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In event-driven wireless sensor networks (WSNs), a reliable, efficient, and scalable routing solution is required for the reliable delivery of sensory data to the base station (BS). However, existing routing algorithms rarely address the issue of energy efficiency under multi-path conflicts for multi-event-driven scenarios. In order to maximize energy efficiency while maintaining a manageable conflict probability, this paper investigates a cross-layer design of routing and power control for multi-event-driven WSNs. We first develop a mathematical characterization of the conflict probability in multi-path routing, and we then formulate the energy efficiency maximization problem as a non-convex combinatorial fractional optimization problem subject to a maximum conflict probability constraint. By utilizing non-linear fractional programming and dual decomposition, an iterative search algorithm was used to obtain near-optimal power allocation and routing solutions. Extensive results demonstrate that our proposed algorithm achieved a gain of 9.09% to 35.05% in energy efficiency compared to other routing algorithms, thus indicating that our proposed algorithm can avoid unnecessary control overhead from multi-path conflicts with a lower conflict probability and can ensure maximum energy efficiency through routing and power control design.

This paper investigates the physical layer security issue in an unmanned aerial vehicle (UAV) aided cognitive radio network. Specially, a UAV operates as an aerial secondary transmitter to serve a ground secondary receiver (SR) by sharing the licensed wireless spectrum assigned to primary terrestrial communication networks, and in the meantime multiple eavesdroppers (Eves) try to wiretap the legitimate UAV-to-SR link. Under the assumption that the location formation of the Eves is imperfect, we jointly optimize the robust trajectory and transmit power of the UAV over a finite flight period to maximize the SR's average worst-case secrecy rate, while controlling the co-channel interference imposed on the primary receivers (PRs) below a tolerable level. The design is formulated as a non-convex semi-infinite optimization problem that is challenging to be optimally solved. To deal with it, we first prove that the considered problem can be simplified as a more tractable one, which resolves the location uncertainties of the Eves without the aid of $\mathcal S$ -Procedure adopted in conventional methods. After that, an efficient iterative algorithm based on successive convex approximation (SCA) is developed to obtain a locally optimal solution. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm and offer important system design insights.

In large field of view for open country, the real-time detection and identification of moving objects with high accuracy is a very challenging work due to the excessive amount of data. This paper proposes a novel framework that consists of a coarse-grained detection as well as a fine-grained detection. To solve the problem of noise-induced object fracture during the coarse-grained detection process, we present a low-complexity connected region detection algorithm to extract moving regions. Furthermore, in the fine-grained detection, Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. To the best of our knowledge, this is the first work that proposes a coarse-to-fine grained framework to detect moving objects on high-resolution scenes. Experimental results show that the proposed framework can robustly work on the high resolution video frames (1920*1080p) with complex situations more fastly and accurately over existing methods.

In the paper, we investigate the physical layer security (PLS) of an unmanned aerial vehicle (UAV) enabled communication system that integrates non-orthogonal multiple access (NOMA) with cognitive radio (CR), namely CR-NOMA. Specifically, the secondary UAV transmits confidential information to multiple secondary legitimate receivers with NOMA by sharing the spectrum with the primary networks, while an eavesdropper (Eve) attempts to wiretap the communication. We aim to jointly optimize the three-dimensional (3D) UAV trajectory and power allocation to maximize the worst-case average secrecy sum rate of all secondary receivers (SRs) while keeping the interference to primal receivers (PRs) below a certain threshold. The formulated problem is mixed-binary non-convex and is generally NP-hard. To address this issue, we first equivalently transform the binary variables into continuous ones by introducing tractable inequality constraints. Then, we decompose the optimization problem into two subproblems and develop an efficient iterative algorithm based on the successive convex approximate (SCA) technique, block coordinate descent (BCD) technique, and penalty function method. Numerical results are extensively studied to show that the proposed joint design significantly outperforms the benchmark schemes in terms of secrecy rate.

