This paper focuses on developing an anti-velocity jamming strategy that enhances the ability of a pulse-Doppler (PD) radar to detect moving targets in the presence of translational and/or micro motion velocity jamming generated by the digital radio frequency memory (DRFM) repeat jammers. The strategy adopts random pulse initial phase (RPIP) pulses as its transmitted signal and thus gets DRFM jammers not adaptable to the randomness of initial phase of the transmitted pulses in the pulse repetition interval (PRI) domain. The difference between the true target echo and the false target jamming signal at each PRI is then utilized to recognize the true and false target signals. In particular, an entropy based multi-channel processing scheme is designed to extract the information of the received signal without the assumption that true and false targets must be both included within one coherent processing interval (CPI). Information such as the component of the received signal  or the operating manner of DRFM repeat jammer can be gained (if jamming exists). Meanwhile, we solve the false target recognition problem under sparse theory frame and our previous work named the short-time sparse recovery (STSR) algorithm is introduced to recover the motion parameters of the true and/or false targets in the time-frequency domain. It should be pointed out that both the translational false target jamming and micro motion target jamming can be recognized in our strategy. The performance of the proposed strategy is compared with the correlated processing (CP) method used by most extant strategies. It is shown that the proposed strategy can successfully recognize the existence of true and/or false targets and keep its power in recovering corresponding motion parameters even when the jamming environment is strong. Pulse-Doppler (PD) radars are radar systems that utilize moving target detection (MTD) or moving target indication (MTI) techniques to detect targets in the midst of noise, clutter and jamming ,2,3. EssMeanwhile, the prominent usage of these radars in the military has directly contributed to the rapid development of the corresponding jamming techniques, namely velocity deception jamming (VDJ), in electronic countermeasures (ECM) fields. Especially with the development and maturity of digital radio frequency memory (DRFM) techniques, the DRFM-based jammer, also called DRFM repeat jammer ,10,11,12Countering DRFM-based velocity deception jamming has been one of the hottest topics in the electronic counter-countermeasures (ECCM) field. Researchers solve the problem by proposing different strategies based on pulse diversity ,10,16,17Actually, we have successfully proposed a sparse-based method to counter TFT ,27 and MIn this paper, a more powerful sparse-driven anti-velocity deception jamming strategy is proposed based on our previous work. The random pulse initial phase (RPIP) signal is adopted as the transmitted signal by the radar. The proposed strategy can counter both TFT and MMFT jamming generated by the DRFM repeat jammer. Moreover, the components of the received signal will be automatically recognized and the motion parameters of true target and/or false target will be recovered in the time-frequency domain. Firstly, an entropy-based multi-channel processing scheme is designed to analyze the received signal about its components. Meanwhile, the information about the DRFM repeat jammer operating manner will be extracted if the jamming exists. Then, the corresponding dictionaries for the true and false targets will be built based on the information pre-known (for true target) and/or extracted . At last, the motion parameters will be recovered in the time-frequency domain by a short-time sparse representation algorithm (STSR), which was proposed in our previous work .The paper is organized as follows. In Different from pulses with constant initial phase (CPIP), RPIP signals belong to random modulated signals, whose phases vary at each PRI. M, T, mth pulse, which is assumed to follow a certain distribution and statistically independent of that of other pulses.Now, we assume that the radar system transmits a RPIP signal, as is shown in P scattering centers in total and they are all making micro motion in addition to the bulk motion of P scattering centers are moving in the same range gate. Then, the echo returned from mth PRI can be expressed as Equation  to protect the target itself. To this end, the jammer needs to conduct the following steps to generate a jamming signal that can be coherent to the transmitted signal of the radar and form the false velocity targets after being processed. Firstly, the transmitted pulse nsmitted . Based ohe radar . Concretmth PRI of radar can be expressed as Equation  target, i.e., i is named the lag information in this paper and it should be pointed that i is of paramount importance for recognizing the jamming pulses, which will be explained further later. The For the receiver of radar, the DRFM repeat jammer generates false velocity targets (denoted as Equation  after beEquation (5)tqd=2We have formulated the two kinds of signals that the radar will receive under the velocity jamming scenarios. It is difficult to distinguish the target echo (denoted as quations  and 4)..sr) and For most extant antivelocity jamming methods, fundamental assumptions are that the existence of the jamming is pre-known or the jamming and the true target echoes are always being received in the same CPI. Obviously, these are not always the cases in real world scenarios. The first requirement for an ECCM scheme should be judging the component of the receiving signal sHl=sThen, the Shannon information entropy Equation :(7)Hl=−∑Equation (8)P using an Intel CPU 1.6 GHz processor with 8 GB of memory . The convex optimization problems are solved by employing the SolveLasso function in cvx ,32. The The simulation scenario is given here, which is demonstrated in on forms ,7, here Here, the validity of our proposed method under different motion cases with regard to recognizing the target and separating the true motion target and false motion target is tested. It should be noted that the signal noise ratio (SNR) is 20 dB and the jamming signal ratio (JSR) is 7 dB. Firstly, we test its performance on the cases that only true motion targets or false motion targets are included. We consider that the motion of the true and false target can be either UALM or micro motion. The parameters are set as shown in Then, we consider the most common cases that the target echo and jamming signal exist simultaneously, putting emphasis on testing the proposed strategy’s performance of separating the two signals and recovering their motion parameters. We take the motion of the target into full consideration, which includes ULM (Case 5), UALM Case 6), ULM with rotation Case 7), and UAML with rotation (Case 8). The corresponding parameter settings are shown in , ULM wit, and UAMDue to the fact that most extant VDJ recognition methods are mainly based on CP theory, it is necessary to compare our proposed strategy with CP-based methods to show the superiority of our proposed method. However, considering the fact that most extant CP-based methods have no time-varying property, we modify the CP method slightly  according to the basic idea of STSR to get it sensitive to the time. As we have proven the correctness of the proposed strategy in different cases, we will only compare the performance of two methods under the same parameter settings of Case 8 for the sake of brevity. For a better comparison, we perform the simulations in different jamming intensity, that is, JSR=−2 dB, 0 dB, 4 dB. The corresponding results are illustrated in When the target is more intense than the jamming signal, the CP-based method can only recover the true signal (as shown in We study the problem of moving targets detection for PD radar in the presence of a translation/micro motion velocity false target. We propose an anti-velocity strategy based on sparse theory that can efficiently recognize the components of the received signal, recognize the false velocity target jamming if it exists, and accurately recover the motion parameters of true and false targets. Concretely, RPIP signal is transmitted by the radar and the difference between the target echo and jamming signal in phase domain is utilized. The strategy detects the components of the received signal by a multi-channel processing method based entropy and recovers the motion parameters in a time-frequency domain with the help of our previous work STSR. Compared with most extant measures, the key advantages of the proposed strategy are, firstly, not only the translational false target, but also the micro motion false target, which is a quite new trend in VDJ fields, are taken into account. Secondly, unlike current methods, assuming that the jamming and target echo exist simultaneously in the received signal, our approach considers more possibilities, namely only the target echo or jamming exists, or both of them are included. These possibilities are integrated into a unified framework and can be automatically detected and responded to by our strategy. Finally, we estimate the target parameters in the time-frequency domain based on sparse recovery theory and the recovery results are more accurate with a lower noise side-lobe base. Experiments show the effectiveness of our strategy and its superiority over the state of the art. It should be pointed out that, at present, our strategy is still in the simulation analysis and prototype testing stage. Future work includes real conditions verification and improvement of the prototype.


