications such as fractures may be a risk. There is also a potential need to adopt less invasive tracker mounts, which do not damage the cortical strength of the femur. In addition, surgeons should be vigilant in retraction of the femur especially around the tip of the trochanter by placing force distal to the tracker base. While, it may be beneficial to mount the tracker base at a different position to prevent injury to the cortical bone, design changes of the tracker base may also be necessary. However, it is hoped that by alerting surgeons to these potential complications, such problems will be minimal in the future.


Wireless Sensor Networks (WSNs) have become a significant part of surveillance techniques. With unequal clustering approaches and multi-hop communication, WSNs can balance energy among the clusters and serve a wide monitoring area. Recent research has shown significant improvements in unequal clustering approaches by forming clusters prior to the selection of cluster heads. These improvements adopt different geometric fractals, such as the Sierpinski triangle, to divide the monitoring area into multiple clusters. However, performance of such approaches can be improved further by cognitive partitioning of the monitoring area instead of adopting random fractals. This paper proposes a novel clustering approach that partitions the monitoring area in a cognitive way for balancing the energy consumption. In addition, the proposed approach adopts a two-layered scrutinization process for the selection of cluster heads that ensures minimum energy consumption from the network. Furthermore, it reduces the blind spot problem that escalates once the nodes start dying. The proposed approach has been tested in terms of number of alive nodes per round, energy consumption of nodes and clusters, and distribution of alive nodes in the network. Results show a significant improvement in balancing the energy consumption among clusters and a reduction in the blind spot problem. With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely Wireless Sensor Networks (WSNs), can be deployed in a wide variety of applications such as healthcare , smart iTypically, a WSN consists of hundreds or thousands of sensor nodes and Base Stations (BSs). Nodes in WSNs can be homogeneous or heterogeneous  and commPartitioning the network in a cognitive way to specify the size of the clusters for balanced energy consumption.Adoption of two-layered scrutinization for the selection of CHs to guarantee minimal energy loss from the network.Shortening the duration of declining state to reduce the blind spot problem.This paper proposes a cognitive partition-based unequal clustering approach to address the blind spot problem in WSNs. In addition to smaller closer clusters, the proposed approach ensures size-based balanced energy consumption. The selection process of CHs in the proposed approach is divided into two layers, such as weight-based selection of candidate CHs and cumulative distance-based CH selection for each cluster from the candidates. The proposed approach guarantees CHs to have short distances among them and consumes the least energy for packet forwarding. As a result, the lifetime of the network increases with a longer steady state than the declining state that reduces the blind spot problem in the network. The contribution of this paper is novel in the following aspects-The rest of the paper is organized as follows. Previous works on unequal cluster formation in WSNs can be broadly categorized into probabilistic, deterministic, and preset approaches. CHs are determined randomly in probabilistic approaches, whereas deterministic approaches adopt weight functions, fuzzy logic, heuristic techniques or a hybrid of these to determine the same. The preset approach simplifies its process by predetermining node locations, clusters, and CHs. The proposed approach presented in this paper would be a new addition to the hybrid unequal clustering approaches as it includes both optimization and weight-based approach to select CHs. Clustering in WSNs is typically done for preserving network lifetime by balancing the energy consumption. However, there are other approaches besides clustering that achieve the same goal. For example, the approach presented in Ref.  uses cooSeveral clustering approaches ,24,25,26Fuzzy logic is also used in a lot of protocols ,30,31,32Recently, several proposals have been made based on heuristic unequal clustering approach ,37,38,39Among the recent works on hybrid unequal clustering approaches, the proposal in Ref.  focuses The limitation of these approaches is that, their procedures of increasing the network lifetime prolong the declining state that introduces blind spot problem in the network. Declining state refers to the last stage of a network lifetime that begins when the nodes start dying. A long-lasting declining state in a given lifetime can degrade the performance of any clustering approach. The approach proposed in this paper tries to keep the declining state short by maintaining more equivalent residual energy in nodes after each round. For this purpose, it divides the monitoring area into several partitions before the selection of CHs that is similar to the approach presented in Ref. . HoweverThis section describes the proposed clustering approach for reducing the blind spot problem in WSNs. The proposed approach is divided into the cluster formation phase and the CHs selection phase in order. The CHs selection phase is further divided into the candidate CH selection and the final CHs selection.G = , where V is the set of all sensors in the network and E = {u and v and R is the transmission range. It is assumed that:Homogeneous sensor nodes with the same functionality and capacity are deployed uniformly within a rectangle area. The BS is located at a distance from the monitoring area.Intra-cluster and inter-cluster communications are a single-hop and a multi-hop data transfer respectively that are conducted by the CHs, i.e., each CH must send the traffic to the next CH towards the BS.Only one CH is selected from each cluster in a round.As data aggregation is out of the scope of this paper, it is assumed that each event is captured by the nearest sensor only and each event generates an equal amount of data unit.A WSN can be represented by the graph R. Otherwise the nodes had to use too many hops to reach their own CHs which results in energy-inefficiency inside the clusters. Considering the circular shape of wireless propagation model, cognitive grid partitioning of the monitoring area would be a perfect network model for facilitating monitoring rectangular area of any size which is illustrated in P into n unequal sectors The proposed clustering approach can be divided broadly into (1) Energy-balancing cluster formation and (2) Repetitive operational rounds. After deployment, sensor nodes send their residual energy to the BS to facilitate the partition of the entire area n serial partitions, namely P that is closest to the BS. Now, assuming that each Z is the total number of events occurred in P within a given time frame, the BS computes the energy loss at each Z..n serialHere, Equation . The totn clusters in the network. For this, the BS selects a set of candidate nodes w is desiHence, for adopting the proposed approach in heterogeneous WSNs, besides residual energy, replaceable nodes, and nodes degree other factors e.g., processing power, transmission power, etc., should be considered also. Furthermore, it is recommended that nodes of all types are uniformly distributed such that each cluster gets equivalent ratio of normal and advanced nodes.The proposed clustering approach has been simulated with MATLAB. The energy consumption model (Equation ) of the k bits of data and D. The weight of any node i is determined using Equation (i. The list of simulation parameters and the cluster properties generated by the proposed approach are shown in Here, Equation . Here, EAt the beginning of the operation, the network shows good performance by capturing the desired number of events per unit of time. However, towards the end of the lifetime, the network experiences death of its constituent nodes which results in inability to capture