This paper proposes a lightweight model combined with data augmentation for vehicle detection in an intelligent sensor system. Vehicle detection can be considered as a binary classification problem, vehicle or non-vehicle. Deep neural networks have shown high accuracy in audio classification, and convolution neural networks are widely used for audio feature extraction and audio classification. However, the performance of deep neural networks is highly dependent on the availability of large quantities of training data. Recordings such as tracked vehicles are limited, and data augmentation techniques can be applied to improve the overall detection accuracy. In our case, spectrogram augmentation is applied on the mel spectrogram before extracting the Mel-scale Frequency Cepstral Coefficients (MFCC) features to improve the robustness of the system. Then depthwise separable convolution is applied to the CNN network for model compression and migrated to the hardware platform of the intelligent sensor system. The proposed approach is evaluated on a dataset recorded in the field using intelligent sensor systems with microphones. The final frame-level accuracy achieved was 94.64% for the test recordings and 34% of the parameters were reduced after compression.

Summary: Members of a disintegrin and metalloproteinases with thrombospondin motif (ADAMTS) family have been implicated in various vascular diseases. However, their functional roles in early embryonic vascular development are unknown. In this study, we showed that Adamts18 is highly expressed at E11.5-E14.5 in cells surrounding the embryonic aortic arch (AOAR) and the common carotid artery (CCA) during branchial arch artery development in mice. Adamts18 deficiency was found to cause abnormal development of AOAR, CCA, and the third and fourth branchial arch appendages, leading to hypoplastic carotid body, thymus, and variation of middle cerebral artery. Adamts18 was shown to affect the accumulation of extracellular matrix (ECM) components, in particular fibronectin (Fn), around AOAR and CCA. As a result of increased Fn accumulation, the Notch3 signaling pathway was activated to promote the differentiation of cranial neural crest cells (CNCCs) to vascular smooth muscle cells. These data indicate that Adamts18-mediated ECM homeostasis is crucial for the differentiation of CNCCs.

Different from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs’ performance by applying machine learning techniques and genetic algorithms. Using the $K$ -means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives’ dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to state-of-the-art algorithms at an acceptable computational complexity.

Unmanned aerial vehicles (UAVs) have attracted growing interest in wireless communications due to their several superiorities, such as highly controllable mobility, favorable communication links with the ground, on-demand deployment, and low cost. This paper investigates a UAV-ground communication system, where a UAV is dispatched to send classified information to a legitimate user in the presence of an eavesdropper on the ground while simultaneously avoiding flying over the no-fly zones (NFZs). We aim to maximize the average secrecy rate of the system by jointly optimizing the UAV's trajectory and transmit power over a given flight period under the practical constraints on the UAV's maximum speed, the initial and final locations, avoidance of NFZs, as well as the transmit power. Although the existing works have studied a similar secrecy rate maximization problem, they all rely on general-purpose solvers, which leads to considerably high computational complexity. To address this issue, we propose an efficient algorithm by applying the techniques of alternating optimization (AO) and successive convex approximation (SCA) to obtain a suboptimal solution and utilizing the alternating directional method of multipliers (ADMM) under the SCA framework to realize low-complex implementation. The simulation results demonstrate the superior computational efficiency of our proposed algorithm and show the impact of NFZs on the system.

The model drift problem is an inevitable problem for online visual tracking. Model drift can amplify the false tracking result over time and lead to failed tracking finally. Background-aware correlation filter (BACF) tracker obtains the training samples from negative background patches instead of shifted foreground patches and can mitigate the drifting to some extent. However, in complex scenes, such as occlusion, deformation, the inappropriate update of BACF may lead to model drift. We propose an anti-drift background-aware correlation filter via introducing the temporal consistency constraint into BACF. That is, we combine the excellent ability of BACF to distinguish the foreground and background with the ability of the temporal consistency constraint to stabilize model changes, and improve anti-drift performance of BACF. To improve computation efficient, we offer a fast algorithm via alternative Direction Method of Multipliers (ADMM) in the frequency domain. Besides, we design a simple yet effective adaptive feature channel selection method, which can further improve the success rate and precision of the tracker in complex scenes. Our proposed tracker with hand-crafted features achieves a gain of 2.2%, 3.3%, and 5.3% in AUC success rate on OTB-2013, OTB-2015, and Temple Color-128 dataset, respectively. Our proposed tracker with deep features achieves AUC success rate of 69.1% on OTB-2013 dataset. Besides, our proposed tracker with hand-crafted features achieves a gain of 3.94% in EAO on VOT-2018 dataset. Moreover, the proposed tracker performs favorably against several representative state-of-the-art methods regarding precision and success rate.