Many studies have yielded valuable knowledge on the early visual system but it is biased since the studies have focused on terrestrial mammals alone. Here, to better account for visual systems in different environments and animal classes, we studied the structure of early visual processing in the archerfish which harnesses its extreme visual ability to hunt by shooting water jets at prey hanging on vegetation above the water. Thus, the archerfish provides a unique opportunity to study visual processing in a vertebrate which is an expert vision-guided predator with a very different brain structure than mammals. The receptive field structures in the archerfish (both sexes) optic tectum, the main visual processing region in the fish brain, were measured and linear non-linear cascades were used to analyze their properties. The findings indicate that the spatial receptive field structures lie on a continuum between circular and elliptical shapes. In addition, the cells' functional properties display a richness of response characteristics, since many cells could be captured by more than a single linear filter. Finally, the non-linear response functions that link linear filters and neuronal responses were found to be similar to the non-linear functions of models that describe terrestrial mammalian single cell activity. Overall our results help to better understand the early visual processing system across vertebrates. One of the key unresolved questions in neuroscience is how natural scene statistics influence the information flow in the early visual system. In mammals, influential theoretical frameworks have demonstrated that under simple assumptions that require the representation of terrestrial natural scene statistics to be efficient Barlow, , a set oTo address these issues, we used the archerfish as an animal model since it is an expert visual predator with a visual system that can process both underwater and land habitats. The archerfish hunts terrestrial insects above the water's surface by shooting powerful, accurate water jets in their direction that cause them to fall into the water Lüling, . Thus, tThe central visual processing unit in the fish brain is the optic tectum  at a concentration of 400 mg per liter of tank water was added to the bath. After the fish lost its buoyancy balance and flipped onto its back, it was placed in a restraining fish holder device and connected to a respiratory system containing a lower concentration of MS-222 (100 mg per liter of tank water and 200 mg of NaHCO3 per liter of tank water). During surgery the gills were continuously watered by a tube inserted into the fish's mouth to avoid possible respiratory failure from exposure to MS-222. Lidocaine  was applied to the scales above the brain region. Using a scalpel, the fish scales, skin and fatty tissue were removed, and Lidocaine was applied again on the wound as local anesthesia. At this stage 10–15 μl of the non-depolarizing muscle relaxant gallamine triethiodide  was injected into the spine toward the tail. Then an opening in the skull above the optic tectum was made using a dental drill and the optic tectum was exposed. The optic tectum was covered with a thin layer of agarose to prevent dehydration and contamination. To reduce motion during experiment due to respiration, the skull was stabilized by attaching its frontal side with dental cement  to the water tank. During recording, anesthesia was removed by replacing the water of the respiratory system with fresh brackish water. We let the water tank to fill until the water level was up to 0.5 cm above eye level  at a concentration of 200 mg per liter of tank water. To control the pH level due to MS-222 acidity, NaHCOl Figure . We keptMΩ) mounted on a calibrated manipulator . The electrode was inserted vertically to the optic tectum. The recordings were performed from all horizontal parts of the SWGZ layer  and filtered  by an amplifier  and then recorded using an in-house LabView program. The average duration of a typical recording session was 10 h, and we were able to hold single units up to 90 min.Extracellular action potentials were recorded with a sampling rate of 20 kHz by a single electrode (2–4 While illuminating the fish's eye with an On-Off stimulus (1 s of white screen followed by 1 s of black screen in a repeating loop), we lowered the electrode until action potentials were detected. Then we gently tuned the electrode position to receive the strongest signal. To detect the limits of the cell's receptive field on the monitor, we manually moved a white bar across the screen in different orientations and directions until a strong reaction occurred; this line was marked as one of the edges of the receptive field. In a similar way, the bar was moved in different directions to determine the other edges of the receptive field. This method served to mark the receptive field boundaries to position the stimuli.Two interleaved types of stimuli, Random Checkerboard and Gaussian Full Field Flash, were presented on a monitor  at a refresh rate of 60 Hz Figures . Each miCheckerboard checkers randomly flickered white or black Figure . The ranThis stimulus was composed of full field flashes taken from a gray scale Gaussian distribution Figure . Since tSpike sorting of single units was conducted offline using an in-house Matlab program. First, the signal was filtered with a band-pass filter (300–5000 Hz). Putative spikes were defined as events where the filtered signal crossed a threshold of 3.5 times the standard deviation of the signal. For each peak, 1.5 ms of signal before and after were preserved . Second, using a graphical user interface, events that did not have the shape of a spike or had impulse intervals shorter than an absolute refractory period were removed. The spikes were then clustered into one or more groups based on the spike's amplitude and width. Typically we identified one neuron per recording.RF of the cells were obtained by calculating the spatial spike-triggered average (STA) at seven time steps (16.7 ms latency each) before the spike. To analyze the spatiotemporal profile of the receptive fields, two spatial dimensional Gaussian and Gabor functions were fitted to the data under the assumption that it was a separable receptive field:The spatiotemporal receptive fields RFs and RFt(τ) capture the spatial and temporal components of the receptive field respectively. Prior to the fit, the original coordinate system  was translated by  and rotated by θ′ to align the receptive field's major axis with the vertical axis:Where Then, each receptive field was fitted with a family of spatial Gaussian and Gabor functions multiplied by a temporal Gaussian function:To interpret the neural response to the Gaussian full field flash stimulus, a linear non-linear cascade was used which modeled the cellular response by first applying linear filters to the stimulus and then passing the result through a static non-linearity. Specifically, the model is given by:fi is a set of linear filters, s is the stimulus and G is the static non-linearity.where The linear filter set was obtained by analyzing the statistics of the ensemble of the stimulus before the spike. Specifically, we used STA and spike-triggered covariance (STC) analysis by first calculating the STC matrix:where:Nspikes denotes the number of spikes and s(tn) is the stimulus presented over some fixed time interval preceding the n-th spike. Then, the eigenvectors of the STC matrix were calculated and together with the STA were used as linear filters. Generally, only the STA and/or the eigenvectors with the highest or lowest corresponding eigenvalues associated with excitatory and inhibitory dimensions were found to be part of a significant linear non-linear model. To determine whether a filter was a part of a significant model or not, the model performance was compared to the chance level. To obtain the chance level, first a set of linear non-linear models based on the intermediate eigenvectors (eigenvectors 5–10), as associated with insignificant models, was generated. Then the correlation coefficients were calculated between these models and the true neuronal firing patterns. Last the critical threshold was set as the mean plus five standard deviations of the resulting correlation coefficients of the population.n = 42 cells.To reveal the space-time structure of the receptive fields in the archerfish optic tectum, neurons localized in the superficial layers of the optic tectum were recorded using a single extracellular electrode. Each cell was stimulated by a random checkerboard stimulus  and the spatiotemporal spike-triggered average (STA) was calculated, where the maximum absolute value over space and time was normalized to one Figures . In someThe majority of visual receptive fields of the cells in the optic tectum are characterized by two general features. The first is the eccentricity of the spatial profile of the receptive field. Specifically, the receptive field shapes ranged from almost circular Figures  to slighp = 0.73, permutation test). Defining the size as SRF = σx · σy, both groups had similar sizes .The second feature is related to the differences in the temporal profile of the receptive fields, which fell into two broad cell groups. One group could be characterized by its monophasic profile with an OFF time lobe alone Figures . The othTo analyze the spatiotemporal structure of the receptive fields and the eccentricity in particular, the data were fitted with three different spatial Gaussian and four different spatial Gabor functions multiplied by temporal Gaussian functions, and the parameters of the best fit were used . The results showed that generally the Gaussian models outperformed the Gabor based models (37 out of 42 cells).Then the best fitted model was used to quantify the eccentricity distribution by calculating the ratio of the major axis to the minor axis of the receptive fields Figure . The ratTo better understand the information processing by archerfish optic tectum cells, the possibility that multiple filters contribute to cellular activity was explored. Random checkerboard stimulus has the disadvantage of large number stimulus dimensions, which makes it difficult to estimate additional linear filters beyond the STA. To overcome this difficulty, a full field flash with Gaussian statistics was used since it is a function of time alone and hence is amenable to further analysis.It was assumed that each cell could be described as a linear non-linear Poisson model where a set of linear filters are applied to the stimulus and can be combined to obtain the neuronal rate by an instantaneous non-linearity  model to the neuronal response.To determine the relevant dimensions for each cell, the success of a single filter model was quantified by calculating the Pearson correlation coefficient  had one of three basic shapes: (1) An energy model shape; i.e., firing rate functions that increased monotonically with the magnitude of the generator Figures , 8A,B, , (3) A hn = 44) and the minority had a half-wave rectified  shape. This contrasted with the inhibitory filters which mostly had a hill shape  and fewer had a half-wave rectified shape .The non-linear projections obtained from the excitatory filters were dominated by the energy function shape  and for complex cells, 30% were direction selective and 70% direction agnostic (n = 17). These insights thus broaden our knowledge of the optic tectum's functionality in fish.Since the optic tectum is the main visual processing unit in fish  where a broad, thorough investigation of the cellular receptive fields structures has been conducted on a variety of terrestrial mammals such as the monkey Ringach, , cat Hu,b, rat , mouse , and ferSupport for similar functionality of the two brain regions comes from Zhaoping's comparison of the optic tecta of non-mammalian vertebrates to the visual cortex of mammals, which suggested that both create a saliency map from visual input (Zhaoping, One longstanding tradition is to divide the primary