Three dimensional beamforming (3DBF) is a famed technology that enhances spectral efficiency, capacity and coverage area of the 5G networks compared to conventional two dimensional (2D) beamforming by implementing bot...
详细信息
ISBN:
(纸本)9781538666876
Three dimensional beamforming (3DBF) is a famed technology that enhances spectral efficiency, capacity and coverage area of the 5G networks compared to conventional two dimensional (2D) beamforming by implementing both user specific horizontal and vertical beamforming. In this paper, three LMS based adaptive spatial filtering algorithms, namely, Ang's, Mathew's and Fixed step-size LMS have been implemented and evaluated on FPGA for 3DBF scenarios. The associated signalprocessing issues, specifically for FPGA implementation have been critically investigated. A prototype design of a 3DBF system deploying these adaptive algorithms has been demonstrated using Xilinx Zynq®-7000 family based FPGA platform. Coexistence of Xilinx System Generator ™ and MATLAB Simulink® environment has been suitably utilized for the purpose. The performance metrics in terms of convergence properties like Mean Square Error (MSE) and Step-size along with beam pattern of all algorithms have been evaluated. Finally, hardware resource utilization of these algorithms has also been analyzed. Investigations reveal the superiority of Mathew's algorithm over the other two for 3DBF.
New algorithms are being developed in the radar community that blend a priori knowledge source processing with traditional digital signalprocessing concepts. This operational blend necessitates a system-level archite...
详细信息
New algorithms are being developed in the radar community that blend a priori knowledge source processing with traditional digital signalprocessing concepts. This operational blend necessitates a system-level architecture capable of delivering both high processing throughput and memory bandwidth. This paper derives these system parameters from the knowledge aided pre-whitening algorithm and evaluates the performance of two high performance embedded computing architectures, the Imagine and Raw processors, on these kernels. The implementation results are compared with the measured performance of a conventional system based on the PowerPC with Altivec. The results show these processors exhibit significant improvements over conventional systems and that each architecture has its own strengths and weaknesses.
A novel approach to the design of frequency deviation measurement algorithms is defined. This approach is based on a signalprocessing scheme that uses quadratic forms of signal samples. The approach is general, and m...
详细信息
A novel approach to the design of frequency deviation measurement algorithms is defined. This approach is based on a signalprocessing scheme that uses quadratic forms of signal samples. The approach is general, and methods to use it as a design tool for developing new algorithms are presented. The performance of an algorithm developed to illustrate the design approach is evaluated using computer simulation tests.< >
A class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors is proposed. The methods estimate the DOA using only linear transformations of the data. ...
详细信息
A class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors is proposed. The methods estimate the DOA using only linear transformations of the data. This is of special interest for cases when the number of sensors is large and the computational advantages of these methods are significant. These methods use a less restrictive noise model and, e.g., can accommodate cases where the noise variance is different for different sensors. Large sample variance expressions for the estimates of the DOAs are derived, and the statistical properties of the proposed method are compared against the properties of multiple signal classification (MUSIC).< >
Medical signals are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease evolution. Medical imaging mainly treats and processes missing, ambiguous, comp...
详细信息
Medical signals are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease evolution. Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant, contradictory, distorted data, and information has a strong structural character. This paper reports two “immune algorithms” based approaches for medical signalprocessing. Immune algorithms belong to the Artificial Immune Systems field. The first proposed approach uses the Clonal Selection Algorithm (CSA) for geometric transform estimation in image registration (IR). The second approach, the Dendritic Cell Algorithm (DCA) is used for automatic driver stress detection using biomedical signals.
Machine learning and related statistical signalprocessing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adapt...
详细信息
Machine learning and related statistical signalprocessing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by the design community and present a new set of design trade-offs. This review focuses on efficient implementation of mixed-signal matrix-vector multiplication as a central computational primitive enabling machine learning and statistical signalprocessing, with specific examples in spatial filtering for adaptive beamforming. We describe adaptive algorithms amenable for efficient implementation with such primitives in the presence of noise and analog variability. We also briefly highlight current trends in high-density integration in emerging memory device technologies and their use in highdimensional adaptive computing.
A comparison is made of three characteristic algorithms for adaptive signalprocessing in ISDN (integrated-services digital network) user interfaces: a fast RLS (recursive least squares) algorithm with transversal FIR...
详细信息
A comparison is made of three characteristic algorithms for adaptive signalprocessing in ISDN (integrated-services digital network) user interfaces: a fast RLS (recursive least squares) algorithm with transversal FIR (finite impulse response) structure, an LMS (least mean squares) algorithm with transversal FIR structure, and a sign algorithm with memory filter structure. The algorithms are compared by using a simulated system for data transmission, which is applied to the study of the characteristics of adaptive algorithms and for the education of the students in the area of telecommunications.< >
The Levinson algorithm, implemented via a ladder filter, has been widely used in seismic signalprocessing and more recently in speech analysis. This algorithm was originally developed (in 1947) for prediction of stat...
详细信息
The Levinson algorithm, implemented via a ladder filter, has been widely used in seismic signalprocessing and more recently in speech analysis. This algorithm was originally developed (in 1947) for prediction of stationary stochastic processes. It has been adapted to applications where only a single sample function (time-series) is available by the use of appropriate "window functions" to "simulate stationarity". Such windowing is often somewhat artificial and can introduce undesirable artefacts in situations where only a limited amount of data is available. However, more realistic "windowing" will destroy the analogy to the stationary stochastic process case and thus apparently prohibit use of the Levinson-ladder- filter implementations. In this talk, we shall first show how by using a concept of how close a given process is to being stationary, we can develop generalized Levinson algorithms and generalized ladder filters applicable to any stochastic process, stationary or not. The complexity of the algorithm and the filter is proportional to the distance from stationarity of the given process. We shall then show that this structure can include analyses of several windowing strategies for applications where only a single observed record is available, thus including results obtained earlier for such problems by Morf, Vieira and Lee. The present results are based on work with D. Lee and H. Lev-Ari.
According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to di...
详细信息
According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research's primary process is to identify and classify the data related to the heart sounds categorized in four general groups of S 1 to S 4 . The sounds S 1 and S 2 are considered as the heart's normal sounds, and the sounds S 3 and S 4 are the abnormal sounds of the heart (heart murmurs), each expressing a specific type of heart disease. In this regard, the desired features are first extracted after retrieving the data by signal processing algorithms. In the next step, feature selection algorithms are used to select the compelling features to reduce the problem's dimensions and obtain the optimal answer faster. While the existing algorithms in the literature classify the sound into two groups of normal and abnormal, in the final section, some of the most popular classification algorithms are utilized to classify the type of sound into three classes of normal, S 3 and S 4 categories. The proposed methodology obtained an accuracy rate of 87.5% and 95% for multiclass data (3 classes) and 98% for binary classification (normal vs. abnormal) problems.
暂无评论