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Cumulant-based cubic difference tone (CDT) detectors for otoacoustic emissions (OAE) signals

Cumulant-based cubic difference tone (CDT) detectors for oto...

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advanced signal processing: algorithms, architectures, and implementations V

作者： Swami, Ananthram Rao, Preeti CS-3 Los Angeles CA USA

ISBN: (纸本)0819416207

Interactions among spontaneous otoacoustic emissions (SOEs), which are narrow-band signals emitted by the cochlea, include the cubic difference tone (CDT) that occurs at frequency 2f 1 - f2 due to the nonlinear coupling of primary SOEs at f1 and f2. We show that appropriately defined trispectra and tricoherence may be used to detect the CDTs. OAE signals are also characterized by temporal variations in the amplitudes and frequencies;this leads to polynomial phase models, and multiplicative noise processes;we give closed-form expressions for the large sample CRLB for the parameters of the resulting model. We also consider cyclic approaches, and we compare polyperiodogram and periodogram based approaches for the detection of arbitrary frequency coupling laws. Some of the techniques are validated against real data.

关键词： signal detection

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Time frequency filtering and synthesis from convex projections

Time frequency filtering and synthesis from convex projectio...

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advanced signal-processing algorithms, architectures, and implementations

作者： White, Langford B. Defence Science and Technology Organisation Salisbury Australia

This paper describes the application of the theory of projections onto convex sets to time-frequency filtering and synthesis problems. We show that the class of Wigner-Ville Distributions (WVD) of L2 signals form the boundary of a closed convex subset of L2(R2). This result is obtained by considering the convex set of states on the Heisenberg group, of which the ambiguity functions form the extreme points. The form of the projection onto the set of WVDs is deduced. Various linear and non-linear filtering operations are incorporated by formulation as convex projections. An example algorithm for simultaneous time-frequency filtering and synthesis is suggested.

关键词： signal Filtering and Prediction

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Dynamically Reconfigurable architectures for Real-time Baseband processing

Dynamically Reconfigurable Architectures for Real-time Baseb...

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作者： Chenxin Zhang University of Lund

学位级别：博士

Motivated by challenges from today's fast-evolving wireless communication standards and soaring silicon design cost, it is important to design a flexible hardware platform that can be dynamically reconfigured to adapt to current operating scenarios, provide seamless handover between different communi- cation networks, and extend the longevity of advanced systems. Moreover, increasingly sophisticated baseband processing algorithms pose stringent re- quirements of real-time processing for hardware implementations, especially for power-budget limited mobile terminals. With existing hardware platforms such as Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), and Digital signal Processors (DSPs), the contradictory design requirements of flexibility, computational performance, and hardware ef- ficiency cannot be attained at the same time. To achieve a balance between the aforementioned design requirements, a coarse-grained dynamically reconfigurable cell array architecture is proposed. The architecture is constructed from an array of heterogeneous function units interconnected through a hierarchical on-chip network. The adopted in-cell configuration scheme enables fast context switching between standards and be- tween computational tasks during run-time. Although cell array is a generic hardware platform, this thesis focuses on the architectural development of the cell array tailored specifically for digital baseband processing of contemporary wireless communication systems. Various degrees of flexibilities among operat- ing scenarios, algorithms, tasks, and supporting standards are exploited. Be- sides, high hardware efficiency is attained by conducting algorithm-architecture, hardware-software, and processing-memory co-design. In this thesis, flexibility, performance and efficiency of the proposed archi- tecture are demonstrated through two case studies. First, the cell array is de- ployed in a digital front-end receiver, aiming t

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New matrix decomposition based on transforming the basis sets of the singular value decomposition yields principal features for time-frequency distributions

New matrix decomposition based on transforming the basis set...

引用

Advance signal processing algorithms, Atchitectures, and implementations X

作者： Groutage, Dale Bennink, David Naval Surface Warfare Cent Bremerton United States

We present a matrix decomposition that can be used to derive features from processes that are described by discrete-time, time-frequency representations. These include, among others, electrocardiograms, brain wave signals, seismic signals, vibration and shock signals, speech signals for voice recognition, and acoustic transient signals. The new decomposition is based on a transformation of the basis vectors of the singular value decomposition (SVD) which we call transformed singular value decomposition or TSVD. The transformed basis vectors are obtained by forming linear combinations of the original SVD basis vectors in a way such that the means of the transformed vectors are extrema of each other. The TSVD basis vectors are used to identify concentrations of energy density in the discrete-time, time-frequency representation by time and frequency descriptors. That is, descriptors such as the location in time, the spread in time, the location in frequency and the spread in frequency for each principal concentration of energy density can be obtained from the TSVD terms in the matrix decomposition series. Several examples are presented which illustrate the application of the new matrix decomposition for deriving principal time and frequency features from the discrete-time, time-frequency representations of nonstationary processes. Two of the examples illustrate how the derived time and frequency features can be used to classify individual short duration transient signals into respective classes, that is,: (1) automatically classify sonar signals as belonging to one of ten classes, and (2) automatically classify heartbeat signals as belonging to one of two people.