visual cortex of terrestrial mammals into simple and complex cells. These have been reported to exist in many mammals such as the monkey (Hubel and Wiesel, The distinction between simple and complex cells is largely based on the non-linear function that links the linear filter outputs to the firing rate. The non-linearity of simple cells has a half-wave rectified shape whereas the non-linearity of complex cells has an energy function shape. This served as the criterion in this study as well. Even though simple and complex cells have been thoroughly investigated and characterized in terrestrial mammals, this study is the first to have observed them in other vertebrates.Specifically, the results showed that the three, non-linear energy, half-wave rectified and hill function types found in terrestrial mammals (Rust et al., All the above suggests that even though the optic tectum is homologous to the superior colliculus of mammals, it might be functionally analogous to both the superior colliculus and the primary visual cortex. In other words, since the optic tectum of non-mammalian vertebrates is the main visual center and one of the largest components of its brain, it implements a wide variety of functions that exist in terrestrial mammals' basic visual brain regions.The traditional division into simple and complex cells in the primary visual cortex has been extended to other sensory modalities. For example, somatosensory simple and complex cells have been recorded from the barrel cortex when responding to spatiotemporal patterns of stimuli applied to rat whiskers (Estebanez et al., AR: designed study, performed experiments, analyzed data, and wrote the manuscript; MB-T: designed study, performed experiments, analyzed data, and revised the manuscript; RS: designed study, analyzed data, and wrote 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 detection of dipole-like sources, such as unexploded ordnances (UXO) and other metallic objects, based on a magnetic gradiometer system, has been increasingly applied in recent years. In this paper, a novel dipole-like source detection algorithm, based on eigenvector analysis with magnetic gradient tensor data interpretation is presented. Firstly, the theoretical basis of the eigenvector decomposition of magnetic gradient tensor is analyzed. Then, a detection algorithm is proposed by using the properties of the tensor eigenvector decomposition to locate dipole-like magnetic sources. The algorithm can automatically detect magnetic dipole-like sources without estimating the magnetic moment direction. It performs well for locating weak, anomalous dipole-like sources in air-borne magnetic data through quantitative interpretation. The effectiveness of the proposed algorithm has been demonstrated in the designed synthetic experiment. Finally, an air-borne magnetic field data taken at high altitude with exact source position information is used to validate the practicality of the proposed algorithm. All of the experiments prove that the proposed algorithm is suitable for magnetic dipole-like source detecting and air-borne magnetic gradiometer data interpretation. An estimated million acres of land throughout the world are contaminated with various types of unexploded ordnances (UXO), landmines, and other steel objects . To cleaRecent advances in magnetic gradiometry have significantly increased the signal to noise ratio (SNR) of magnetic gradient tensor (MGT) data. Magnetic gradiometry has been increasingly used in wide area assessments to rapidly locate potential UXO contamination . SchneidIn order to expand the magnetic gradiometry survey over large areas and over areas that are unsuitable for deploying ground-based systems, airborne magnetic gradiometry systems have been developed. Airborne magnetometer surveys can rapidly screen both surface and subsurface steel objects without being affected by the geological background noise. Billings and Wright deployed a low-level helicopter magnetometer survey and proved its ability to perform wide-area UXO assessment . Doll etIn recent years, eigenvalue analysis has become one of the most popular methods applied to geophysical data processing. Fakiris et al. presented an automated target detection approach with Principal Component Analysis (PCA) for marine magnetic data interpretation . Zou and2, SI Unit) in Equation (1) [r and ri denote observation and source positions, respectively. Γ can be written as shown in Equation (2).Γ can be diagonalized as Γ can be projected to a new form coordinate system with the diagonalized procedure. The rotation matrix can be expressed as Equation (3):x-, y-, and z-axis, respectively. After decomposition, the non-diagonal elements are much smaller than the diagonal elements and the magnetic dipole direction information is extracted to the rotation matrix The magnetic gradients present the spatial rates of change of the magnetic field tion (1) .(1)B=−Cirection . Γ can bents Φij . Li sugg=0, ψ′=0 . z-axis after eigenvector decomposition. If the angle In this paper, we propose a magnetic dipole-like location method using eigenvector analysis. According to Equation (3), the eigenvector rotates angle According to this deduction, it is possible to locate magnetic dipole-like sources by using tensor eigenvector decomposition. To make the value of the eigenvector inversely proportional to the position of the magnetic dipole, the direction of eigenvector Theoretically, the eigenvector A small angle Although, theoretically, the noise in full tensor data will not present a local peak value in MDTA, a high-level noise and low quality data will create false alarms in the MDTA map in practice. Therefore, a threshold filter In this section, three synthetic experiments were designed to validate the proposed algorithm. The performance of the proposed algorithm with different dipole moment directions and multiple-source scenarios are discussed in detail.Firstly, a synthetic model was designed with nine magnetic dipoles that are buried in different depths, as shown in 2, and the declination and inclination of the dipoles magnetization vector were set as The moment of the dipoles was 20 Amasmussen . These mFor the reason of the symmetry of tensor components, we only draw five independent tensors , which includes the East Poplar oil field on the Fort Peck Indian Reservation to assess the ground-water quality. The magnetic data were acquired using the Geometrics G-822 magnetometer, which was mounted on the SkyTEM 301 airborne electromagnetic system. The raw magnetic data were corrected for the diurnal drift, removing the International Geomagnetic Reference Field (IGRF). The reduced-to-pole (RTP) magnetic field was calculated according to the inclination and the declination parameters that were provided in the raw file .The flight altitude was 850 m, and the observation distance was much larger than the size of the metallic oil and water/brine wells, so that the wells can be considered as the magnetic dipoles in this application. The anomalous caused by these dipole-like sources are weak. While the locations of the well targets have been well marked with exact ground prior-information, as shown in The survey area lies in the Williston Basin, which is a large structural basin with an area of 800 × 500 km. The sedimentation of the Williston Basin formed mostly in the Paleozoic and early Mesozoic eras, and ceased by the Cretaceous era. The thickness of the entire sedimentary section is about three kilometers. Although there is a subsidiary domal structure in the study area, named the Poplar Dome, the geological framework of the study area in the East Poplar oil field is relatively clear and simple .The corresponding gradient tensor maps of this data were calculated by the formulae suggested by Pedersen and Rasmussen . The MDTThis magnetic field data was also analyzed with other UXO detection algorithms, as shown in Both the NSS and analytic signal methods successfully located the primary oil wells in the range, as shown in This data contains high-level noise, and it was further amplified in the calculation of tensor components. However, this experiment shows that MDTA is not obviously influenced by the noise and error. Actually, we believe that real field data acquired by a full tensor magnetic gradiometry system will provide better results with the proposed algorithm. A common problem with air-borne magnetic surveys is that the anomalies are weak when the distance between targets and observation point is large. The anomalies produced by a magnetic dipole-like source decay with distance cubed inversely. Like the field data in this paper, the amplitude of a weak anomaly may be confused with observation noise and cannot be directly extracted with traditional detection methods. As analyzed in There were some reasons for selecting this magnetic survey data in the field experiment section. Firstly, the location of the metallic objects, such as the oil wells and water/brine oils, were well known prior to this study. This information was critical for validating the proposed algorithm in the field data experiment. Secondly, the altitude of this magnetic survey is high, at 850 m, so the data contains some weak anomalous sources that can be used to prove the efficiency of the proposed algorithm. Although the detected sources are not totally buried under the surface, the covered layer do not have an important influence on the air-borne magnetic survey for the detection of the metallic objects.Many methods have been developed to detect metallic targets, most of which consider these objects as magnetic dipoles. Commonly, a magnetic dipole has three degrees of freedom related to the source location, while another three degrees are present in the direction and magnitude of the magnetic dipole. In practice, the uncertainty of the direction of the magnetic moment significantly complicates UXO detection. Unlike the remnant magnetization, it is impossible to estimate the direction of every dipole. So, many methods have been proposed to extract the amplitude of the magnetic gradient data, such as the analytic signal and NNS, which are used independently of the magnetization direction. However, these methods are designed to limit the influence of remanent magnetization in an inversion procedure. They build an envelope of magnetic data for all moment directions. Although the uncertainty of magnetization direction can be removed with these transforms, the positioning information of the sources also will be reduced. In UXO detection applications, the goal of the algorithm is to extract the source location parameters, and the direction of the magnetic dipoles is not our main concern. This study analyzed the physical procedure of tensor eigenvector decomposition and directly calculated the horizontal position of a magnetic dipole source. Compared to the analytic signal and NNS methods, the proposed method performs with higher precision in locating the source, and is not severely influenced by the direction of magnetic moment. In this paper, an attempt to locate metallic objects using eigenvector analysis for full tensor magnetic gradient data is presented. The physical meaning of the magnetic gradient tensor data decomposition has been expressed in detail. We used the characteristics of tensor data decomposition to locate the dipole-like magnetic source. The synthetic experiments show that the proposed method is not severely interfered with by the direction of the magnetic moment, and it can be used as an efficient tool to locate weak anomalous sources. We also validated that the method works well when the sources are close or overlapping each other in the synthetic experiment. In practice, the proposed algorithm can be used for both the total-field-magnetic data and magnetic gradient tensor data, and can also help provide information on the horizontal position of the source in modelling or inversion. Future improvements to this work should consider estimating the depth parameter of sources for UXO data interpretation.