The acoustic classification technology of moving vehicles is a significant application in wireless sensor networks (WSNs). However, the wild environment makes it more intractable for the acoustic target classification owing to the complicated interference noise. The widely used mel-frequency cepstral coefficients (MFCCs) are somewhile contaminated by the acoustic noise, resulting in degrading the classification performance in real environments. To increase the classification accuracy of the moving vehicles, this paper presents an acoustic feature extraction method, which integrates the nonlinear function with the MFCC method, termed as NMFCC. Through nonlinear transformation, the proposed method could amplify the acoustic features in a nonlinear way, and obtain more robust acoustic features, thereby achieving a superior classification performance. Comparing the NMFCC with the baseline feature extraction method, experimental results not only demonstrate the viability of the proposed algorithm but also show a satisfactory performance of the moving vehicle classification for diverse practical scenarios in the wild.

Acoustic sensors are used as an important sensor component in wireless sensor networks (WSNs) for wild environmental monitoring, because of its small size, light weight, and low power consumption. To further utilize the superiorities of acoustic sensor, this paper aims to design a single-mode wild area surveillance sensor based on acoustic sensors, which have the advantages of practicality, low hardware power consumption, and low software complexity. The proposed algorithm can effectively suppress wind noise and improve the performance of target detection, classification and direction of arrival (DOA) estimation. The power consumption test results show that the designed micro-sensor node has low power consumption of about 13.8 mW in on-duty mode and long-term continuous monitoring capability of about 33 days. The field experiment results reveal that the node has favorable performance in detection (detection rate as high as 96% with false alarm rate under 5%), tracking (error is less than 7.6 degree), and target classification (accuracy is greater than 92.6%).

Unmanned aerial vehicles (UAVs)-assisted communications have been an essential complement of conventional wireless networks. In this paper, we consider a UAV-enabled multicasting system, where a UAV with a directional antenna of adjustable beamwidth is employed to disseminate a common file to a group of ground users. By minimizing the mission completion time, we investigate how beamwidth control would affect the UAV's three-dimensional (3D) location/trajectory. First, we consider the quasi-stationary UAV scenario, where the UAV is deployed at a static location. In this case, we jointly optimize the 3D UAV location and antenna beamwidth under the practical constraints on the UAV's altitude and beamwidth, while ensuring that all users are covered by the main lobe of the UAV antenna. Although this problem is nonconvex, its global optimality can be obtained by using a two-step algorithm, where semi-closed solutions of the 3D location and beamwidth are derived. Next, in the mobile UAV scenario, a joint 3D UAV trajectory and beamwidth design is proposed, additionally constrained by the horizontal and vertical speed. To tackle this nonconvex problem, we develop an iterative optimization algorithm based on successive convex approximation techniques, which updates the 3D trajectory and beamwidth simultaneously in each iteration. Numerical results demonstrate the effectiveness of the proposed design as compared to benchmark schemes.

A novel reliability evaluation methodology of complex systems is proposed using dynamic object-oriented Bayesian networks (DOOBNs). This modeling methodology consists of two main phases, namely, construction phases for object-oriented Bayesian networks (OOBNs) and for DOOBNs. In the first phase, the network fragments with similar structures and parameters are divided into classes; these classes are then encapsulated. The construction of OOBNs is completed according to the relationship among the encapsulated classes. In the second phase, every fragment of the dynamic Bayesian networks that was constructed by the first phase is encapsulated as a class called DOOBN. The construction of DOOBNs is completed according to the relationship among the time fragments. The accuracy of this methodology is validated using the all-series, all-voting, voting-after-series, series-after-voting, parallel-after-series, and series-after-parallel systems. This methodology is further illustrated and verified by using deepwater blowout preventer system and can model the system from global to local levels, thereby effectively reducing modeling difficulty and adopting efficient arithmetic reasoning algorithms.