events in certain places. This scenario is described as blind spot problem which results in degradation of network performance in terms of number of captured events per unit of time. The sole reason of this problem lies on the fact that a few nodes die out quickly in the network due to unbalanced energy consumption. The more unbalanced the energy consumption is, the more quickly nodes start dying in the network. On the other hand, majority of the recent clustering approaches focus on enlarging the network lifetime without incorporating a robust energy-balancing technique. This results in a long duration between FND and LND, i.e., the network suffers from event capturing inability or blind spot problem for a long time. To reduce this sufferance, this paper focuses on adopting a mechanism that forces a balanced energy consumption in each round for both nodes and clusters.Distribution of dead and alive nodes is also an important aspect to consider while evaluating the performance of a clustering approach. Beside of reducing the duration of the declining state, the clustering approach should ensure the uniform occurrence of node’s death throughout the network during this state. This maintains the consistency between the alive nodes and the probability of capturing an event during the declining state. In terms of alive nodes per round , the proFrom From In this paper, we have proposed a new unequal clustering approach for WSNs that minimizes the blind spot problem while prolonging the network lifetime. This approach promises equivalent and least energy consumption from the clusters in each round. For this purpose, the clusters are formed by dividing the monitoring area into multiple partitions in a cognitive way. Such a partitioning approach ensures the consumption of energy to be equivalent in each cluster. Furthermore, the proposed approach adopts a two-layered scrutinization while selecting the CHs. This ensures the least energy consumption from the network and prolongs the steady state. In addition, nodes are left with small energy before entering the declining state as they maintain equivalent energy consumption throughout the steady state. Consequently, the declining state gets a short duration that reduces the blind spot problem in WSNs.Future work for extending the proposed approach would entail the cognitive partitioning of the network to support scalability. The authors have already developed a Blockchain-based protocol suite for WSNs  that ado


Auditory mismatch negativity (MMN) is an event-related potential (ERP) waveform induced by rare deviant stimuli that occur in a stream of regular auditory stimuli. The generators of MMN are believed to include several different cortical regions like the bilateral temporal and the right inferior frontal gyrus (IFG). However, exact cortical regions associated with MMN remain controversial. In this study, we compared the number of long-distance connections induced by the standard and deviant stimuli during awake state and propofol anesthesia state to identify the cortical areas associated with the generation of MMN. In awake state, we find that deviant stimuli synchronize more information between the right frontal and temporal than standard stimuli. Moreover, we find that the deviant stimuli in awake state activate the bilateral frontal, central areas, the left temporal and parietal areas as compared to the anesthesia state, whereas the standard stimuli do not. These results suggest that, in addition to the bilateral temporal and the right IFG, the bilateral frontal and centro-parietal regions also contribute to the generation of MMN. Auditory MMN is an ERP waveform induced by rare deviant stimuli that occur in a stream of regular auditory stimuli  is the primary tool to test the predictive coding hypothesis  participated in this study. All the subjects are drug-free and allergy-free to propofol. Exclusion criteria included a history of neurological, psychiatric or hearing diseases. Each participant was asked to fast for at least 8 h before propofol administration.Scalp EEG data was acquired from 64 Ag/AgCl electrodes which were placed according to the International 10–10 system. The reference electrodes were placed at bilateral mastoids. Two electrodes were placed above and below the left eye to record the vertical electro-oculogram (VEO). The impedance of all electrodes was kept below 5 kΩ. The EEG data was recorded using SynAmps amplifier  with a sampling rate of 1,000 Hz.n = 200) and two deviant tones . There are 300 randomly ordered tones in each block. The duration of tones was 75 ms with a 10 ms rising/falling time, and the inter-stimulus interval between each stimulus was 1000 ms. E-prime software  was used to design and present these pure tones (80 dB) through a pair of earphones .The MMN paradigm contained three kinds of pure sinusoidal tones with different pitch: a standard tone  were continuously monitored by using intra-operative monitors . The anesthesia depth was tracked by using the bispectral index monitor. The bispectral index (BIS) is a processed EEG parameter and it has been widely adopted in the clinical settings for assessing the level of anesthesia during surgery . During the experiment, vital signs including heart rate, blood pressure, respiratory rate and pulse oximetry  and deviant stimuli  were extracted with 100 ms before to 700 ms after the stimulus was given. The clean epochs were baseline-corrected by subtracting the mean amplitude of the first 100 ms of each epoch. Then, the extracted epochs were grouped and averaged according to the types of stimuli, i.e., the standard stimuli or deviant stimuli. Moreover, we only considered the deviant stimuli which is preceded by at least 3 continuous standard tone. The MMN waveform was obtained by subtracting the averaged EEG signal of standard stimuli from that obtained on deviant stimuli. Subjects with no MMN waveform were excluded from further analysis.Since long-range synchronization among cortical regions was mostly presented in the high frequency band  was employed to measure the connectivity between a pair of ROIs. The reason of selecting wPLI is due to its robustness against the influence of volume conduction  and its corresponding Hilbert transform where i always leads or lags signal j in all trials and it equals to 0 indicates that the phase difference between two signals are completely random across trials.The obtained wPLI ranges from 0 to 1 in which 1 indicates signal N to maintain the integrity of network, where N is the number of nodes in the network (Watts and Strogatz, To prevent false connections, the obtained wPLI of all possible pairs were pruned by preserving the strongest connections above a threshold. Previous research showed that the average degree of networks must be larger than 2 × ln In source analysis, we grouped the 148 ROIs into 14 cortical areas according to its anatomic location. Similar to the previous work (Kitzbichler et al., The weighted value of long/short connections was calculated as the sum of the corresponding wPLI values in the connection matrix. The long-distance connection of the whole brain network was given asThe short-distance connection of the whole brain network was defined as:k∈ is the index of the 14 cortical areas, Lk and Sk represent the connection strength of long/short-distance connects, respectively. The ratio between the value of the long-distance connections and the short-distance connections was defined aswhere Ratio of the brain networks involved in processing the two kinds of stimuli in awake state. If there was a statistically significant difference on Ratio, then we compared Lall and Sall obtained from two kinds of stimuli to find out the cause of the difference in Ratio. Then, we compared the obtained measures between awake and anesthesia states.We first compared the Ratio between the two states and between the two kinds of stimuli were conducted by using paired t-test. P < 0.05 was considered as statistically significant.Statistical comparison of the number of long/short-distance connections and Fourteen out of 25 subjects showed a typical MMN waveform during awake state, the other 11 subjects with no clear MMN waveform were excluded from further