Effective signal detection and feature extraction in noisy environments generally depend on exploiting some knowledge of the signal. When the signal is exactly known, the matched filter is the optimum signal processin...
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Effective signal detection and feature extraction in noisy environments generally depend on exploiting some knowledge of the signal. When the signal is exactly known, the matched filter is the optimum signalprocessing strategy. Other signalprocessing strategies succeed when the signal detail is but partially known. The short-time Fourier transform and the Gabor transform are two methods that exploit signal envelope information. The former is a well known and widely used representation which is important in many fields. The related but distinct Gabor transform has been less frequently used, but has features absent in Fourier analysis. This paper compares the two transforms and makes the case that the Gabor representation can often be the more compact, and may require substantially less computation and storage in some applications. There is a sense in which the Gabor achieves a preferential trade of signal-to-noise ratio for resolution, and because of this, one can also expect better signal recognition and feature reconstructions from the Gabor transform in the presence of noise.
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...
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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.
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...
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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.
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 coupli...
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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.
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 ...
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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.
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 a...
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 processingalgorithms 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
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 ...
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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.
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 sig...
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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.
In this paper, we introduce a new definition for the instantaneous frequency of a discrete-time analytic signal. Unlike the existing definition which uses only two data samples around a particular time, this method ut...
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In this paper, we introduce a new definition for the instantaneous frequency of a discrete-time analytic signal. Unlike the existing definition which uses only two data samples around a particular time, this method utilizes all the data samples for estimating the instantaneous frequency. We prove that this quantity is identical to the average frequency evaluated at the particular time in the discrete-time TFD. This property is consistent with the analogous continuous-time property. We also derive requirements on the discrete-time kernel needed to satisfy this property. Using computer-generated signals and real data, performance comparisons are made between the proposed approach and the existing one.
The kernel in Cohen's generalized time-frequency representation (GTFR) requires is chosen in accordance to certain desired performance attributes. Properties of the kernel are typically expressed as constraints. W...
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The kernel in Cohen's generalized time-frequency representation (GTFR) requires is chosen in accordance to certain desired performance attributes. Properties of the kernel are typically expressed as constraints. We establish that many commonly used constraints are convex in the sense that all allowable kernels satisfying a given constraint form a convex set. Thus, for a given set of constraints, the kernel can be designed by alternately projecting among these sets. If there exists a nonempty intersection among the constraint sets, then the theory of projection onto convex sets (POCS) guarantees convergence to a point in the intersection. If the constraints can be partitioned into two sets, each with a nonempty intersection, the POCS guarantees convergence to a kernel that satisfies the inconsistent constraints with minimum mean square error.
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