Abstract Along with the developments of deep learning, many recent architectures have been proposed for face recognition and even get close to human performance. However, accurately recognizing an identity from seriously noisy face images still remains a challenge. In this paper, we propose a carefully designed deep neural network coined noise-resistant network (NR-Network) for face recognition under noise. We present a multi-input structure in the final fully connected layer of the proposed NR-Network to extract a multi-scale and more discriminative feature from the input image. Experimental results such as the receiver-operating characteristic (ROC) curves on the AR database injected with different noise types show that the NR-Network is visibly superior to some state-of-the-art feature extraction algorithms and also achieves better performance than two deep benchmark networks for face recognition under noise.

The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.

The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects—first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.

Although there are well established object detection methods based on static images, their application to video data on a frame by frame basis faces two shortcomings: (i) lack of computational efficiency due to redundancy across image frames or by not using a temporal and spatial correlation of features across image frames, and (ii) lack of robustness to real-world conditions such as motion blur and occlusion. Since the introduction of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015, a growing number of methods have appeared in the literature on video object detection, many of which have utilized deep learning models. The aim of this paper is to provide a review of these papers on video object detection. An overview of the existing datasets for video object detection together with commonly used evaluation metrics is first presented. Video object detection methods are then categorized and a description of each of them is stated. Two comparison tables are provided to see their differences in terms of both accuracy and computational efficiency. Finally, some future trends in video object detection to address the challenges involved are noted.

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

Improving tracking quality and extending network lifespan are two main objects for target tracking, which are usually contradictory due to limited energy of sensor nodes in wireless sensor networks. This article incorporates this contradiction into a problem of multi-objective optimization in tracking networks where multiple sensor nodes are scheduled for collaborative target tracking by adopting the unscented Kalman filter algorithm. We propose an effective scheme to extend the lifespan of wireless sensor networks while guaranteeing preset tracking quality. More specifically, with regard to practical circumstances, we perform analysis on the target detection probability, as well as residual energy of sensor nodes, when selecting a suitable set of candidate sensor nodes. Then, we put forward a novel energy-balanced sensor nodes scheduling algorithm, Greedy Balance Replace Heuristic Algorithm, to select a near-optimal task sensor set from the candidate sensor node set to balance tracking quality and network lifetime. In addition, we also design an efficient multi-sensor node collaborative method to track a single target and to timely report its state to the remote end. From simulation results, it is demonstrated that the proposed node scheduling scheme can not only maintain the preset tracking accuracy but also extend network lifespan with a low computation complexity.

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise.

One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity.

Pooling layer in Convolutional Neural Networks (CNNs) is designed to reduce dimensions and computational complexity. Unfortunately, CNN is easily disturbed by noise in images when extracting features from input images. The traditional pooling layer directly samples the input feature maps without considering whether they are affected by noise, which brings about accumulated noise in the subsequent feature maps as well as undesirable network outputs. To address this issue, a robust Local Binary Pattern (LBP) Guiding Pooling (G-RLBP) mechanism is proposed in this paper to down sample the input feature maps and lower the noise impact simultaneously. The proposed G-RLBP method calculates the weighted average of all pixels in the sliding window of this pooling layer as the final results based on their corresponding probabilities of being affected by noise, thus lowers the noise impact from input images at the first several layers of the CNNs. The experimental results show that the carefully designed G-RLBP layer can successfully lower the noise impact and improve the recognition rates of the CNN models over the traditional pooling layer. The performance gain of the G-RLBP is quite remarkable when the images are severely affected by noise.

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.

In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system. Inspired by the IVA algorithm, we propose an improved IVA method based on a microphone array for acoustic signal enhancement in the wild, which adopts a particular multivariate generalized Gaussian distribution as the source prior, an adaptive variable step strategy for the learning algorithm and discrete cosine transform (DCT) to convert the time domain observed signals to the frequency domain. We term the proposed method as DCT-G-IVA. Moreover, we design a target classification system using the improved IVA method for signal enhancement in the UGS system. Different experiments are conducted to evaluate the proposed method for acoustic signal enhancement by comparing with the baseline methods in our classification system under different wild environments. The experimental results validate the superiority of the DCT-G-IVA enhancement method in the classification system for moving targets in the presence of dynamic wind noise.

Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively.