analysis. The obtained MMN waveform from three representative subjects at Fz electrode during awake state was provided in Figure Ratio between the long/short-distance connections induced by deviant stimuli was significantly higher than that induced by standard stimuli. However, no difference was found during anesthesia state. It was noticed that the Ratio of two kinds of stimuli were both higher during anesthesia than during awake state. We then compared the difference of the Sall and the Lall of two kinds of stimuli during awake state and anesthesia to investigate the cause of the increase in Ratio. As shown in Figures Sall and Lall were decreased under anesthesia. However, the reduction on Sall was significantly higher than Lall, which results in an increase in Ratio during anesthesia state.As shown in Figure Ratio between the standard and deviant stimuli might be caused by the changes of the Lall or the Sall, we compared the Lall and the Sall of the two kinds of stimuli in both awake state and anesthesia state. We found that in awake state, the Lall of deviant stimuli was significantly higher than the standard stimuli, while the Sall of these two kinds of stimuli had no significantly difference (Figure Lall of deviant stimuli. As shown in Figure Lall, Sall, and Lk at each cortical region between the standard and deviant stimuli during anesthesia state (Figures Since the changes in e Figure . This fi Figures , 4.We also compared the number of long-distance connections induced by standard stimuli and deviant stimuli between the awake and anesthesia states. As compared to the state of anesthesia, standard stimuli in the awake state induced greater number of long-distance connections at left prefrontal and right central areas Figure . In contIn this study, we compared the number of long-distance connections induced by the standard and deviant stimuli during awake state and propofol induced anesthesia state to identify the cortical areas which are associated with the generation of MMN.We found that in the awake state, deviant stimuli induced a greater number of long-distance connections than standard stimuli due to its high demand on cognitive function. This result was in consistent with the hypothesis of the global workspace theory (Kitzbichler et al., By comparing the measurements of brain networks involved in processing the two kinds of stimuli between awake and anesthesia state, we found that deviant stimuli induced a greater number of long-distance connections at the bilateral prefrontal, central, parietal and cingulate areas in awake state than that in anesthesia state. However, significant difference between two states induced by standard stimuli only existed on two cortical regions: left prefrontal and right parietal areas. As shown in Figure Our results revealed that in the awake state, deviant stimuli induced more long-distance connections at bilateral temporal regions. However, the number of long-distance connections induced by deviant stimuli had no significant difference between the two states at bilateral temporal regions. There were several studies suggested that the cortical regions that are responsible for higher-level cognitive process is more sensitive to anesthesia as compared to the lower-level sensory cortex. The temporal cortex is responsible for the perceptual function of the auditory signal, which belongs to the low-level cortex and it is less sensitive to anesthesia, hence the number reduction on long-distance connections at temporal cortex during anesthesia is not as obvious as observed in other cortical areas.The temporal cortex is responsible for forming sensory memory model, while the frontal cortex is responsible for comparing the memory model to the input stimuli. The sensory memory model will be adjusted when the frontal cortex finds that the current stimulus and the model does not match, and this process would result in the MMN (Garrido et al., Several studies have suggested that the involvement of frontal lobe might be related to a specific MMN paradigm (Näätänen and Alho, Our results also suggested the parietal region (Kasai et al., There are several limitations of our study. We used the standard Colin27 head model as the standard head model of every participant in source analysis, which might slightly bias the accuracy of the source reconstruction. Moreover, the EEG signal only record the neural activities on the scalp, the activation of the inner cerebral hemisphere, such as cingulate gyrus, cannot be accurately observed even by using the source analysis. Hence, we excluded the involvement of cingulate gyrus during the generation of MMN.In conclusion, by comparing the brain networks involved in processing the standard and deviant stimuli in the MMN paradigm during awake and anesthesia state, we observed the increased number of long-distance connections originate from the bilateral prefrontal, temporal, central and parietal cortices, which suggested that the generation of MMN would require communications among prefrontal, temporal, and centro-parietal regions.YZ processed the data and wrote the manuscript. FY recruited subjects, performed data collection, and revised the manuscript. LW performed the data collection and processing and wrote the manuscript. YW performed data processing and revised the manuscript. CW performed the experiments. QW was responsible for designing the study and manuscript editing. LH designed the study and reviewed the manuscript. All authors contributed to the completion of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer DO and handling Editor declared their shared affiliation.


Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either  LiDAR systems are rapidly becoming an integral part of automated and intelligent vehicles for environmental awareness. The price of LiDAR sensors is reducing, and automakers are increasingly installing LiDAR in production vehicles for advanced intelligent functions ,2. LiDARAll objects in the driving environment are classified as dynamic or static according to the moving conditions. Therefore, the LiDAR point cloud from detected objects can also be classified as dynamic or static point-wise depending on the motion state of the object. Such point-wise classification of point cloud states can be used for safety and convenience functions in automated and intelligent vehicles. For instance, points classified as static are measured from the surfaces of static objects, such as curbs, poles, buildings, and parked vehicles. Such a static point cloud can be applied to various automated and intelligent driving functions, such as mapping, localization, and collision avoidance systems ,4,5. PoiAs shown in the previous examples, the information of the point cloud state is classified according to the motion is useful for automated and intelligent vehicles. This paper proposes an algorithm to rapidly segment the motion states of a point cloud detected by LiDAR in real-time. The overall process of the proposed algorithm is shown in This research has three main contributions: (1) reflecting the laser characteristics of LiDAR, (2) applying a combination of probabilistic and evidential approaches to update the motion state of points, and (3) online motion updated for real-time applications. This paper focuses on the characteristics of lasers, such as multi-echo, beam divergence, and horizontal and vertical resolution, so that it can segment the motion of points more accurately than existing algorithms, such as occupancy grid mapping. In addition, when updating the state information, a combination of probabilistic and evidential modeling is applied to more accurately reflect the actual LiDAR characteristics to update motion in a point-wise manner. Because all the proposed updating processes are real-time, they are suitable for real-time application in automated and intelligent vehicle systems.With the development of autonomous vehicles, LiDAR is being more widely used, and many studies of LiDAR are accordingly being conducted. However, there have not been many studies that classify the point-wise motion of LiDAR measurements themselves in real-time. In the field of autonomous and intelligent vehicle systems, studies related to the proposed algorithm