CHAOTIC COMPRESSIVE SENSING OF TV –UHF BAND IN IRAQ USING CHEBYSHEV GRAM SCHMIDT SENSING MATRIX

Cognitive radio (CR) is a promising technology for solving spectrum sacristy problem. Spectrum sensing is the main step of CR. Sensing the wideband spectrum produces more challenges. Compressive sensing (CS) is a technology used as spectrum sening in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage sensing matrix are required, and it plays an important role for performance of CS. The design of efficient sensing matrix requires achieving low mutual coherence . In decoding stage the recovery algorithm is applied to reconstruct a sparse signal. İn this paper a new chaotic matrix is proposed based on Chebyshev map and modified gram Schmidt (MGS). The CS based proposed matrix is applied for sensing real TV signal as a PU. The proposed system is tested under two types of recovery algorithms. The performance of CS based proposed matrix is measured using recovery error (Re), mean square error (MSE), and probability of detection (Pd) and evaluated by comparing it with Gaussian, Bernoulli and chaotic matrix in the literature. The simulation results show that the proposed system has low Re and high Pd under low SNR values and has low MSE with high compression.

Hierarchical Multi-User Detection for Highly Overloaded Uplink Grant-Free NOMA

Grant-Free Non Orthogonal Multiple Access (NOMA) offers promising solutions to realize uplink (UL) massive Machine Type Communication (mMTC) using limited spectrum resources, while reducing signalling overhead. Because of the sparse, sporadic activities exhibited by the user equipments (UE), the active user detection (AUD) problem is often formulated as a compressive sensing problem. In line of that, greedy sparse recovery algorithms are spearheading the development of compressed sensing based multi-user detectors (CS-MUD). However, for a given number of resources, the performance of CS-MUD algorithms are fundamentally limited at higher overloading of NOMA. To circumvent this issue, in this work, we propose a two-stage hierarchical multi-user detection framework, where the UEs are randomly assigned to some pre-defined clusters. The active UEs split their data transmission frame into two phases. In the first phase an UE uses the sinusoidal spreading sequence (SS) of its cluster. In the second phase the UE uses its own unique random SS. At phase 1 of detection, the active clusters are detected, and a reduced sensing matrix is constructed. This matrix is used in Phase 2 to recover the active UE indices using some sparse recovery algorithm. Numerical investigations validate the efficacy of the proposed algorithm in highly overloaded scenarios.

Hierarchical Multi-User Detection for Highly Overloaded Uplink Grant-Free NOMA

Grant-Free Non Orthogonal Multiple Access (NOMA) offers promising solutions to realize uplink (UL) massive Machine Type Communication (mMTC) using limited spectrum resources, while reducing signalling overhead. Because of the sparse, sporadic activities exhibited by the user equipments (UE), the active user detection (AUD) problem is often formulated as a compressive sensing problem. In line of that, greedy sparse recovery algorithms are spearheading the development of compressed sensing based multi-user detectors (CS-MUD). However, for a given number of resources, the performance of CS-MUD algorithms are fundamentally limited at higher overloading of NOMA. To circumvent this issue, in this work, we propose a two-stage hierarchical multi-user detection framework, where the UEs are randomly assigned to some pre-defined clusters. The active UEs split their data transmission frame into two phases. In the first phase an UE uses the sinusoidal spreading sequence (SS) of its cluster. In the second phase the UE uses its own unique random SS. At phase 1 of detection, the active clusters are detected, and a reduced sensing matrix is constructed. This matrix is used in Phase 2 to recover the active UE indices using some sparse recovery algorithm. Numerical investigations validate the efficacy of the proposed algorithm in highly overloaded scenarios.

Implementation of Differential Sensing Scheme to Remove Panel Noise in Touchscreen Controller

Differential sensing scheme is an improvement method from single-line sensing to cancel display or panel noise in touchscreen system. However, differential sensing method raised a multiple-touches cancelation issue. As a consequence, the recovery algorithm is invented to overcome those issues. This paper introduces several methods to implement differential sensing scheme with recovery algorithm to remove panel noise and white noise in touchscreen system.

