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By considering the existence of bridge sensor nodes that lose the capability of sensing, the average bridge consensus problem aims to achieve the average of selective observations only from sensing nodes while bridge nodes play the role of relaying information. We note that traditional average consensus algorithms achieve the global average of states from all nodes in the network, which are not directly...
This paper presents a reactive collision avoidance algorithm for vehicles with unicycle-type nonholonomic constraints. Static and dynamic obstacles are avoided by keeping a constant avoidance angle to the obstacle. The algorithm compensates for the obstacle velocity, which can be time-varying. Conditions are derived under which successful collision avoidance is mathematically proved, and the theoretical...
Distributed and cooperative algorithms are of preponderant importance for the correct operation of multiagent systems. In particular, average consensus algorithms represent an appealing alternative for combining measurements in large-scale networks of low-capable sensors, due to their low computational cost and strong convergence properties. However, the actual performance of average consensus algorithms...
This paper deals with the optimization of sensing matrices and sparsifying dictionaries for compressed sensing systems. A gradient-based method with a new measurement strategy denoted as real mutual coherence is proposed. Further more, the sensing matrix is optimized by minimizing an objective function in which the target Gram is selected as Ψ Ψ, this choice has advantages to reconstruct real images...
Detection of targets using low power embedded devices has important applications in border security and surveillance. In this paper, we build on recent algorithmic advances in sensor fusion, and present the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles using acoustic and seismic sensors. Here, by "multi-mode", we mean...
In recent technological advancement such as smart grid applications, security surveillance & border protection, internet of things, disaster management & other smart home applications exhilarate the deployment of autonomous, self-configured, large-scale wireless sensor networks. Efficient power conservation is crucial concerns for sensor networks to operate in the hostile environment. Therefore...
Cognitive radio (CR) has been identified as an enabling technology toward meeting the high spectrum utilization efficiency demand in future internet-of-things (IoT) systems. Development of new spectrum sensing schemes better suited to CR-based IoT networks, which are typically heterogeneous with perfect network-wide synchronization difficult to achieve, is thus rather crucial. Motivated by the low-complexity...
Accurate recognition of human actions is essential to many health-care, entertainment, and human-computer interface applications. However, the achievable accuracy depends on a variety of parameters for the various stages of recognition, including sensing, feature extraction, and classification. In this paper, we quantitatively evaluate the classification accuracy for varying sensing rates, sensor...
Measurement of a physical parameter can adversely be affected by several factors like manufacturing process variations, environmental conditions etc. The measured output may non-linearly vary with many of these factors and may cause error in the parameter being measured. A precise modeling of the input-output relationship of measurement systems is indeed a necessity in order to have an accurate measurement...
Thermoelectric generators (TEGs) directly convert heat energy into electricity for power-supplying sensors and other portable electronic devices. In this paper, a maximum power point tracking (MPPT) controller in the modified perturb and observe (P&O) algorithm was designed on the basis of analyzing the output characteristics of the TEG modules. The MPPT controller consists of a voltage sensor,...
Still to this day, there are many industrial sequential processes described in Ladder Diagram (LD), a language not suitable for that purpose, that can be described in SFC, a proper language for describing sequential processes. We provide analysis on the main algorithms for extracting the sequential logic from a LD program that can be found in the literature and point some problems in them. We then...
There are almost no on-board intelligent anomaly detection systems in most of the existing unmanned Aerial Vehicles (UAVs), and the flight status assessment still depends on ground control station. While, this method can't meet the requirement of real-time anomaly detection for UAV autonomous and safe flight. In order to achieve real-time monitoring of UAV flight status, and improve the reliability...
The Sparsity Adaptive Matching Pursuit (SAMP) algorithm needs not to know a priori information of sparsity, which makes it has unique advantages compared to other greedy algorithms. In the reconstruction process, the step size can be changed with stage, which increases its reconstruction accuracy. Aiming at the step size of SAMP, it is unreasonable, will only increase and it is not decrease properly...
Cooperative detection system, which combines selforganizing network and single radar system, improves the ability of cooperative localization by distributed multi-node using multi-direction scattering power of target. Multiplehypothesis (MH)-Based Algorithm for Target Localization finds all possible targets using multi-path echo information with unknown number of targets, and realizes multi-target...
The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
Recent work has demonstrated the effectiveness of gradient descent for recovering low-rank matrices from random linear measurements in a globally convergent manner. However, their performance is highly sensitive in the presence of outliers that may take arbitrary values, which is common in practice. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against...
We consider the problem of choosing the best subset of sensors that results in a prescribed error probability Pe in Bayesian setting. Since minimizing the error probability is often difficult to evaluate and manipulate, conventional methods adopt Bhattacharyya distance instead of it. In fact, Chernoff distance is the best achievable exponent in the Bayesian error probability and it is more accurate...
Tracking single or multiple maneuvering targets is an urgent need for defense. In order to meet the military requirement, we propose a modified clustering-based Rao-Blackwellized particle filter (CBRBPF) to track single or multiple maneuvering targets with observations received by single or multiple sensors. The modified RBPF is basing on the clustering-based data association method. We partition...
Contrast to the two-dimensional directional sensor networks, the three-dimensional directional sensor networks increases complexity and diversity. External environment and sensor limitations impact the target monitoring and coverage. Adjustment strategies provide better auxiliary guide in the process of self-deployment, while strengthen the monitoring area coverage rate and monitoring capability of...
Today there exist many different types of smart assistants and devices, such as virtual assistants, smartphones and wearables, which have a purpose to coordinate and optimize the daily activities of the people worldwide. The smart assistants' focus is mainly on basic human needs, e.g. browsing, scheduling, navigating and other similar activities. However, not many smart assistants are concerned with...
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