aim to generate static environment maps or remove nonstatic points based on tracking results.An occupancy grid map is a typical method for generating a static environment map using LiDAR measurements. The occupancy grid map divides the environment around the vehicle into a 2D grid or 3D voxel cells with uniform size. The occupancy level of each cell can be updated through the laser’s ray tracing. The occupancy level of a grid (or voxel) cell passing by the laser becomes lower because the physical space of the cell is likely to be free. Conversely, the levels of cells located on the reflecting surface become higher. Based on these principles, static objects are classified as occupied when the occupancy level exceeds a certain threshold. Contrarily, for a moving object, the occupancy level of the cell is not constantly accumulated, so it is not classified as occupied. The numerical value of each cell’s occupancy level is updated based on probability theory  or belieThe advantages of occupancy grid-based static point cloud classification are that its implementation is relatively straightforward and its performance is stable because it has been studied for a long time in various applications. However, occupancy grid mapping has several disadvantages for use in real-time automated and intelligent vehicle applications. Large memory is required because the driving environment must be represented by a grid or voxel cells. Also, the ray tracing method takes a long time to update all cells related to all LiDAR beams. In addition, because space is represented by discrete cells, a discretization error occurs when the resolution is coarse.Research on the detection and tracking of moving objects using LiDAR has been conducted to recognize the driving environment of automated and intelligent vehicles ,18. ObjeThe point motion classification algorithm presented in this paper has many advantages over previously proposed ones. First, the proposed method directly segments the point cloud into dynamic and static states using the laser beam model. Therefore, there is no chance of misclassification due to discretization error in the occupancy grid method and erroneous clustering of the tracking-based method. In addition, the proposed method does not require a large amount of memory like the occupied grid approach because it simply buffers recent point clouds. Finally, the proposed method is able to satisfy the real-time requirements of point motion classification because it does not need to update all of the gird cells to initialize a new track.n describes the index of the laser beam from 1 to N. The m represents the order of multi-echo for the laser beam and usually has a value of 2 or less. The t represents the time for the LiDAR scan measurement. The “motion state” has three possible values: The objective of the proposed point motion segmentation algorithm is to classify the latest LiDAR point, W denotes the time window size of the previous data buffer to be used for motion classification of the current point cloud. There are several methods for obtaining the sensor pose The basic principle of the proposed algorithm is to classify the motion state of the current point cloud by applying the laser characteristic model to the registration relationship between the previously measured point cloud and the current point cloud. The inputs of the proposed algorithm are the current point cloud, The point motion segmentation algorithm consists of two steps: (1) probabilistic modeling of point motion and (2) evidential point motion classification. In the first step, the probability of In the second step, the probabilities of each motion classification LiDAR uses rotating laser beams to measure the distances and angles from surrounding objects. A laser pulse is emitted at a specific angle, and the distance to the object for that angle can be measured using the time-of-flight (ToF) principle, as demonstrated in The actual LiDAR’s laser is not emitted in a straight line, as shown in the r is distance to a point with a second echo, m is the number of echos. The Cartesian coordinate representation is The LiDAR point cloud measurement N LiDAR points W-buffered point clouds The point motion segmentation classifies the latest Probabilistic motion modeling of the point cloud Equation .(1)pp(moEquation .4)p(mop(mo(3)p..zti,m isquations . HoweverThe process of point-wise probabilistic motion estimation achieved through the likelihood field was described in the previous section. However, there is a limitation to the probabilistic method if the point Both probabilistic and evidential approaches are based on the concept of assigning weights to the hypothesized states of the measurement. However, the evidential approach allows sets of alternatives, which means new states can be created by combining existing states. The probabilistic approach deals with the two states k is the tuning factor, which determines the size of the likelihood boundary, and we used k. Because the confidence of the probabilistic model decreases as the time difference k increases, the confidence Based on the evidential approach, we can explicitly handle the points located outside the likelihood field for the given point Equation .(10)λk=W, several mass functions For the given point Equation ) is applEquation .An autonomous vehicle (A1) was used for the experiment to evaluate the proposed algorithm. A1 was equipped with two LiDARs (Velodyne VLP-16) and an IMU, as shown in LiDAR lasers beams have beam divergence, which means the beam cross section is increased over time. We designed a beam divergence model based on the specification of the LiDAR sensors, as shown in Dead reckoning was implemented using the IMU to estimate the LiDAR pose for each time step. For the experiments, raw IMU data (acceleration and gyro) from the RT3002 sensor were used without real-time kinematic (RTK) GNSS correction. The specifications of the MEMS IMU are listed in The synchronization between the LiDAR, IMU, and point motion classification algorithm was measured by a pulse per second (PPS) signal from an RT3002. The LiDARs and IMU were precisely calibrated to be located in the same coordinate system.To evaluate the performance of the point-wise motion segmentation, experiments were conducted under various scenarios . The total length of the experiment road is more than two kilometers. W is 20, 30, 50, and 100, as shown in To quantitatively analyze the segmentation performance, confusion matrices for the tracking-based classification algorithm and the proposed algorithm with various time window configurations were created, as shown in The algorithm was verified in an RTMaps environment with a QuadCore Intel Core i5-3570K, 3600 MHz  of the LiDAR sensor. The point-wise motion segmentation is performed based on the laser beam characteristics and the 3D pose correlation between consecutive LiDAR points. A combination of probability and evidence theory is used to accurately and reliably segment the motion state of points into (1) The point motion segmentation algorithm considers the characteristics of the LiDAR laser beam, such as multi-echo, beam divergence, and horizontal and vertical resolution. Therefore, the point motions are segmented more accurately and reliably than by conventional algorithms .(2) To update the motion state of each LiDAR point, a combination of probability theory and evidence theory is applied to point motion modeling to accurately reflect the LiDAR characteristics. Probability theory is used to model the likelihood fields of LiDAR point clouds, taking into account the uncertainty of LiDAR measurements. Evidence theory is used to incorporate multipoint motion probabilities, taking into account pose uncertainty and the (3) The proposed point motion segmentation algorithm was evaluated experimentally. The segmentation accuracy was The performance of the algorithm is related to the positioning algorithm that estimates the pose of the LiDAR sensor. In future research, we plan to analyze the quantitative relationship between the LiDAR sensor positioning and the proposed algorithm performance, and to study the SLAM algorithm that classifies the point motion and simultaneously estimates the pose of the LiDAR sensor.


Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique. The precise indoor location information of a mobile node with ubiquitous resources in an environment is one of the hot topics in the field of geolocation technologies today. Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) can only be used for outdoor location-based services (OLBS), while for indoor environments, satellite signals are strongly attenuated because of multipath effects, etc. and become unreliable for positioning indoors. As a result, many IPS solutions have been proposed so far to enable indoor location-based services (ILBS) for complex indoor environments, including hospitals, airports, shopping malls, and so on. ILBS also helps with resource management, which includes optimal deployment, locating, and monitoring of resources. IPS solutions are classified as infrastructure-based and infrastructure-free technologies. Infrastructure-based solutions primarily include Radio Frequency Identifier (RFID), RF, Bluetooth, and Ultra-Wideband (UWB) based technologies, while infrastructure-free solutions are typically based on Wi-Fi, magnetic field, motion sensors ) and vision techniques . InfrastWi-Fi-based techniques are generally divided into calibration techniques and techniques without calibration . An indoIn this study, we are extending our approach from static radio maps generation  to the dThe rest of the document is organized as follows: Survey-based Wi-Fi fingerprinting technique consists of two phases: the offline phase of the database training survey and the online phase of location estimation. The training phase requires a comprehensive analysis of the environment to build an RSSI database with a certain degree of spatial granularity. It takes a lot of time to record the RSSI level at each point of the environment grid and becomes even more challenging in the working environments. Recently, many research efforts have been made to reduce this fastidious and time-consuming training phase of fingerprinting and several solutions have been proposed .Some solutions using interpolation techniques with a modified path loss model at sparse reference points to reduce survey effort to a certain extent are also proposed. In , the autCrowd-sourcing systems use active user inputs to create an online radio map database instead of using the tedious task of creating a training database through a survey. The Organic Indoor Localization OIL  integratRecent research developments in ILBS technology use data from a wide range of sensors  to improve indoor positioning and mapping  using daA probabilistic approach to location estimation using the fusion of multiple radio maps is described in the Probability Map Technique (PMT) . The proA model-based indoor positioning algorithm is presented in  to addreIndoor propagation prediction methods are generally divided into two categories. In the first approach, the free space propagation model is generated by fitting an empirical or statistical propagation to the measurement data. While the second approach is based on ray tracing in which the theory of electromagnetic propagation is applied. Here are some recent techniques that use vector maps of the environment to estimate indoor propagation.Empirical model based propagation predictions are simple in computation and are scalable. Here we discuss an enhanced empirical propagation model, which incorporates much of the propagation phenomena that are suggested by electromagnetic theory such as the uniform theory of diffraction (UTD). In this approach, propagation loss function has two distinct regions, the first region which is similar to free space and the second region where the effects of walls and ceiling become visible on propagation . Each reIn deterministic models, ray tracing and radiosity are very attractive due to their accuracy. However, due to the requirement of high computational and memory resources for 3D representation, these approaches are limited to 2D representation only. In , the autThere are also several other techniques proposed for automating propagation prediction to solve the tedious job of the manual survey of the environment. A closely related work is an online path-loss parameter estimation approach which is discussed in  where a Ray tracing seems to be an attractive solution, whereas, to ensure the rays incident at a certain point we need to increase the number of rays from emitting source which brings scalability issues due to high computational cost. Similarly, radiosity, with its high computational resources, limits the type of objects in the environment and their absorption and refraction properties to control the complexity of the maps in the calculation process. We have selected empirical method as a base model in our proposed solution due to its inherent characteristics of scalability, low computational cost and acceptance of an object of various types. And most importantly, the method based on ray tracing and radiosity requires vector maps while our system works with simple raster maps and requires no information on the angle and length of objects in the environment.In this study, we extend our approach of static path loss map generation using the raster map to more realistic dynamic online radio maps by adding IoT sensors to the environment, such as shown in Creating a raster map of the environment is a simple and important step in the proposed system. Pixel maps are created from the floor plans of the real-time test bench sites used in our experiments. First, the floor plan image is resized to a known resolution, e.g., 10 pixels/m. Then, each concrete wall with white lines  and wooden partitions with gray lines  is traced on a black background . For simplicity, only two types of walls are considered. The color value of the wall is an adjustment factor for the corresponding value of WAF induced. The adjustment factor must be carefully selected according to the thickness and material of the walls or obstacles. Finally, a grayscale image map with information on the size, type, orientation, and location of each wall in pixel format is obtained. For active updates of Wi-Fi signal strength from certain points in the environment, an economical ESP8266 module that offers Wi-Fi capabilities as an RSSI scan sensor node is selected. When the sensor starts sending updates to the system, this information can be used to calibrate the sensors. For sensor calibration, we simply calculate the RSSI reception difference of all sensors and the target mobile device. To this end, all the sensors and the target device are placed side by side, and the updates are saved. During this process, multiple scans of each sensor and mobile device are recorded to find the difference factor A is the MAC address, A is the MAC address, The preprocessing phase begins with the registration of APs and sensors. In this process, the proposed system needs to collect the location and RSSI information of each AP, including The next step is to estimate the path loss constant of the environment and the attenuation factor of the walls. To this end, the proposed system uses the Particle Swarm Optimization (PSO) algorithm to estimate the two parameters . Before The information given in Equation  from eacr and wall attenuation factor PSO algorithm iterates values from 1 to 10 for the unknowns X1 and X2 as path loss constant The last step of the first phase is the generation of metadata for the radio map of each AP. In this step, the proposed system defines the coverage area of each sensor and the corresponding AP on the site-map for generating radio maps. And most importantly, the preparation of metadata is done to reduce the computing resources required for Phase II, i.e., the generation of online RSSI maps. To generate a radio map of an AP at a resolution of 1 square meter, the proposed system needs the following information at each cell of the grid .Equation  shows thEquation –14) hel helMACS,Equation  that helThe second phase begins when the proposed system has collected the information given in the Equation  for eachEquation  represenEquation .(16)M Department and Computer Science (CS) Department are selected as real-time test-bed for our experimentation with an area of To begin the experiment, first, the sensors and target mobile devices are calibrated. All devices are placed side by side at the same location and 30 scan updates are collected, with each update averaging 5 scans. The RSSI level from 15 APs in the ascending order are compared, as previously reported in Before the system begins to create the fingerprint database (radio maps), it estimates environmental pathway loss and the WAF as described in 2 grid points are collected on both sites. The graph clearly shows that the proposed system estimated RSSI values correlates better to the real time values in both cases as compare to estimation done using empirical values. The correction of the RSSI estimate using fresh sensor updates is highlighted using dark circles in To validate the evaluation of path loss and attenuation parameters, the RSSI values estimated from the proposed system and the empirical approach are compared with the actual RSSI values collected for both LOS and NLOS cases. The Finally, now to verify the accuracy of our radio maps, we generated the fingerprint database of both environments using the estimated RSSI values of the APs at each cell of 1 m × 1 m square grid. For the test, we selected a path that includes three lab rooms, a common room, a corridor and a stairway area for Site A, while for Site B, the selected path includes 3 corridors, a common room, and an area for stairs, as shown in sourcing , and Ransourcing  approachThe strength of the proposed system lies in its “plug and play” features, works with simple raster maps and does not require detailed vector-based maps in which each length and angle must be calculated accurately. Secondly, the proposed system automatically estimates the propagation parameter of the environment and takes into account the temporal propagation loss through the online feedback of the sensors. Furthermore, changing Wi-Fi configurations, such as replacing, adding, or removing APs, requires a simple update of the registration list of APs instead of a recalibration survey of the complete environment. The correct configuration information of the location and power of the transmit/receive signal of the peripherals during the recording process may be considered a weakness of the proposed system. In In this paper, we investigated how the proposed system uses IoT sensors for automatic assessment of path loss and attenuation parameters in a Wi-Fi environment and for estimating signal propagation in a complex indoor environment by exploiting the architecture information provided in simple raster maps. In this way, the proposed system generates and updates the fingerprint database to enable indoor position estimation for fingerprint matching algorithms. In addition, a complete system that works from registration of devices and acquisition of online feedback from IoT sensors to the generation of the fingerprinting database is presented without any survey of the whole environment, in particular for an office like environments. Results show that the proposed technique is successful in automatic generation and calibration of the fingerprinting database and provides comparable accuracy in position estimation without doing any tedious job of the survey of the real environment. Possible directions for future work are, automated registration of AP with the location, radiant power of AP at 1 m, and directional path loss parameter assessment to improve the accuracy of fingerprinting. Incorporating deep learning based estimation of the propagation of the electromagnetic signal is another direction.


