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This paper provides an experimental result of non-contact vital sensing by a Doppler sensor for multiple targets. This is based on the signal processing scheme that is termed as differential of accumulation for real-time serial-to-parallel converter (DARS). To the best of authors' knowledge, there is no extant studies dealing with multiple targets with a Doppler sensor. In this experiment, we employ...
We review recent advances in longitudinal fiber dispersion engineering that have enabled construction of efficient parametric devices operating at a few photon level. We outline principal physical processes and present operational demonstration of parametric devices for high speed signal processing and sensing.
Processing of information from the sensors and the resources available to the commander are very important for his decision-making process. Quality of information for decision-making process has a great importance. This article focuses on the description of the modeling of signal processing from sensors in a probabilistic model of recognition Custom / Foreign Identification Friendly or Foe and access...
In the past period, great efforts have been made to develop methods for people detection based on monitoring their respiratory motion using ultra-wide band sensors (radars). The basic principle of these methods consists in the detection of signal components of raw radar data possessing a significant power in the frequency band 0.1 Hz-0.7 Hz (a frequency range of human respiratory rate) for a constant...
As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics....
Sensor driven data is used to fuel today's Smart Home industry with big data, real-time monitoring and control; providing the occupants with wellbeing, security and convenience. Two sectors have seen increased growth within the Smart Home platform, Assisted Living and Energy Management applications. By converging these two sectors and sharing data within a single platform, houses can begin to become...
Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that...
In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections...
A displacement sensing system with the prototype of magnetic-grating-like hydraulic cylinder is developed. It consists of a signal acquisition module, a signal conditioning module, a signal processing module, a signal isolation module and an electrical stimulator module. The system realizes signal processing with the application of tangent segmentation principle. Tests of this devised system have...
We propose a novel technique for the extraction of one independent component from an instantaneous linear complex-valued mixture of signals. The mixing model is optimized in terms of the number of parameters that are necessary to simultaneously estimate one column of the mixing matrix and one row of the de-mixing matrix, which both correspond to the desired source. The desired source is assumed to...
Clustering of multimodal data according to their information content is considered in this paper. Statistical correlations present in data that contain similar information are exploited to perform the clustering task. Specifically, multiset canonical correlation analysis is equipped with norm-one regularization mechanisms to identify clusters within different types of data that share the same information...
This work proposes distributed recursive least squares (RLS) strategies for adaptive reconstruction and learning of signals defined over graphs. First, we introduce a centralized RLS estimation strategy with probabilistic sampling, and we propose a sparse sensing method that selects the sampling probability at each node in the graph in order to guarantee adaptive signal reconstruction and a target...
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 this paper, the focus is on optimal sensor placement and power rating selection for parameter estimation in wireless sensor networks (WSNs). We take into account the amount of energy harvested by the sensing nodes, communication link quality, and the observation accuracy at the sensor level. In particular, the aim is to reconstruct the estimation parameter with minimum error at a fusion center...
Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provide quantitative services, such as personalized training instructions or the counting of distances. But qualitative monitoring and assessment is still missing, e.g., to detect malpositions, to prevent injuries, or to optimize...
Hyperparameter estimation is a recurrent problem in the signal and statistics literature. Popular strategies are cross-validation or Bayesian inference, yet it remains an active topic of research in order to offer better or faster algorithms. The models considered here are sparse regression models with convex or non-convex group-Lasso-like penalties. Following the recent work of Pereyra et al. [1]...
In a networked control system (NCS) where plants and controllers are connected through communication networks, it is important to decrease the number of times of communications under preserving of control performances such as optimality and stability. From this viewpoint, event-triggered control has been studied so far. In event-triggered control, the measured signal is sent to the controller only...
In this paper, we experimentally validate the functionality of a developed algorithm for spectrum cartography using adaptive Gaussian radial basis functions (RBF). The RBF are strategically centered around representative centroid locations in a machine learning context. We assume no prior knowledge about neither the power spectral densities (PSD) of the transmitters nor their locations. Instead, the...
Distributed channel estimation (DCE) is one of the core research topics in wireless sensor networks (WSNs). Under the hypothesis that channel parameters can be modeled as a sparse system, DCE based on compressed sensing (CS) is an effective approach to channel estimation. Among all the existing CS-DCE schemes, every node must store a sensing matrix whose size will increase with the number of channel...
In this work, we consider the joint sparse support recovery problem where the goal is to recover the common support of multiple joint sparse vectors from their compressive, linear measurements. We propose a Rényi Divergence based Covariance Matching Pursuit (RD-CMP) algorithm which recovers the common support of the joint sparse signals as the hyperparameters of a joint sparsity inducing Gaussian...
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