The use of RGB-Depth (RGB-D) sensors for assisting visually impaired people (VIP) has been widely reported as they offer portability, function-diversity and cost-effectiveness. However, polarization cues to assist traversability awareness without precautions against stepping into water areas are weak. In this paper, a polarized RGB-Depth (pRGB-D) framework is proposed to detect traversable area and water hazards simultaneously with polarization-color-depth-attitude information to enhance safety during navigation. The approach has been tested on a pRGB-D dataset, which is built for tuning parameters and evaluating the performance. Moreover, the approach has been integrated into a wearable prototype which generates a stereo sound feedback to guide visually impaired people (VIP) follow the prioritized direction to avoid obstacles and water hazards. Furthermore, a preliminary study with ten blindfolded participants suggests its effectivity and reliability. According to the World Health Organization, 285 million people around the world are estimated to be visually impaired and 39 million of them are blind . It is rThe appearance of RGB-D sensors across a wide range of visual assistance contexts is not only due to their low-power-consumption and cost-efficiency, but also its robustness and good performance as they are able to simultaneously perceive color and depth information at video framerates. Apart from color and depth, polarization and its imaging extend information dimension to be used for material discrimination and target detection because polarization parameters reflect physical properties of materials . MeanwhiLight-coding sensors project near-IR speckles and capture the patterns with a calibrated camera, the structured light pattern being coded with spatial or temporary projecting methods . These sA stereo camera estimates depth maps through stereo matching of images from two lenses. There is a large body of work on stereo matching, some of which are local stereo matching algorithms ,33,34 whIn this paper, we proposed to use a polarized a RGB-D sensor for detecting traversable areas and water hazards by adequately considering polarization effects. We equipped a stereo camera with an attitude angle sensor, a pair of horizontal and vertical polarizers to form a pRGB-D sensor. The point cloud in 3D space relative to the sensor is computed with the depth image and intrinsic parameters. The points are adjusted to the world coordinate system with the attitude angles of the sensor. The traversable area is determined by building a stochastic polar occupancy grid, estimating the ground pose by fitting a B-spline surface and using dynamic programming to segment the ground area and obstacles. The water areas are identified by using polarization effects as the primary cue and the polarization difference image is computed with disparity information from the pRGB-D sensor.This work extends what was presented in ,12,29, wThis paper is organized as follows: in Several related works have been dedicated to traversable area detection, which can be divided into two categories: traversable area detection based on scene segmentation and traversable area detection based on surface normal vector estimation. Dakopoulos presented a comparative survey of wearable obstacle avoidance systems to inform the research community and users about the capabilities of the progress in assistive technology for VIP . As for Several related work have been devoted to water hazard detection by taking multi-cue approaches with color, texture, depth and polarization information. Rankin detected water areas by analyzing brightness and saturation values, and searched for the reflections of ground cover with range information, and also discussed the possibility to specifically use the zero disparity to encode water area . Yao extThe approach is able to detect traversable area and water hazard simultaneously, which prevents colliding into obstacles and stepping in water areas during navigation.The approach takes into account of the attitude angles of the sensor, which enables accurate detection with the continuous movement of the wearable prototype during navigation.The approach combines the detection of water hazard with the detection of traversable area, which decreases the computing cost and reduces water area detection error in scenarios such as sky regions and edges of buildings.Almost all reported works were conducted under ideal scenarios or cause intolerable side effects in navigational assistance for VIP. To the best of our knowledge, no previous work has addressed the detection of water hazards to help VIP to avoid stepping in water areas and no previous work has fully combined the detection of traversable area and the detection of water hazard. Our approach builds on the prior work and makes the breakthrough while the superiority is apparent in the following aspects:In this section, the approach to detect traversable area and water hazard is elaborated in detail. As shown in As shown in For the traversability awareness, it not only refers to long-range ground plane detection but also involves the early warning when a VIP approaches obstacles at close-range. For obstacle detection, the minimum range of 0.5 m, equivalent to the length of an arm, is enough for the avoidance of most obstacles given the normal walking speed of a VIP during navigation.For water hazard detection, the primary cue is the polarization effect as specular reflection on water is known to polarize light . The speSuppose the polarized light from the air comprises energy components However, part of the energy Light in water can be considered as highly unpolarized light with random scattering and internal reflection. With part of the scattered light coming out of the water through refraction, the total light energy component coming out of the water is the summation of reflection and refraction for each polarization component, which can be calculated with Equations (5) and (6) where For illustrative purposes, in If the wearing height of the pRGB-D sensor is Thereby, given the average value of user height, the wearing height rmations . As a reIn order to detect traversable area, a simple and effective technique is presented. First, 3D coordinates of the point cloud are calculated. Given the depth With the help of the attitude sensor, After the point cloud adjustment, occupancy grids are computed with the stereo disparities transferred from the adjusted point cloud coordinates, and the method proposed in  is used As shown with the flowchart in Considering pixel Pixel G is classified as traversable in the process as presented in After that, we warp the right image to the left image to produce a point correspondence for water hazard detection. With the stereo pair, we are able to calculate a brightness difference image which signifies the polarization difference. Considering pixel After traversable area detection and water hazard detection, a farthest traversable line to represent the traversable distances in different directions is proposed to generate the stereo sound interface. In each direction, the farthest distance for navigating is determined by both the traversable area and water hazard detection results. In each direction, we first locate the farthest traversable pixel with the biggest depth value This paper uses a variant of the non-semantic audio interface presented in our previous work  to transAs shown in Divide the farthest traversable line into five sections which correspond to the five different musical instruments. We only use five instruments to make sure that it is easy to understand and would not sound confusing after the familiarization period. Five instruments, including trumpet, piano, gong, violin and xylophone, produce sounds simultaneously.The horizontal field view of the pRGB-D sensor is 86.5°, so each musical instrument corresponds to the traversable line with a range of 17.3°.Each direction of traversable area and water hazard is represented by a musical instrument in the virtual 3D space.For each musical instrument, the bigger the sum of height in the corresponding section of the traversable line, the louder the sound from the instrument. As the relationship shown in For each musical instrument and the corresponding section, the bigger the areas that overlap with the red triangle sections which denote suggested directions to take on navigation, the higher the pitch of the instrument. As the relationship shown in The generation of the stereo sound follows rules below to guide and attract VIP to take the prioritized direction to detour around hazardous regions:In this section, the proposed approach is verified with a series of experiments including tests on a score of indoor and outdoor scenarios, comparisons with other works in the literature and a field test in the real-world environment involving ten voluntary blindfolded users.To compare the performance of traversable area and water hazard detection with respect to other works, the results of several approaches are shown in We removed this kind of error in our work with adequate use of the attitude angles of the pRGB-D sensor. The approach in  set up aIn order to provide a quantitative evaluation of the approach, the performance of the algorithm is thoroughly analyzed by calculating traversable area detection rate (For quantitative analysis we used a sequence of 1285 frames from the pRGB-D dataset , which iThe proposed pipeline effectively combines polarization-color-depth-attitude information and improves the detection performance in indoor and outdoor environments, which is able to provide real-time assistances for VIP to avoid colliding into obstacles or stepping into water areas. However, it still has certain potential limitations. Since the proposed traversable area detection is based on the pRGB-D sensor whose depth information is acquired with stereo matching between left and right RGB images, the approach will have a slight problem when the depth information is defective as shown in To evaluate the ability of our system to help visually impaired users follow a prioritized route without losing their orientation or colliding into obstacles or stepping into water puddles, we conducted a user study in a real-world outdoor space. Before the participants were blindfolded, the stereo sound feedback of the system when wearing a bone-conduction headphone was explained as shown in After the learning and familiarization stage, ten voluntary participants were blindfolded and were required to travel around a building and an empty field without colliding into obstacles or stepping into water areas. As a comparison task, the ten volunteers were divided into two groups randomly and while one group travelled with only traversable area detection, the other group travelled with both traversable area and water hazard detection. To provide the participants with a sense of orientation, the user would get an extra hint to turn at the bends or the road intersections. For a single test, three measures were recorded including the time needed by a participant to walk the route as shown in After the test, the ten participants were asked two simple questions including whether the prototype was easy to wear and whether the stereo sound feedback could be used to find traversable directions. As shown in the questionnaire results , all useMost participants were able to understand the changing process of loudness and pitch, and follow the stereo sound to find the traversable directions and detour around the hazards, while a small group of participants only used the direction of the stereo sound to turn left or turn right to avoid collisions. This demonstrated the usefulness and effectivity of the approach in terms of traversable area detection and stereo sound feedback. In addition, some users provided some advice on adding functions, such as the detection of curbs and negative obstacles. Moreover, the users were optimistic about the pRGB-D system and would like to have a more profound experience.RGB-D sensors are a ubiquitous choice to assist navigation for the visually impaired. However, most solutions are confined to using intensity information whereas the polarization cues are weak. The proposed pRGB-D framework enhances the safety and robustness of navigation by combining traversable area detection and water hazard detection with polarization-color-depth-attitude information. Indoor and outdoor empirical evidences, quantitative analysis of the detection as well as a user study with ten blindfolded participants demonstrated its usefulness and reliability.In the future, we will improve our navigation assistance approach to reach a higher level of perception and offer more independence to visually impaired individuals. Specifically, we look forward to including polarization for ranging and further investigating discovery schemes for the detection of small obstacles such as curbs and transparent objects. Additionally, it is necessary to run a larger study with visually impaired participants to test this approach, and different audio output settings could be compared. The use of portable processors and sensors with larger field of view would also benefit the system in terms of wearable mobility and detectable performance.