This study elaborates the research design and methodology to investigate the reverse culture shock (RCS) experience of young Thai tourists in the tourism field. Taking the worldview position of relative ontology and interpretivist/constructivist paradigms, this paper employs qualitative design using multiple research methods, namely, essay writing, graph plotting and semi-structured interview. Essay writing and graph plotting are initially used as pre-interview activities as part of the memory recall procedures. This stage is important because it helps curtail memory distortion and enrich insight into the participant’s past RCS experience. The semi-structured interview with young Thai tourists is subsequently conducted to elicit individual perceptual and emotional experiences and coping mechanism after they returned home. The three research methods complement one another to draw out rich travellers’ experience in the tourism study and can be beneficial to the extended disciplines of social science and psychology.Advantages of this article include:•Practical and feasible processes for a qualitative study in social science and psychology, particularly when recall of memory is involved.•The ability to gain enriched information of experience.•The ability to elicit emotional aspects from the study of experience through graph plotting. Specification TableA growing number of Thai youths assuming the role of tourists through exchange student and working holiday programmes have recently been investigated. However, youth tourists claim to face reverse culture shock (RCS) during their return to their home country (the re-entry phase), thereby negatively affecting the readjustment process to the home culture .Although the RCS phenomenon is widely proven in numerous countries, the insights into the RCS amongst youth tourists who participate in exchange student  and working holiday programmes have been rare and remain underexplored. Most diverse RCS studies in youth focus on full-time students  ahome culture ).In the next step, the researchers conducted another recalling memory activity to facilitate the participants to look back on their memory lane before the interview. A participant would be invited to attend the pre-activity session and bring the essay along. The researchers conducted a brief discussion with regard to the essay and asked probing questions to refresh the participants’ memory about the RCS. This initial talk would take for 10 to15 minutes. Afterward, the participants were asked to draw a graph to represent their feelings after they return to their home country at different points in time, starting from one month prior to return until eight months upon return. The researchers applied Ellis et al.  concept After the participants drew the graph according to their emotional stage at each timeline, they were asked to plot down the related emotions and perceptions onto the graph by writing down the number that reflects his or her emotion at each stage. The list of emotions and perceptions has been taken from the questionnaire items used in the RCS study of Gaw . MoreoveThe aforementioned procedures served as the pre-interview activities. The participants took approximately 45 minutes to one hour to draw the graph and plot their level of emotion and perception. Subsequently, the semi-structured interview was conducted. The interview aims to clarify and probe the details regarding the situations, perceptions and emotional stages, as well as their coping mechanism, during their return. The interview was conducted to supplement the graph that was previously drawn and thus allow the participants to elaborate their thoughts and feelings at each stage. Furthermore, the interview questions were customised to reveal the emotion and perception stages that the participants experienced and their coping strategies upon returning to their home country.The graph plotting was designed based on the emotional and time scales, which were adopted from Gullahorn and Gullahorn , includiThe interview questions required further explanation for the graph, and the additional items were used to tackle the third research objective (the application of western RCS frameworks in the Asian context). One example of the probing questions asked in the interview is, ‘Explain why you drew the graph in the period of […]. Specifically, what did you feel during that stage?’ The probing questions covered various dimensions that were affected based on the participants’ perception during the adaptation stages upon their return. These dimensions included relationship, family, work and daily lifestyle. Appendix 1 presents several examples of the graph and the interview questions. Given that all interviewees and researchers are Thai natives, the memory recall procedures and tools  were written in Thai language to minimise the language barrier when one’s information and expression are provided. The interview was recorded and transcribed verbatim.In conclusion, six-step procedures were involved in the recall process, particularly for investigating RCS in the study. Overall, the entire process took approximately 90 to 120 minutes. Details about the six-step procedures and the flow of research procedures are described as follows: Fig. 5ProPre-activities for the recall process 1The pre-interview and story-telling through essay writingPre-activities for the recall process 2The participants take their written essay about their overseas experiences, together with their diaries and photos, to the interview session.3According to their written essay, the probing questions are asked to the participants.4The participants are asked to plot the graph to indicate the relationship between time and their emotional stage.5The participants are asked to list down the emotions and perception  that thThe interview session 6The semi-structured interview is carried out.•They must have stayed overseas in a minimum of three months.•They must have spent time in a non-Asian country.•They must have returned to their home country (Thailand) for more than six months and up to five years.The data were gathered from 26 Thai youth travellers who have returned from overseas programmes. A total of 12 males and 14 females aged 16 to 22 years old participated in the study. Fifteen youths travelled to the United States (US), two went to South America whilst the rest visited Western Europe. The two categories of youth tourists were selected based on their travel purpose, namely, working tourist and educational tourist. The latter category was further divided into two subgroups, namely, 1) those who attended a one-year exchange program (13 youths) and 2) those who short-term exchange program (6 youths). Thus, the number of working tourists equals 7 youths. To determine the participants who were eligible for the interview process, the three following pre-screening conditions were employed.The first two conditions aim to ensure that the participants have spent sufficient time and have a chance to experience culture shock in the country where the cultural context is dissimilar to his or her own (see further ). The laThe multiple stages of data collection  serve as the source of data for the analysis process. Apart from the essay and graph that were written in Thai, the Thai interview content was also transcribed directly. All the data were then analysed in Thai language. Thus, the first-hand meaning of the Thai youth tourists’ experience and the sociocultural interpretation could be fully comprehended by the Thai-native researchers who exhibit good command of Thai and English languages. The researchers organise the data into themes and subthemes through the support by NVivo 11 software. Themes were indicated based on the pattern of data according to the perceptual and emotional aspects of respondents on RCS.The coding process of each interviewee’s script was separately conducted by two researchers. Subsequently, the codes from the two researchers were crossed-checked to ensure inter-coding reliability, thereby obtaining agreement on the majority of codes. The researchers discussed about uncertain codes and decided a consensus until all the codes were agreed upon. Certain sentences that were quoted into the manuscript were translated to English by the researcher who previously coded such sentences. Next, another researcher verified the translation equivalence that refers to the lexical, idiomatic, and syntactical similarity gained across diverse languages  of the qTo ensure the trustworthiness of the study, the researchers followed Lincoln and Guba ’s categocredibility that refers