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

With the improvement of living standard and the development of science and technology, Internet of Vehicle (IOV) will play an important part in industrial transportation as a main research field of Internet of Things. As a result, it is very necessary to grasp the location of vehicle. However, the traditional single global position system is easily affected by the external environment, so an accessorial locating approach based on wideband direction of arrival (DOA) estimation in intelligent transportation is proposed. First, model the array received signal on the road infrastructure. Then, by means of random forest regression (RFR) in the supervised learning, upper triangle elements of the covariance matrix of each frequency and the actual DOA are, respectively, extracted as the input features and output parameters; thus, the corresponding prediction coefficients are solved by training. After that, the trained RFR model can be used to calculate the final direction using test samples. Finally, these vehicles can be located according to the geometrical relation between the vehicle and the infrastructure. The proposed algorithm is not only suitable for uncorrelated signals but also for uncorrelated and correlated mixed signals without wideband focusing. The simulations show that compared with some sparse recovery algorithm, the prediction accuracy and resolution are effectively improved.

Block Sparse Bayesian Recovery with Correlated LSM Prior

Compressed sensing can recover sparse signals using a much smaller number of samples than the traditional Nyquist sampling theorem. Block sparse signals (BSS) with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. Utilizing the sparse structure can improve the recovery performance. In this paper, we consider recovering arbitrary BSS with a sparse Bayesian learning framework by inducing correlated Laplacian scale mixture (LSM) prior, which can model the dependence of adjacent elements of the block sparse signal, and then a block sparse Bayesian learning algorithm is proposed via variational Bayesian inference. Moreover, we present a fast version of the proposed recovery algorithm, which does not involve the computation of matrix inversion and has robust recovery performance in the low SNR case. The experimental results with simulated data and ISAR imaging show that the proposed algorithms can efficiently reconstruct BSS and have good antinoise ability in noisy environments.

Experimental estimation of the longitudinal component of a highly focused electromagnetic field

The detection of the longitudinal component of a highly focused electromagnetic beam is not a simple task. Although in recent years several methods have been reported in the literature, this measure is still not routinely performed. This paper describes a method that allows us to estimate and visualize the longitudinal component of the field in a relatively simple way. First, we measure the transverse components of the focused field in several planes normal to the optical axis. Then, we determine the complex amplitude of the two transverse field components: the phase is obtained using a phase recovery algorithm, while the phase difference between the two components is determined from the Stokes parameters. Finally, the longitudinal component is estimated using the Gauss’s theorem. Experimental results show an excellent agreement with theoretical predictions.

Stress Recovery Algorithm for Reduced Order Models of Mechanical Systems in Nonlinear Dynamic Operative Conditions

Nonlinear forced response analyses of mechanical systems in the presence of contact interfaces are usually performed in built-in numerical codes on reduced order models (ROM). Most of the cases these derive from complex finite element (FE) models, because of the high accuracy the designers require in meshing the components in commercial FE software. In the technical literature several numerical methods are proposed for the identification of the nonlinear forced response in terms of kinematic quantity (i.e. displacement, velocity and acceleration) at the location where the master degrees-of-freedom are retained in the ROM. In fact, the displacement is the quantity usually adopted to monitor the nonlinear response, and to evaluate the effectiveness of a partially loose friction interface in damping vibrations with respect to a linear case where no friction interfaces exist and no energy dissipation can take place. However, when a ROM is used the engineering quantities directly involved in the mechanical design, i.e. the strains and stresses, cannot be retrieved without a further data processing. Moreover, in the case of a strong nonlinear behavior of the mechanical joints, the distributions of the nonlinear strains and stresses is likely different than the modal ones, meaning that the latter cannot be used to predict the safety margins of the structure working in real (nonlinear) operative conditions. This paper addresses this topic and presents a novel stress recovery algorithm for the identification of the strains and stresses resulting from a nonlinear forced response analysis on a ROM. The algorithm is applied to a bladed disk with friction contacts at the shroud joint, which make the behavior of the blades nonlinear and non-predictable by means of standard linear analyses in commercial FE software.