The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as “junk” DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years. At first sight, the question “what is a gene?” would seem to elicit a simple answer: genes transmit inherited characteristics and must be the cause of them. However, since the time when the idea of gene was first mooted, the advent of novel Next-Generation Sequencing (NGS) technologies have complicated and expanded this view into an ever-evolving concept Figure .conditions, foundations and determiners which are present in unique, separate and thereby independent ways by which many characteristics of the organisms are specified (…) precisely what we wish to call genes”  immediately suggests a possible copying mechanism for the genetic material.” Overall these discoveries led to the realization that the genetic material is made up of a chain of polynucleotides, called DNA, and to the establishment of the central Dogma, which states that polypeptides are translated from RNA which is transcribed from DNA. Together with the Watson-Crick double helix model, the relation between DNA and polypeptide synthesis provided a mechanistic model of gene and gene activity and inaugurated the molecular biology era.The existence of genetic traits inherited as discrete entities was first intuited by Darwin,  and Men in the mIn eukaryotes, the discovery of the diverse modalities of RNA maturation evolved the awareness of colinear relationship of DNA, RNA, and polypeptides. The existence of alternative splicing, 5' and 3'-ends alternative maturation and RNA editing processes allow a single gene to produce multiple proteins by means of a single act of transcription. That only the minority of the transcribed genes in higher eukaryotes encode for proteins suggests that the genome has a potential which extends beyond the discrete coding loci. The rest of the genome (at least 60%) is transcribed independently of its coding capabilities  regulation  operon of together?” A partial answer came when the International Human Genome Sequencing Consortium, in the framework of the Human Genome Project (HGP) provided a more accurate quantification of the number of genes by publishing the complete human genome sequence  a whole geography of distributions (…) a storybook that's been edited for a couple billion years. And you could take it to bed like A Thousand and One Arabian Nights, and read a different story in the genome every night.” Thus, each cell of the same individual tells a different story, even if the storybook is the same. This observation can draw on the Aristotelian theory of potentiality and actuality. Potentiality represents the genotype, which contains all the information needed to develop the function, while actuality is the motion of the genotype into a phenotype that is the composite of an organism's observable features. The driver of the switch from potentiality to actuality, however, was unknown. In the late nineteenth century, the question of how a fertilized egg can give rise to a complex organism with cells of varied phenotypes was the object of long debates between two main schools of embryologists. The “pre-formationists,” who thought that each cell contains preformed elements that enlarge during development, while the “epigenesists” thought that chemical reactions among soluble components execute the developmental plan    by silencing transcription through the recruitment of several chromatin modifying complexes, including PRC1 and PRC2  databases  and in vitro occurring interactions  the endogenous RNA pull-down, which is based on the purification of the endogenous transcript under native or ultraviolet (UV) cross-linking conditions. In the first method   thed Figure , left, tin vivo are better preserved. However, also in this case, the occurrence of background interactions cannot be excluded  washing conditions  has recently been developed. In iCLIP experiments, occasional transcription arrests are mapped through circular ligation based methods. Since this step is often inefficient, Van Nostrand and collaborators have modified this part with the addition of two different adapters in two separate steps. The 3' RNA adapter is ligated directly to the crosslinked RNA after immunoprecipitation, while the 3′ single-stranded DNA adapter is ligated after reverse transcription to the 3'end of the cDNA , structural  and regulatory  RNAs.LncRNAs represent the most recently discovered class of regulatory RNAs which exert their roles through a variety of mechanisms without being translated into proteins. The interaction with proteins enables RNAs to operate through distinct means and to exert a wide-range of functions across diverse biological processes Figure . One intIn the attempt to find order within the RNA landscape we could define pervasive transcription as a cocktail of disordered sounds that are selected by evolution to be turned into music Figure . This isAC wrote the manuscript and selected the literature. MB proposed the topic, wrote the manuscript and reviewed the text.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.


Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. There is a growing need for autonomous robots in applications such as rescue, disaster assessment, plant engineering, or other tasks that would be a risky or impossible for a human to perform that requires efficient localization techniques for robots. The problem of localizing autonomous robots in unknown environments is typically solved using a low-cost scanning laser or another type of range finder; this approach has seen successful for large- to medium-sized ground robots ,2. UnforRecently, numerous works have appeared to solve the problem of localizing UAVs in an indoor environment. The most popular solution for localizing UAVs is the Simultaneous Localization and Mapping (SLAM) technique, which is widely applied in robotics. Compared with vision-based SLAM systems, laser-based systems provide more accurate indoor maps and models . Based oGenerally, optical range finders perform 2D laser scanning of vehicle surroundings and provide data with high resolution and at high sampling rates. Processing of such data is computationally expensive and requires massive computing resources . In contsplit-and-merge segmentation algorithm [In this paper, a point-to-point scan matching method is used to solve the indoor navigation problem of UAVs. This method does not require the environment to be structured or contain pre-defined features and is much faster than other traditional methods. Moreover, the UAV localization algorithm was designed so that an Intel NUC-based embedded on-board computer is sufficient for computational needs. Further, the altitude of the UAV with respect to the indoor floor is calculated using line segment extraction  with a slgorithm .In addition, a novel approach is presented for the real-time classification of a pipeline in the generated indoor map by integrating the proposed SLAM method. The generated indoor map resulting from the proposed method can be utilized for visualization of the 3D environment, extraction and classification of object information , or for (1)The use of a modified PSM algorithm in orientation estimation and selecting the ideal plane for the scan matching algorithm with application to robust 3D mapping.(2)Presentation of a novel approach for real-time pipeline classification in the generated indoor map using a histogram-based radius estimation.Finally, we show that the proposed SLAM system can be used in indoor mapping and modelling applications, as illustrated in In this section, different scan matching-based SLAM approaches are presented along with a LiDAR and IMU integrated navigation system for indoor UAVs. Also, this section presents a different pipeline classification method for application in plant engineering.Iterative Closest Point (ICP) is an iterative algorithm that aligns two consecutive laser scans without extracting the features, such as lines and planes. Since it does not extract features, ICP is applicable even for unstructured environments where such features do not exist. It is however, slower than other methods because it uses the whole point cloud to find an optimal transformation. A SLAM method based on stereo vision and the ICP algorithm has been described in . A SLAM The probabilistic scan matching (Probabilistic SM) method proposed in  is like The feature-based 2D laser scanning proposed in  estimateA comprehensive UAV indoor navigation system proposed by Wang and Cui in  is basedsplit-and-merge segmentation algorithm [Fei and Kangli  proposedlgorithm  to extraAnother UAV localization method for indoor environments based on a LiDAR sensor, entitled “Autonomous flight in unknown indoor environments”  and propThe LiDAR/IMU integrated navigation solution proposed by Liu and Shifei  is a typThere has been growing demand for the 3D object classification and reconstruction of as-built models from point cloud data. Current object classification methods generally fall into two categories. First, real-time object classification continues to be an active area of research . MethodsA study by Czerniawski et al.  presenteKim et al.  use prioIn this section, a LiDAR and IMU integrated navigation system is proposed. This UAV navigation system includes an existing 2D laser scan matching method for planar localization of UAVs and altitude estimation using a low range LiDAR sensor. Also, modifications made to the scan matching process to overcome altitude angle variation during the flight of a UAV are described in detail.Planar localization of a UAV is achieved using measurements from the horizontally scanning primary LiDAR sensor mounted on the on-board computer. Scan measurements from the LiDAR scanner can be understood as a momentary snapshot of the surrounding environment of the UAV. The point-to-point matching-based Polar Scan Matching (PSM) method  is used As mentioned before, we follow the point-to-point matching based on the PSM approach proposed by Diosi and Kleeman  in ordern range measurements The current scan is described as The scan matching process works as follows: scan pre-processing and scan projection are followed by translation estimation, which is alternated with scan projection and followed by orientation estimation.S is projected with respect to the reference scan acquired at position R such that it reflects how the current scan would look if it were performed from the reference position R. The range bearings of points from position R are calculated using Equations (1) and (2):Prior to matching, spurious measurements and objects that are likely to move are removed from the scan data using a median filter and a scan segmentation filter in order to increase the accuracy and robustness of scan matching. Next, the current scan acquired at position R. The re-sampled range value for each sample bearing using linear interpolation is represented by the notation –(9) in ZYX sequence, and the