In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

Data broadcast is a fundamental operation in wireless sensor networks (WSNs). The existence of wireless interference makes it nontrivial to design a minimum-latency broadcast scheme, which is known to be NP-hard. Existing works all assume strict time synchronization and provide centralized TDMA scheduling algorithms. However, WSNs in practice are more likely to be distributed asynchronous systems. In this paper, we investigate the problem of data broadcast with minimum latency for distributed asynchronous WSNs. To this end, we propose a Distributed Asynchronous Broadcast (DAB) algorithm which crucially leverages an elaborately optimized carrier-sensing range together with collision-backoff schemes to coordinate the transmissions among the nodes on a predetermined broadcast backbone. Theoretical analysis shows that DAB is order-optimal and achieves constant factor approximation to the optimal delay. We then conduct extensive simulations to evaluate the practical capability of DAB in asynchronous WSNs and the results corroborate our theoretical analysis.

Game theory has emerged as a brand new approach to model and analyse several problems of wireless sensor networks, such as routing, data collection, and topology control. Recently, a novel clustering mechanism called clustered routing for selfish sensors (CROSS) has been proposed based on game theory. The sensor nodes, which are modelled as players, join in a clustering game to campaign for cluster heads with an equilibrium probability. However, the CROSS algorithm needs the global information of how many nodes participate in the game at every round. Considering that this global way introduces much more packets exchange and energy consumption, we present a Localized game theoretical clustering algorithm (LGCA). In our protocol, each node selfishly plays a localized clustering game only with its neighbours within a communication radius R c . Moreover, exactly one node can successfully bid for a position of the cluster head in one district, thus achieving an optimal payoff. Simulation results show that our method achieves a better result compared with CROSS and LEACH in terms of network lifetime.

In this paper, we introduce a sub-band direction-of-arrival (DOA) estimation method suitable for employment within an automatic bearing tracking system. Inspired by the magnitude-squared coherence (MSC), we extend the MSC to the sub-band and propose the sub-band magnitude-squared coherence (SMSC) to measure the coherence between the frequency sub-bands of wideband signals. Then, we design a sub-band DOA estimation method which chooses a sub-band from the wideband signals by SMSC for the bearing tracking system. The simulations demonstrate that the sub-band method has a good tradeoff between the wideband methods and narrowband methods in terms of the estimation accuracy, spatial resolution, and computational cost. The proposed method was also tested in the field environment with the bearing tracking system, which also showed a good performance.

Objective • To explore and analyze the efficacy of luteolin on the proliferation and apoptosis of cerebral glioma and its possible mechanism, providing a theoretical basis for luteolin in glioma treatment. Methods • The cell line of Human cerebral glioma U87 was utilized for our current research. The study group were added with 0, 20, 40, and 80 μmol/L luteolin, respectively. Luteolin’s Inhibitory function in the proliferation of U87 cells was detected by CCK-8, the Transwell chamber test was used to measure cell migration and invasion, while flow cytometry was used to assess apoptosis and Western Blot to gauge the expression of JNK/STAT3 pathway proteins. Results • In comparison with the control group, the intervention of various doses of luteolin could effectively inhibit glioma cell proliferation, the inhibitory rate uplifted remarkably with an increase of dose as well as intervention time (95% CI. 92.160-107.494, P < .05), which presented a significant time and dose dependence. The apoptosis rate of the low-dose, medium-dose, and high-dose categories was higher than that of the comparison group after 2 days of luteolin intervention (P < .05), and the apoptosis rate appeared to increase with the increase in intervention dose (P < .05). After 2 days of luteolin intervention, there were significantly more migratory and invasive cells in 3 categories than that in the control group (P < .05), and the number increased as the luteolin intervention dose was raised (P < .05). Bax and Cyt-c expressions dropped after 2 days of luteolin treatment when compared with the control set (P < .05), and the expression of Bax and Cyt-c fell considerably with increasing luteolin dose. Bcl-2, Caspase-3 and PARP expressions were significantly elevated in comparison to the control group (P < .05), and the increase was more obvious with the rise of the luteolin dose. Conclusion • Luteolin has a good inhibiting function on the proliferation of glioma cells, inhibiting cellular invasion and migration and promoting the apoptosis of glioma cells and the expression of apoptosis-related proteins, which has laid a good foundation for the application of luteolin to treat glioma cells. [ABSTRACT FROM AUTHOR]