to the confidence in the ‘truth’ of the findings, the researchers employed method and data triangulation techniques to cross-check and validate the results. The three methods  that yield three data types were compared and contrasted to ensure the confidence level, minimise data contradiction and ensure data sufficiency prior to analysing their meaning. Transferability that refers to the ability to show that the findings have applicability in other context is done through logical and in-detail explanation of the research design and procedures. Dependability that reflects how the findings can be repeated is also achieved through the detail explanation of research procedures, along with documentations of the findings and researchers’ thoughts and ideas. Lastly, confirmability represents the degree of neutrality or the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest. This aspect was achieved through the reflexive discussion amongst researchers to ensure that potential biases on meaning interpretation were minimised.To ensure the study’s Thai youth tourists who return from the exchange programmes and from the work and travel programme demonstrated a number of perceptual stages after completing their work, holiday and educational experiences overseas. Their viewpoints were changed within the two broad themes (levels), namely, individual and social levels. Family and school were found to play significant roles and exhibit strong impacts on their perception of their respective home countries. In addition to the changes in perceptual dimension, the returnees experienced various emotions that occurred at different stages. For instance, their emotions were remarkably negative after few weeks to one month upon their return. The feelings mentioned were alienation, loneliness and depression, frustration, anger, boredom with the environment and sadness. Five respondents addressed their willingness to leave the country during this period. During the two to six-month period, returnees felt a mixture of negative feelings that were more dominant than positive ones. Upon entering the seventh and eighth months, the majority of returnees held more positive feelings than negative ones, which indicated improved adaptation at home. They mentioned about having more friends and positive relationships and feeling comfortable with their home environment. Nevertheless, a few negative aspects were still mentioned by the returnees who continued to experience difficulties in terms of fitting in.With regard to the coping strategies, four types of Adler ’s copingEach individual returnee was asked to plot his or her emotional level on to the graph sheet  where thSecondly, the graph that shifted to the left  demonstrLastly, the unfit graph and graph that shifted to the right  are alsoThis paper applies a mixture of qualitative research methods to investigate the RCS experiences of Thai youth tourists. The study reveals that by combining various techniques, rich and comprehensive data can be derived from Thai participants, which yield improved and different patterns from a complex phenomenon, particularly RCS. This paper including the selected methods can contribute to the extended areas of social science and psychology that requires the accuracy of memory recall.One of the difficulties or limitations found in the studies of culture shock  is the use of cross-sectional study rather than longitudinal study . In partAnother limitation of this research is related to the composition of the sample. Although the researchers attempted to balance the number of participants who have short-term versus one-year overseas experience, the imbalance of countries visited by the youths remain. In particular, those who went to the US comprise half of the participants, and the results fail to indicate whether people who visited the US portrayed a similar pattern of their experiences amongst themselves. This imbalance may have some impact on the overall result, especially on the ability to distinguish patterns according to the different cultural dimension of visited countries. Therefore, the inclusion of other overseas destinations with different cultural distance could provide additional findings and insight into the RCS study.psychoemotional indicators and behavioural and cognitive indicators to measure the returnee’s adaptation [Lastly, a general limitation of the RCS studies lies in the mixed use of aptation , therebyaptation , which cThe 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.


Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the attention mechanism, multiple feature layers with obvious target features are fused to achieve preliminary MA detection. Finally, the spatial relationships between MAs and blood vessels are utilized to perform a secondary screening of the preliminary test results to obtain the final MA detection results. We evaluated the method on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The results show that our method effectively improves the average accuracy and sensitivity of MA detection. Automated medical data processing is a current trend in bioinformatics analysis that is beneficial for disease detection and diagnosis. Diabetic retinopathy (DR) is one of the most serious complications of diabetes mellitus and is a major cause of blindness worldwide ,2. No apThus, similar to other automated disease detection applications ,4, smallMany scholars have conducted research on common object detection, including small target recognition. However, few researchers have used deep learning to detect MAs in fundus images. The current object detection algorithms cannot effectively use the microtarget features extracted in the feature layer; thus, they are unsuitable for use in detecting MAs.Motivated by the above issues, this paper proposes a novel small object detection method based on a multilayer attention mechanism and spatial confidence. First, the multilayer attention mechanism effectively utilizes useful information for small object detection in feature maps extracted from preprocessed images, causing more attention to be paid to areas in which such objects may appear in the feature maps. The output of this process forms the preliminary detection results. Then, the reliability of the preliminary test results is judged based on spatial confidence discrimination, and false positive objects are screened out to improve the object detection accuracy.This paper proposes a fundus image quality equalization method for preprocessing and slicing fundus images; then, based on the proposed method, we construct the fundus dataset IDRiD_VOC.This paper proposes a feature fusion algorithm based on a multilayer attention mechanism that includes both feature layer and channel fusion. Using this algorithm, the feature layer with obvious small target features can be selected more accurately, and the preliminary detection of small target can be realized.This paper proposes a confidence discrimination method based on spatial positional relationships, including target and blood vessel distance calculations, target confidence scores, and a voting mechanism. We use this method to perform a secondary screening of the small target results of the preliminary detection process. Our confidence discrimination method improves the detection sensitivity while ensuring sufficient detection accuracy.The contributions of this paper are as follows:The rest of this article is organized as follows. In this study, the microaneurysm part of the IDRiD dataset  was usedThe image quality equalization method described in https://github.com/tzutalin/labelImg). The final dataset includes 3583 image blocks, corresponding XML annotation files, and related texts (used to record which images belong to the training and test sets). Specifically, the training set includes 3200 patches, and the test set contains 383 patches. Henceforth, this dataset is simply referred to as IDRiD_VOC.Using the Pascal VOC dataset format, a dataset of eye fundus images was produced using the LabelImg tool  algorithm is applied to the green channel to enhance the contrast of the fundus image. At the same time, the green-channel image is segmented using the Otsu threshold segmentation algorithm, which divides the image into foreground and background. Finally, the images obtained by using the CLAHE algorithm and the Otsu algorithm are combined to extract the RoI of the fundus image. In A model diagram of our proposed feature fusion method based on a multilevel attention mechanism is shown in The receptive field (RF) refers to the specific features that are being observed