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The ubiquitous sensing-capable mobile devices have been fuelling the new paradigm of Mobile Crowd Sensing (MCS) to collect data about their surrounding environment. To ensure the timeliness and quality of the data samples in MCS, it is critical to select qualified participants to maintain sensing coverage ratios over important spatial areas (i.e., hotspots) during time periods of interest and meet...
This paper proposes a simple sensing and estimation framework, called one-bit sketching, to faithfully recover the principal subspace of a data stream or dataset from a set of one-bit measurements collected at distributed sensors. Each bit indicates the comparison outcome between energy projections of the local sample covariance matrix over a pair of random directions. By leveraging low-dimensional...
Accurate localisation has always been a hot topic for indoor environment. Recently, compressive sensing has been applied to fingerprinting based localisation and achieved good performance. This paper provides an overview of the state-of-the-art compressive sensing based indoor localisation techniques and an introduction to potential solutions to challenges faced by current systems. The main focus...
In a cognitive radio ad hoc network, secondary users must cooperate in a decentralized way in order to determine the presence or absence of the primary user. In such a setting, malicious nodes deteriorate the cooperative spectrum sensing performance by reporting incorrect sensing information to the other nodes. We classify distributed cooperative spectrum sensing in cognitive radio ad hoc networks...
In this paper, an efficient distributed approximate message passing (AMP) algorithm, named distributed AMP (DAMP), is developed for compressed sensing (CS) signal recovery in sensor networks with the sparsity K unknown. In the proposed DAMP, distributed sensors do not have to use or know the entire global sensing matrix, and the burden of computation and storage for each sensor is reduced. To reduce...
The successful implementation of dynamic spectrum access in cognitive radio networks requires that the secondary user has an autonomous knowledge of the true status of the licensed user activities. This paper investigates and proposes a robust blind spectrum sensing technique that is based on the variational Bayesian learning for Gaussian mixture model framework for use in multi-antenna cognitive...
Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits therefore. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. Compressive sensing paradigm commonly used for array antenna design and signal...
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted...
This paper presents the application of a humanoid robot as an evaluator of assistive devices; we propose a framework of the evaluation by utilizing identification of the mechanical properties of a humanoid robot. The accurate estimation of joint torque with the identification can enhance the performance to estimate the supporting effect of the devices. We evaluate a passive assistive wear "Smart...
In this paper, we propose a machine learning based spectrum sensing framework for a new cognitive radio (CR) scenario where the primary user (PU) operates under more than one transmit power level. Different from the existing algorithms where the primary transmit power levels are assumed to be known, the proposed approach does not require much prior knowledge of either the primary user or the environment...
Advances in sensor and ubiquitous technologies have contributed to the broad scale adoption of pervasive devices. Context or activity recognition from sensor signals is an emerging area that has garnered huge research interest. In this paper, we propose a novel predictive model that utilizes dyadic wavelet transform, vector quantization and Hidden Markov Model (HMM) to predict a high level activity...
In large scale wireless sensor network (WSN) energy reservation is crucial, as in such an environment sensors cannot be periodically maintain. Therefore we investigate the opportunity to reduce the power consumption by reducing the data rate traffic of the network. This is done utilizing either data correlation and sparsity in one dimension or the spatial sparsity among clustered sensor nodes. We...
In this paper, Cognitive Radio sensing Based on the joint distribution of pseudo WIshart matrix Eigenvalues (CRAB-WISE) is extended to include Wishart matrix based sensing to improve the performance of detecting a primary user signal in an interweave CR scenario. The resulting filter-bank based scheme is applied in a cooperative sensing approach where J users transmit their individual sensing decisions...
Compressive Sensing (CS) is a newly introduced signal processing technique that enables to recover sparse signals from fewer samples than the Shannon sampling theorem would typically require. It is based on the assumption that, for a sparse signal, a small collection of linear measurements contains enough information to allow its reconstruction. Combining the acquisition and compression stages, CS...
This paper deals with a new method PMSM driver inverter faults detection and isolation based on EKF (Extended Kalman Filter). Firstly, a nonlinear model of PMSM diver where the on resistance of inverter FET is included as state variable is derived. Secondly, the method to detect and isolate inverter fault from the estimated on resistance is presented. Finally, the usefulness of the proposed scheme...
Surface ElectroMyoGraphy (sEMG) is a fundamental tool in medicine, rehabilitation, and prostethics but also made appearance on the consumer world with devices such as the Thalmic lab's MYO. Current state of the art transfers the whole sEMG signal but encounter problems when this signal has to be transferred wirelessly in real-time. To overcome limitations of the current state of the art we propose...
Cooperative sensing yields a significant sensing performance improvement. In this paper, we propose an effective sensing algorithm in distributed situation that is considering inband and outband sensing using evolutionary game theory. We drive that the strategy group of secondary users converge to the ESS. Secondary users in ESS state are able to cooperatively sense the channels with optimized group...
Wireless capsule endoscopy (WCE) has emerged as a convenient diagnostic method for human gastrointestinal (GI) diseases owing to its non-invasiveness and capability to explore the entire GI tract. It also has a large potential to play a therapeutic role owing to the rapid advances in micro-electromechanical systems (MEMS) technology. For accurate diagnosis and treatment of pathological conditions,...
Recently in many countries the efforts of researchers are directed to the design of low cost, accurate and robust controllers for small Unmanned Aerial Vehicle (UAV) using the minimum number of on-board sensors. The basic problem of such kind of design is to restore the unknown (unmeasured) states. The approach proposed in the paper consists of two procedures: state restoration by the reduced-order...
The new method of a modernized Wiener optimal filter synthesis is proposed in the article. A main characteristic feature of the method is connected with the achievement of the high-precision selection of the useful stationary random signals vector on a background of a controlled multidimensional stationary noise.
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