current scan measurement The normalized quaternion output t and t + 1 be the two successive time instants at which two LiDAR scans are acquired, and let Let Using range measurements Then pose of the LiDAR from the reference and current scanner locations can be represented using the matrix form in Equation (5):Now, after projecting the current scan line with respect to the reference scan, the angular difference The primary LiDAR mounted on the on-board computer on a UAV detects obstacle information in the 2D scanning plane. In the case of ground robots, which always move in a plane, the altitude angle is maintained at an almost constant level. However, it is very difficult to control a UAV at a constant altitude angle during flight in the indoor environment. Therefore, LiDAR measurement information from the UAV platform cannot accurately describe the obstacle distribution in the horizontal plane.This leads to an enormous amount of error in the scan matching process during relative position estimation. Therefore, it is necessary to reduce the influence of altitude angle variation during LiDAR measurement.As shown in The angular variation occurring during flight of the UAV is captured from the roll and pitch angles of the 6DOF IMU integrated with the primary LiDAR. Quaternion output from the IMU sensor are converted to Euler angles using Equations (8)–(10), which use the ZYX sequence.Considering a unit quaternion Each scan measurement from the LiDAR is associated with the Given two 2D laser scans, In the case of invalid plane measurement, the current scan UAV altitude measurement in an indoor environment with a completely flat floor can be obtained directly from the low range finder. However, there are some cases where altitude estimation fails because of disturbances from objects such as tables, chairs, or windowsills in an indoor environment. In order to solve this problem, a floor detection algorithm  is used Using a split-and-merge line extraction algorithm , measureIn order to integrate and filter the 2D position data from scan matching and 1D altitude data from line extraction, we also implemented a linear Kalman filtering algorithm, similar to the work presented in . The bloX coordinate of the 2D position at the kth time interval from the scan matching algorithm.Y coordinate of the 1D position at the kth time interval from the altitude estimation algorithm.Z coordinate of the 2D position at the kth time interval from the scan matching algorithm.x is the state of the system, u is a known input to the system, y is the measured output, w is the process noise, and z is the measurement noise. Vector x contains the present state of the system, which is estimated from given measurements in vector y. Equations (13) and (14) define the linear model of the filter used [ter used .The following sections describe the proposed pipeline classification method adopted for automatic identification of the pipe radius in real-time utilizing ray casting from a UAV position. In the first step, the ray-point cloud intersection using an octree ray casting algorithm  is used p is a point on the line, The first step for pipe radius estimation in real-time is to select a seed point in the point cloud data using an octree ray-casting operation, which finds a single seed point by the ray intersection. lgorithm .A nearest neighbor algorithm will compute the distance to all points in the point cloud to the seed point. It will then sort the points in ascending order of distance and return all points whose distance is less than the defined radius . The nearest neighbor point for the seed point is estimated and shown in ip), the covariance matrix is obtained as:k is the defined number of nearest neighbors, p represents the centroid of the nearest neighbors, jλ is the j-th eigenvalue, and jv is the j-th eigenvector. The estimated normals are shown in The first features extracted from a given dataset are the surface normals. For estimating the seed point normal as well as the normal of the neighboring point, the surface normal estimation method from the Point Cloud Library (PCL) is used . The theAfter estimating the normals, a tangential vector . A matching pair is specified as a tangent vector and a pairwise direction vector. If the result of the dot product of two vectors is within a pre-determined threshold angle, it is specified as a pair matching the corresponding direction. θ in the figure is as follows:A typical angle is calculated for each pair of points estimated in the previous step because the angle of the maximum direction varies according to the distance of pairs within the currently selected ROI. To find the typical angle, the cosine law is utilized, as shown in θ using the length of the sides , as shown in The above Equation (15) calculates the angle ip of the selected ROI. Therefore, the value of cos θ is the calculation of the dot product of surface normals of matching pairs (two points ip) by rotating the tangential vector and finding the maximum dot product result as a maximum angle. By taking a proportion of the pair distance and radius of the current region, the angle result is calibrated using the Point Feature Histogram (PFH) [The distance of pairs is different in the point cloud am (PFH)  and is sAs shown in In order to verify the proposed UAV indoor navigation and its application in real-time pipeline classification, actual flight tests were carried out. The platform used for the work described in this paper comprises a Phantom 3 Advanced Quadcopter from the commercial UAV designer and manufacturer DJI . This quadcopter has a maximum take-off weight of 1 kg and can complete 12~14 minutes of flight time.An Intel NUC D54250WYKH i5 processor was used as the on-board computer. A UTM-30LX EWmodel of a scanning laser range finder from Hokuyo  is used as the primary LiDAR for planar localization of the UAV, which has a 270° scanning range and 30 m coverage distance. Another URG-04LX-UG01 scanning laser range finder was used as the secondary LiDAR for the altitude estimation of the UAV. In additional to the quadcopter battery, a lithium battery with 2000 mA capacity was used to power the LiDAR and on-board computer. A 6DOF wireless inertial measurement (WIMU) device was used to acquire the UAV angular variations, which includes Magnetic, Angular Rate, and Gravity (MARG) sensors. All required computation for the localization algorithm was performed by the on-board computer, and real-time indoor map visualization along with pipeline classification related computations were performed in the host station connected through Wi-Fi communication. The proposed navigation system results are illustrated in The result of the trajectory shown in Another flight test was carried out in an indoor hall in order to analyze the algorithm accuracy in terms of trajectory estimation. The experimental flight of the UAV comprises a moving drone in a reference trajectory, as shown in The mapping system was also evaluated using the navigation approach in order to construct an indoor environment outside the lab corridor. As shown in The pipeline classification algorithm described in The accuracy of the customized scan matching method was compared with other traditional scan matching ICP and PSM methods. The source codes of the PSM and ICP methods were obtained from the authors’ websites . Figure x and tz represent the relative translation along the X-axis and Z-axis between the consecutive laser scan, respectively; Results of intensity-based object tracking are regarded as ground truth. As seen in the results, our method achieved similar results to the ground truth. Computational time in the proposed navigation approach mainly depends on the scan matching and altitude estimation processes, in which time-consuming methods are unacceptable. The orientation estimation process is performed parallel with a separate processing unit that output at 80 Hz frequency is higher than the LiDAR measuring. In order to perform computational time analysis of the scan matching process and altitude estimation, we considered four scan pairs. Point density in each scan pair was 180, 360, 720 and 1080, respectively.As seen in In Another test was conducted to demonstrate the effectiveness of the LiDAR with integrated IMU scan matching method. In this test, the scanning platform was driven along the indoor corridor. The respective results of the IMU integrated LiDAR method were analyzed and compared. The results of error distribution for both methods in a dynamic position are shown in During dynamic position of the scanning platform, abrupt movement of the platform caused false orientation estimation with the LiDAR standalone method. These false measurements were inherent in the LiDAR scan matching method, but were eliminated by using IMU measurements. Hence, when the system was moving, the results of the LiDAR integrated IMU scan matching method were obviously better than those of the LiDAR standalone solution.r) of the proposed approach is lower and the precision rate (Pr) is higher with increasing ROI.The accuracy of the proposed pipeline classification approach is measured by observing five random positions of ray casting on known 30 cm and 25 cm cylinder point cloud data , as showIn this paper, we propose a complete navigation solution for UAVs in an indoor environment by customizing and combining suitable existing algorithms. Also, the scan matching algorithm was extended to use with UAVs by stabilizing the LiDAR measurement plane using an IMU sensor. Innovations have been focusing on improving the algorithm efficiency and system integration with the use of inexpensive and lightweight range sensors. The main advantage of the system is the increased computational efficiency with the limited resources in the on-board computer. The experiment shows that the proposed navigation has significant potential for use in small UAVs in indoor mapping applications.Extending the application area of the proposed navigation system, we proposed a new approach for classification of pipelines generated in an indoor map. The presented method utilizes the ROI and typical angle to estimate and classify the pipe radius. The ROI was enacted for estimating normal and the pairing of points since a wider or smaller ROI results in uncertainty when calculating point cloud features. The methodology was successfully tested on a synthetic pipeline as well as an industrial pipeline for real-world environmental point cloud data sets.This work can continue in several directions. The navigation method can be improved by enhancing the scan matching algorithm with optimal computational complexity. Also, integrating a gyro-stabilized platform for LiDAR will improve the efficiency and robustness of the system by avoiding an ideal plane detection process. Thus, the limitation on the control of a UAV, especially during turns, can be removed completely. Additionally, the classification method can be extended to different indoor environment objects.


Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure. Spike Timing-Dependent Plasticity (STDP) , directly resulting from the presynaptic activity (denoted as Ppre), and local dendritic activity resulting from all activity sources in the local dendrite (denoted as Pd). Secondly, the cross-correlation between presynaptic Ppre and local postsynaptic activity Ppost determines the potential for synaptic efficacy. This cross-correlation reflects synaptic depression due to presynaptic activity if little or none local postsynaptic activity is present. Thirdly, the cross-correlation of the local postsynaptic activity of the synapse Ppost with the dendritic activity Pd, which has been shown to be essential for STDP . The negated causal cross-correlation of presynaptic and postsynaptic activity, Ppre and Ppost, is scaled by the dendritic activity Pd which ensures stability of the synaptic strength. This represents the direct effectiveness of the synapse by relating the presynaptic activity with the filtered postsynaptic response it has generated. Hence, the negated causal cross-correlation causes synaptic depression and this can only be overcome by the potentiation due to a robust postsynaptic response. This response is based on the postsynaptic activity generated by the synapse and the cross-correlation of this activity with the dendritic activity. If sufficiently large postsynaptic activity is present when the synapse is active , the synapse becomes potentiating by overcoming the initial synaptic depression. To avoid unnecessary confusion regarding the biological interpretation of the activity term, it suffices to consider it an abstraction similarly to the postsynaptic potential in functionality.We construct a specific learning rule (dynHebb) based on the comparison between the local postsynaptic activity W in the Synaptic Activity Model (SAM) is described by the following equationsThe change in synaptic weight Wi(t) is the synaptic weight of synapse i, Ppre is the presynaptic activity, Ppost the resulting local postsynaptic activity of the synapse and Pd the local dendritic activity.where Ppre, Ppost, and Pd terms are described by Px inTo encapsulate the contribution of the localized memory that each synapse contributes to the global dynamics, the second term in Equation (1) represents the linear local response of the postsynaptic activity. This term may be considered to provide a linearly scaled direct response to the input, allowing local activity to remain after input has died away. To provide suitable functions for the cross-correlation terms, the synaptic activity is formed by a descriptive decay function, based on the alpha function Koch,  commonlyW is the synaptic strength in case of the Ppre, I is the input, Q is the input variable and P represents the synaptic activity.where α = 1, β = −1, ϕ is in the range . The cross-correlation terms in Equation (1) are therefore causally related, due to the fact that Ppost is produced by the same spiking model as Ppre, but weighted. If the weight is such that the cross-correlation PprePpost is high, this will depress the synapse, unless the post-synaptic cross-correlation term PpostPd, and the local linear Ppost itself can overcome this effect. Similarly, the post-synaptic dendritic activity is causally related to the post-synaptic activities forming the second cross-correlation term. Therefore, the entire dynamic behavior of the synapse is a direct causal function of its input, in relation to its output contribution to the local neuronal activity. The post-synaptic spiking neuron model can then be activated based on the scaled Pd. Scaling is necessary due to the fact that the spiking models are based on specific assumptions for their parameter values. Finally, the weight for the synapse is calculated based on Equations . Note that all equations are first order differential non-linear equations. These can be simulated using standard numerical integration methods, where the exact methodology used is not critical, taking care to minimize numerical error.where Ppre (black) and Ppost (green) and the negative cross-correlation of the two . In Figure Ppost as in Figure Pd (gold), resulting in the cross-correlation of these two activities . In Figure F(t) (red). The synaptic weight Wi(t) is then shown in Figure To clarify the function of the Dynamic Hebbian Learning rule, in Figure F(t)   Figure , and the) Figure  over an Figure 7A of a configuration with two excitatory synapses and one inhibitory synapse. The three synapses are at different distances from the soma, but close enough to each other to influence the local dendritic activity. Synapse A is furthest away, followed by B, and C the nearest to the soma. In subsequent simulations the first synapse from presynaptic neuron A is located on the left branch of the “Y,” the second synapse from presynaptic neuron B is located on the right branch. The third synapse is located on the base of the “Y,” with the location of the soma at the bottom. These synapses will then compete to become the most relevant synapse to cause a postsynaptic activity spike. The synapses will not simply reach the limit of the neuron, due to the relative contribution each makes to the total dendritic, and thus somatic activity. To demonstrate the dynamic nature of the STDP function, we will compare the effects of the different locations of the sole inhibitory synapse. In other words, we make synapse A, B, and C inhibitory in turn, with the other two excitatory.The dynHebb rule can be applied to synapses in different spatial configurations. Commonly, two synapses are targeted on the same postsynaptic compartment −Ppre(t)), and the corresponding amplitude change between the activity events provides the relative change in synaptic strength due to this activity ΔWi(t) = Wi(t) − Wi(t − 1), where the value of Wi is constant between spiking events. The potential effect of cumulative increases due to a residual memory (state) in the synaptic strength at each increase in the phase difference can easily be mitigated by providing sufficient time between input pulses to allow transients to die out. The dynHebb learning rule is not affected by the current state of the synaptic weight itself, as can be seen from the synaptic rule function eWi(t) in Equation (2) slowly decreases the weight, which can be overcome by occasional activity of the synapse. Alternatively, this term can be set to zero, if desired, without compromising the inherent properties of the rule allowing more persistent activity.To calculate the resulting STDP curves, a standard experimental approach is used  and 4.975 Hz (201 ms period), resulting in a beat frequency of 0.025 Hz and a total stimulation period of 40 s. No further dendritic activity exists apart from the activity generated by either of the synapses 1 and 2. Competition can be found in this situation as interacting synaptic strengths over an entire simulation period Figure . The secThe subsequent change in phase difference causes the first synapse to increase in strength and the second synapse to weaken again (at 42 s). Finally, the first synapse will weaken and both synapses return to their equilibrium state. From this simulation, the STDP curve can be determined by calculating the time differences for individual inputs with the postsynaptic response. The resulting STDP curves of both synapses show that the first synapse Figure  blue hasThe dynamic interactions that emerge from the dynHebb learning rule, contribute to more complex behavior of the synapse. Because firing rate and timing as well as correlation have been shown to contribute to cortical plasticity  and the action potential was driven by a frequency of ~10.1 Hz (99 ms). When the simulated action potential has a slightly higher frequency than the two synapses Figure , there aThe dynHebb learning rule employs cross-correlation to combine the input activity with the local postsynaptic dynamics. Synaptic inhibition affects the amount of postsynaptic activity and therefore will influence the STDP curve depending on the timing of the inhibitory input. Additionally, the occurrence of a simultaneous action potential can modify the STDP curve further. To demonstrate these interacting activities, a different configuration has been used. In a single compartment, one excitatory synapse is driven using a fixed frequency. A nearby inhibitory synapse is driven using a different frequency. An optional postsynaptic action potential can also be included, and subsequently, the resulting STDP curves are determined Figure . The excWe first regard the STDP curves of the excitatory synapse without any further postsynaptic activity, such as an action potential, but with inhibition Figures . The preDuring development, due to the changing properties of channel dynamics such as NMDA-channels, the shape of the postsynaptic EPSP changes  and dendritic activities, will also affect the postsynaptic activity of the synapse. This can be determined by examining well known properties of STDP as described below.The apparent change in STDP learning rule induced by the difference in external input timings has also been reported to occur experimentally in CA1 pyramidal neurons where the external input timing at local synapse determines the sign of STDP learning (Kwag and Paulsen, It has been suggested that different STDP windows may be specific for different cell types, such as in the dorsal cochlear nucleus (Tzounopoulos et al., The results on frequency dependence show that the dynHebb correlation learning rule also applies to harmonics. For the input frequencies around 10 Hz, the shape of the STDP curve at ±100 ms (e.g., Figure The existence of heterosynaptic plasticity has been shown in various experiments (Morrison et al., in vivo has been shown to be organized locally which allows the integration of spatially distributed input (Jia et al., Additionally, the dendritic organization of cortical input The frequency and relative timing dependent nature of the contribution of the action potential to STDP has been shown to occur widely (Sjöström et al., These results illustrate the need for synapses to receive global input from the neuron itself to scale the synaptic input and to receive input from other synapses but not to be overwhelmed by the total postsynaptic activity. Interaction between synapses via the postsynaptic activity generated by other synapses and action potentials permits a wide range of possible behaviors of the STDP learning curve. Using local synaptic rules which integrate the information from other sources ensures stability of both the neuron and the network (Marder and Goaillard, The clear effect of the interaction of the synaptic inhibition with the STDP learning rule is that the associated excitatory input in the synapse should be