关键词： signal processing

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Real-time implementation of the multistage detector for next generation wideband CDMA systems

Real-time implementation of the multistage detector for next...

引用

Proceedings of the 1999 advanced signal processing algorithms, architectures, and implementations IX

作者： Xu, Gang Cavallaro, Joseph R. Rice Univ Houston United States

The multistage detection algorithm has been widely accepted as an effective interference cancellation scheme for next generation Wideband Code Division Multiple Access (W-CDMA) base stations. In this paper, we propose a real-time implementation of this detection algorithm in the uplink system, where we have achieved both high performance in the interference cancellation and computational efficiency. When interference cancellation converges, the difference of the detection vectors between two consecutive stages is mostly zero. We recode the estimation bits, mapping from ±1 to 0 and ±2. Bypassing all the zero terms saves computations. Multiplication by ±2 can be easily implemented in hardware as arithmetic shifts. The system delay of a three-stage detector can be reduced by half with satisfactory bit error rate. We also propose a VLSI implementation of this algorithm that has the potential of real-time performance. The detector handling up to eight users with 12-bit fixed point precision was fabricated using a 1.2 μm CMOS technology.

关键词： Digital signal processing

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Application of canonical correlation analysis in detection in presence of spatially correlated noise

Application of canonical correlation analysis in detection i...

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advanced signal processing algorithms, architectures, and implementations II

作者： Chen, Wei G. Reilly, James P. Wong, Kon M. McMaster Univ. Hamilton Ontario Canada

ISBN: (纸本)0819406945

A new approach to the problem of detecting the number of signals in unknown colored noise environments is presented. Based on an assumption that the noise is correlated only over a limited spatial range, the principle of canonical correlation analysis is applied to the outputs of two spatially-separated arrays. The number of signals is determined by testing the significance of the sample canonical correlation coefficients. The new method is shown to work well in both white and unknown colored noise situations and does not require any subjective threshold setting. Instead, a set of threshold values are generated according to a specified or desired false alarm rate. Simulation results are included to illustrate the comparative performance of the proposed canonical correlation technique (CCT), versus the well-known AIC and MDL criteria, in colored noise. It is found that the performance of the AIC and MDL criteria degrade very rapidly as the degree of color in the noise increases. On the other hand, the performance of the CCT method is relatively insensitive with respect to variations in degree of color.

关键词： signal processing

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Pipelining multiple singular value decomposition (SVDs) on a single processor array

Pipelining multiple singular value decomposition (SVDs) on a...

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advanced signal processing: algorithms, architectures, and implementations V

作者： Kota, Kishore Cavallaro, Joseph R. Rice Univ. Houston TX USA

ISBN: (纸本)0819416207

We present a new family of architectures for processor arrays to implement Jacobi SVD which allow systolic loading and unloading of input and result matrices. Unlike most of the previous SVD arrays in the literature, our architectures do not require special handling of external I/O and hence are closer to the traditional concept of systolic architectures. The boundary processors communicate with the host the same way any of the interior processors communicate with their neighbors. The arrays are surprisingly uniform and simple. The various architectures in the family represent different throughput-hardware tradeoffs corresponding to the degree to which the multiple sweeps have been unrolled and determine the number of independent SVDs which may be pipelined on the array. We achieved systolic loading by using the flexibility provided by the cyclic Jacobi method on the order in which pivot pairs may be chosen. The array operates on the matrix data even as it is being loaded. Once the pipeline is full, the ordering is very similar to odd-even ordering. Our ordering is equivalent to cyclic-by-rows ordering and hence the algorithm is guaranteed to converge. Our systolic loading scheme is very important in an I/O limited system, since it allows more communication to occur in parallel, where the communication includes the loading and unloading operations. The array with the highest throughput in our family of architectures, which implement one-sided Jacobi (either Hestenes' method or Eberlein and Park's method), is a linear array of processors with unidirectional links between neighbors. The architectures with lower throughput require fewer processors connected in a ring, allowing data to recirculate among the processors. The input matrix is loaded one column at a time from the left and the results stream out one column at a time from the right.