in the input space. The value of the RF represents the range of the observable area in the original image. The larger this value is, the more global features the RF may contain. The smaller the value is, the more local features it may contain. ResNet-101 is used as our basic feature extraction network. Because different ResNet-101 layers have different RFs, it is necessary to select appropriate feature layers to obtain appropriate RFs. To illustrate the significance of the RF for detection, consider a traditional five-level CNN as an example: the conv1 RF is too small, capturing little semantic information about potential targets, and using this feature map would increase the number of calculations, whereas the RFs of the conv2 to conv5 features can better cover targets of various sizes, making these feature layers more suitable to use for the task at hand.By analysing the experimental dataset and data collected from hospitals, we found that the diameters of MAs are mainly distributed between 10 and 40 pixels. The distribution details are given in i denotes the feature level, meaning that As in many other medical data analysis applications ,60,61, tTo enhance the useful information for small target detection that is contained among the different channels, this paper also introduces an attention mechanism for channel-wise feature fusion. Global average pooling is applied to The pooling result function , W1∈Rc·cTo optimize the small object detection model, the loss function is defined to consist of two terms: a detection loss and an attention loss:The object detection loss function further consists of two main terms: one for correcting the predicted bounding box location and one for correcting the object score in the predicted box. The specific formula is as follows:X denotes the predicted score map, and In the multilayer feature fusion attention loss function, by comparing the predicted score map with the ground-truth object map, we can determine which pixels are more likely to include objects and thus require more attention; then, the weights can be further adjusted:Inspired by ,64, the The first step is to calculate the distance between each predicted bounding box and the nearest blood vessel in the preliminary detection results. The distance calculation algorithm is presented in Algorithm 1.Algorithm 1: Distance Calculation Algorithm.Input: Vascular segmentation map I; Candidate object set Output: Candidate object and vascular distance set D;1:Initialize distance set 2:Judge whether blood vessels appear in the region 3:D and reset If yes, add the distance 4:Otherwise, set 5:Once the distance between each predicted bounding box and the corresponding blood vessel has been obtained, a majority voting mechanism is introduced in this paper for screening the preliminary detection results. The confidence score of each predicted bounding box is calculated according to the following formula, which represents the probability that the corresponding object is an MA. Finally, according to the category probability score and the confidence score of each predicted box, the voting mechanism is used to screen out some false positive objects that fall either on or at too far a distance from blood vessels to improve the detection accuracy. The majority voting function is formulated as follows:This function calculates a weighted average of the category probability score from the preliminary detection of the candidate MA, denoted by The experimental design are as follows. In the experiments, we first compared existing object detection algorithms with the attention-based feature fusion method proposed in this paper and then compared the fusion results of schemes involving different layers based on the attention mechanism. Then, the confidence of the preliminary detection results based on the proposed feature fusion method was judged, and the validity of the confidence degree was assessed based on the accuracy of the detection results. Finally, we compared our method with other MA detection methods.The parameters related to the experiments are listed in To comprehensively evaluate the effectiveness of the proposed method, the following metrics were adopted: precision, recall (sensitivity), average precision, and F-measure. Before the above evaluation indicators can be defined, several related concepts must be introduced: the number of true positives (TP) is the number of positive samples that are correctly identified as positive samples, the number of true negatives (TN) is the number of negative samples that are correctly identified as negative samples, the number of false positives (FP) is the number of negative samples misidentified as positive samples, and the number of false negatives (FN) is the number of positive samples misidentified as negative samples. The Intersection-over-Union (IoU) reflects the degree of coincidence between the detection result of the model and the original ground-truth frame, and it is calculated as follows:Among the specific evaluation indicators, the precision refers to the proportion of positive samples among the detected results and is calculated as follows:The recall rate (sensitivity) is the proportion of positive samples detected among all positive samples and is calculated as follows:The average precision (AP) is the area under the precision–recall curve. In this paper, the 11-point calculation method used in the Pascal VOC2007 challenge is applied to calculate the AP. The precision is measured at 11 different points according to the recall interval, and the calculation formula is as follows:The F-measure is a comprehensive evaluation index that comprehensively considers the discrepancy between precision and recall; specifically, it is the weighted harmonic average of the precision and recall. This paper mainly uses the F1 score, which is calculated as follows:In this paper, three object detection methods, namely, SSD, Faster R-CNN and AttractioNet, are used as the base algorithms for comparison. SSD is a regression-based target detection algorithm, while Faster R-CNN and AttractioNet are based on region-nomination target detection algorithms. By comparing SSD with Faster R-CNN and AttractioNet, the adaptability of these two types of detection algorithms to fundus images can be verified. By comparing Faster R-CNN with AttractioNet, it is possible to verify the adaptability of different region nomination algorithms  to fundus images.The specific experimental comparison design is as follows. First, SSD, Faster R-CNN and AttractioNet were compared with our feature fusion method (Ours-1) based on the attention mechanism proposed in this paper. Then, the preliminary detection results obtained with the proposed attention-based feature fusion method were compared with the further results obtained with the spatial confidence discrimination method (Ours-2) proposed in this paper.All experiments were conducted using the IDRiD_VOC dataset described in For the experiment with the proposed feature fusion method based on the multilayer attention mechanism, For the experiment conducted to test the confidence discrimination method based on spatial confidence proposed in this paper, To illustrate the performance of our methods, the results of various object detection methods are compared.In In Selected detection results from the experiment are shown in Based on the small object detection experiments reported in this paper, As shown in Automated MA detection in fundus images can effectively ease the demand on medical resources. A novel deep learning framework for MA detection is proposed in this paper. This framework is based on an attention mechanism and on the relative spatial distribution of blood vessels and MAs. Shallow-layer network features related to small target detection receive special focus due to our attention module, leading to better performance compared with competing methods. Afterwards, a spatial voting method is applied to further improve the detection results. The experiments conducted in this study confirm the performance of the proposed method based on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The proposed method achieves better detection performance than the other tested methods and constitutes a valid approach for MA detection.The fundus images considered in this study were captured by only one type of fundus camera. Different cameras may introduce new features or produce images with different layouts. Therefore, future studies should include more images captured by different types of fundus cameras to improve the generalization ability of the proposed model.