weakened, rather than strengthened, whenever the inhibitory input occurs after the excitatory input. Additionally, the synapse should be strengthened whenever the inhibitory input precedes the excitatory input. Synaptic inhibition, therefore, not only controls the activity level of the neuron, but also directs the type of learning of nearby excitatory synapses in the target neuron. Experimental results of STDP in the striatum demonstrate the existence of a switch from anti-Hebbian to Hebbian type STDP learning curves in the presence of GABA-ergic blockers (Fino et al., We suggest that the functional role of the changing EPSPs is related to the relative unreliability of input in the young neuron. The young network has lower discerning resolution but has better reliability in terms of the correct response due to spike timing. The younger neuron can still be trained even with a high level of input noise. In the mature neuron, the more narrow EPSPs result in STDP learning with higher resolution because of the increased reliability of the input due to the training of the entire network. This mechanism could be seen as a reliable biological mechanism for annealing the network into stable and functional networks with high resolution. A similar phenomenon has been observed in dissociated cortex cultures, where wide bursting profiles are predominant in the early stages of growth, and after 3–4 weeks show a short onset, resulting in increased timing precision of the synchronization between neurons (van Pelt et al., The dynHebb synaptic model can be employed to investigate interactions between locally competing synapses at the dendrite. This type of localized competition is most important for local computation at the dendritic level (olde Scheper, Although the aim of this paper is not to perform an extensive comparison of the various models of STDP, but rather to emphasize the importance of dynamic models, it would be appropriate to mention some cardinal differences between the dynHebb model and selected popular models. Firstly, models like Shouval et al.  and GrauSpecific STDP learning rules are often experimentally determined by experimental protocols (Testa-Silva et al., Obviously, there is no ready made solution that can easily simplify the process, however by assuming a methodology that is more in line with the natural process of synaptic transmission, rather than adhering to a steady-state approach, the likelihood of determining significant results can be greatly increased. The deterministic nature of the dynHebb rule allows the emergence of the small relative contribution of individual learning events without the stochastic background noise that can obscure the event in probability based learning rules. In our simulations, we employ a continuous stimulation protocol that does not require instantaneous updates and highlights the dynamic nature of STDP learning. We can replicate a standard experimental protocol by activating a specific model synapse directly and generate a postsynaptic action potential at the soma. Or, we can activate nearby model synapses to demonstrate the dynamic interaction at the synaptic level of the postsynaptic activity induced by different synapses. This will allow further development of the STDP model using experimental results to guide the specific interpretation of the Hebbian learning.The Dynamic Hebbian Learning model demonstrates that only local autonomous non-linear interactions govern the STDP learning rules. The dynamic interactions at the postsynaptic site modify the default Hebbian STDP curve into many possible experimentally observed forms. More detailed modeling of the constraints that determine the shape of the learning curve, such as local dendritic morphology, networks of neurons, current flow within the dendrite and second messenger signaling, will provide a systematic understanding of the STDP learning mechanism. Awareness of the dynamic cross-correlation principle will greatly facilitate the interpretation of data and may lead to the discovery of novel mechanisms involved in tuning the STDP curve of individual synapses within the network.Furthermore, it has been shown that the self-organizing nature of stable cortical networks itself requires fluctuating synaptic strengths which results in a purely additive STDP mechanism. This is based on observed patterns of synaptic strength in the dynamics of dendritic spines of the rat hippocampus (Zheng et al., Firstly, using the dynHebb rule, further work in dendritic computation and interaction between neurons has become possible. We are particularly interested in the robustness of developing neural networks (van Pelt et al., Model design, implementation and analysis were performed by TVoS. RM, and AvO were involved in the design and interpretation of the results. All authors were involved in improving 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.


Ongoing fluctuations of neuronal activity have long been considered intrinsic noise that introduces unavoidable and unwanted variability into neuronal processing, which the brain eliminates by averaging across population activity (Georgopoulos et al.,  We have recently shown that respiration-locked olfactory bulb activity in awake, head restrained mice causes respiration-locked delta oscillations and gamma power modulations in the somatosensory cortex  respiration causes respiration-locked oscillations that are synchronized across large areas of neocortex at the species-specific respiratory rhythm; (2) that increases in the power of gamma oscillations (40–100 Hz) occur preferably during certain phases of  the respiratory cycle. Both hypotheses are supported by solid experimental results in the somatosensory barrel cortex in awake mice  frequency range, have been strongly implicated in affective and cognitive brain functions such as attention  in children on cortical oscillatory activity  the olfactory cortex and surrounding areas receive olfactory bulb input; (2) the somatosensory cortex receives inputs from mechanoreceptors of chest, the abdominal skin and muscles that are stretched and moved by respiration; and (3) the insular cortex receives input from chemoreceptors and mechanoreceptors in the lungs, diaphragm and internal organs. Our recordings of olfactory bulb dependent respiration-locked oscillations in the mouse somatosensory cortex suggest that respiration-locked activity propagates from primary sensory areas to parts of the cortex that do not receive direct respiration related sensory inputs. A likely mode of propagation is through the cortico-cortical network itself, possibly involving also cortico-thalamic connections. However, the anatomy of axonal connections within the parabulbar and limbic areas suggest a number of subcortical regions and neuromodulator systems may also be influenced by respiration-driven sensory input. For example, widely projecting serotonergic and cholinergic neurons within the rat basal forebrain have been shown to rhythmically discharge in phase with respiration , where d is the Euclidian distance between the lattice points, and c is a constant . Note that some of the nodes are excitatory (E), while others are inhibitory (I). In the present model, we select 25% of the nodes as inhibitory and the rest are excitatory. The update rule is defined by the so-called “k-majority”, i.e., a node becomes active at time t + 1, if more than k of its neighbors have been active at time t, while it will be inactive in the opposite case. Note that inhibitory nodes have inverse effects on excitatory nodes; namely, the activity of inhibitory nodes are subtracted from the total activation when the k-majority rule is tested . In a regular square lattice without shortcuts, the majority rule is given by k = 2. In the results shown here, we select k = 2 and k = 3 for E and I nodes, respectively. Figure Our model has several parameters; the number of shortcuts (LAMBDA); the ratio of excitatory nodes (OMEGA), and threshold parameter . In this model we select parameters OMEGA = 0.75 and LAMBDA = 0.0017; this parameter choice is illustrated by yellow circle in Figure This suggests that the physiological properties of cortical network itself may be sufficient to explain the modulation of gamma power in phase with respiration-locked sensory activity. This is not to say that other factors, such as cortico-thalamic interactions or the action of neuromodulators have no role, but future research will have to determine the nature of their involvement.Each of these forms of cortical activity appears to have different functions. Oscillatory rhythms that are phase-locked to respiration may help to synchronize large portions of the cortical network and create a temporal alignment for slower processes. The calming effect of controlled, slow and deep breathing could be due to this respiration-locked synchronization of activity across large areas of cortex, an EEG activity pattern commonly observed during meditative states  neuronal oscillations following the respiratory rhythm; (2) increases in gamma power phase locked to breathing; and (3) the timing of phase transitions in large scale network activity phase locked to respiration. Gamma oscillation power and phase transition timing are strongly implicated in cognitive function, directly linking breathing to cognitive processes. Our findings and hypotheses provide a new perspective of the function of respiration beyond the life-supporting exchange of gases towards a link between the states of the body and mind. This new physiological role of respiration calls for experimental designs to incorporate respiratory information and for future investigations of the interactions between respiration and cognitive, sensory and motor processes.DHH developed the original research concept, participated in data collection and analysis and wrote the manuscript. SSM participated in data collection and analysis and contributed to writing the manuscript. YL and AB-F contributed to the design of the research, to data analysis and to the writing of the manuscript. RR contributed to data collection, analysis and writing of the manuscript. WJF contributed to the design of the research, especially the modeling aspect, and to the writing of the manuscript (WJF passed away before the completion of this manuscript and is included as a posthumous author). JWW participated in the development of experimental designs, coordinated ECoG data collection and contributed to the writing of the manuscript. ACP contributed to the design of the research, to data analysis and to the writing of the manuscript. MR, YS and RK performed the modeling portion of the study. RK and MR wrote the modeling portion of the manuscript and contributed to the writing of the overall 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.