关键词： Pipeline processing systems

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Generic High-Speed Design with Low-Area implementations of Statistical Operations Based on an FPGA Device 26

Generic High-Speed Design with Low-Area Implementations of S...

引用

26th International Symposium on Symbolic and Numeric algorithms for Scientific Computing, SYNASC 2024

作者： Chehaitly, Mouhamad Querol, Jorge Vummadisetty, Praveen Chatzinotas, Symeon University of Luxembourg SnT Luxembourg Luxembourg

ISBN: (纸本)9798331532833

Artificial Intelligence has emerged as a transformative technology, revolutionizing numerous industries by enabling advanced automation, predictive analytics, and decision-making capabilities. For that Artificial Intelligence overruns many domains like telecommunication, smart manufacturing industry, autonomous machines, Automated Disease Diagnosis in Medical Imaging, defense, and others. On the other hand, the hardware implementation of Artificial Intelligence comes with certain challenges and constraints, especially in a critical area, which leverages machine learning algorithms and real-time data analysis to optimize production processes and improve overall efficiency. Statistical operations play a crucial role in various machine learning algorithms to understand, process data, or make predictions to optimize models. So, in this work, we developed a high-speed and low-area design and implemented statistical operations for image or signal processing using an FPGA Device. To enhance the performance, we develop different hardware architectures based on different levels of parallelism to process the statistical operations to compute the Mean, Variance, and RMS (Root Mean Square). These generic architectures work in parallel/pipeline architectures with and without memory. The proposed architectures implement an FPGA target (Intel/Altera Agilex 7: AGMH039R47A2E1V) using Altera Quartus prime pro edition version 23.4 and achieve an ultra-high throughput with low-area consumption compared to the state-of-art methods. For 480×640 image size, the mean calculation architecture involves 1498 logic registers, 1912 slice LUT, and just 29kbits memory and it operates at a maximum frequency of 406.5MHz. Additionally, for an 8×8 image size, we need 33 clock cycles to achieve the mean calculation and 33+1 clock cycles to complete the variance calculation, compared to other approaches that require more than 64 clock cycles. © 2024 IEEE.

关键词： Digital storage

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Computing the generalized singular value decomposition on the connection machine

Computing the generalized singular value decomposition on th...

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advanced signal-processing algorithms, architectures, and implementations

作者： Park, Haesun Ewerbring, L.Magnus Univ of Minnesota Minneapolis United States

A new algorithm for computing the generalized singular value decomposition that diagonalizes two matrices is introduced. First, two existing algorithms are studied, one due to Paige and the other to Hari and Veselic. The former requires only orthogonal plane transformations, resulting in two matrices with parallel rows. The latter employs nonsingular (not necessarily orthogonal) transformations to directly diagonalize the two data matrices. For many applications, it is preferable to be given both the diagonal matrices and the transformation matrices explicitly. Our algorithm has the advantages that the nonsingular transformation is readily available, computation of transformations is simple, convergence test is efficient in a parallel computing environment, and the condition of each nonsingular transformation can be controlled. We present implementation results, obtained on a Connection Machine 2, to compare our new algorithm with that of Paige.

关键词： signal processing

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Transient detection in the presence of strong narrow band interference via Wigner-Ville spectrum

Transient detection in the presence of strong narrow band in...

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advanced signal processing algorithms, architectures, and implementations 1990

作者： Marinovich, Nenad M. Roytman, Leonid M. Department of Electrical Engineering City College of the City University of New York New YorkNY10031 United States

We consider the problem of detecting a known Gaussian random transient in the presence of a strong, known, random, Gaussian, narrowband interference. This can be regarded as a special case of the classical problem of detecting a known Gaussian random signal in known Gaussian colored noise. There exists a standard solution for such a problem, based on the classical optimum detector for random signals in noise. However, such a detector does not explicitly use the non-stationary character of the signal as a priori available information. Reformulation of the optimum detection in the time-frequency plane allows one to exploit this distinguishing signal feature and suppress the stationary interference and noise. This is accomplished here by use of the Wigner-Ville signal representation and an optimum signal/noise subspace decomposition that maximizes the transient signal to noise ratio. The new detection procedure eliminates the subspace where major part of the energy of random noise sample will fall while retaining almost all of the signal energy. In this fashion, a gain in the output signal to noise ratio is achieved as verified by simulations. © 1990 SPIE. All rights reserved.

关键词： signal to noise ratio

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