Many popular computing environments are vulnerable to covert timing channels. With improvements in software confinement mechanisms, shared processor hardware structures will be natural targets for such an attack. The ...
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Many popular computing environments are vulnerable to covert timing channels. With improvements in software confinement mechanisms, shared processor hardware structures will be natural targets for such an attack. The authors present a microarchitecture-level framework that detects the possible presence of covert timing channels on shared hardware. Experimental results demonstrate their ability to detect different types of covert timing channels on various hardware structures and communication patterns.
Adaptive equalization for a time division multiple access (TDMA) digital cellular system is treated in this paper. First, a survey of adaptive equalization techniques is presented, including their performance characte...
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Adaptive equalization for a time division multiple access (TDMA) digital cellular system is treated in this paper. First, a survey of adaptive equalization techniques is presented, including their performance characteristics and limitations, and their implementation complexity. Then, the design of adaptive equalization algorithms for a narrow-band TDMA system is considered.
There are many signal processing applications involving complex-valued data in which phase information is an essential discriminant and for which nonlinear, nonparametric classification would be valuable. This is evid...
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There are many signal processing applications involving complex-valued data in which phase information is an essential discriminant and for which nonlinear, nonparametric classification would be valuable. This is evident when utilizing the output from a Fourier transform, interpreting chaotic oscillator phase plane data, or analyzing time-sensitive impedance plane information generated from the phase quadrature detection of a modulated time domain signal. Artificial neural networks have the potential for being useful in such cases, but must rely on training alone to capture the complex relationships present in the data in order to perform a complex mapping. This paper details the design of a network which incorporates the complex relation in the structure and learning algorithm, thereby enforcing the formation of a complex mapping of the problem space. The network is applied to two phase-sensitive problems: 1) interpretation of chaotic oscillator phase plane plots, and 2) eddy current defect detection and characterization. In chaotic oscillator analysis, the network, in conjunction with the oscillator, demonstrates the ability to interpret small signal behavior. In eddy current impedance plane analysis, the network demonstrates a clear performance advantage over both real-valued multilayer feed-forward networks (MFFN's) and human subjects, with overall classification accuracy improvements of 45% (to a 99% level) and 48%, respectively. This network structure and learning algorithm should provide similar results in other signal processing applications where time or phase considerations are critical for class discrimination.
Several detectors of local activations in unipolar cardiac electrograms are discussed. The detectors are based on the frequency content of the waveforms. For this study, myocardial regions with no local electrical act...
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Several detectors of local activations in unipolar cardiac electrograms are discussed. The detectors are based on the frequency content of the waveforms. For this study, myocardial regions with no local electrical activity were created with cryoablation in canine ventricles, so that the characteristics of electrograms reflecting local activation could be compared with those with only distant electrical activity. For each electrogram, representations, of the original signal were created using the output of bandpass filters; for each representation, the value of the maximum amplitude was taken as a measurement of the frequency content of the electrogram in that frequency band. The content of each frequency band and the first derivative of the signal were tested as discriminators between local and distant electrical activity. Combinations of frequency bands were also tested using a logistic regression technique. It was found that a detector based on multivariate analysis of different frequency components of a signal may be more effective than single-band filtering in discriminating between local and distant electrical activity in the heart, especially when those components have very different magnitudes.< >
The problem of identifying one of the longest sensitizable paths in a circuit is called a critical path problem. Several critical path algorithms have been proposed in the last few years. However, due to the long comp...
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The problem of identifying one of the longest sensitizable paths in a circuit is called a critical path problem. Several critical path algorithms have been proposed in the last few years. However, due to the long computation time required to produce accurate results, these algorithms may not be able to generate any result for large designs with many long false paths unless the accuracy of the results is compromised. Parallel processing seems to be an appropriate way to speed up the required computation. In this paper, we study the parallel algorithms for critical path problem. We first present a sensitization criterion. Based on this sensitization criterion an algorithm called DT-algorithm, which is a variation of the D-algorithm with stable Time range of signals also taken into consideration, is developed. The DT-algorithm is especially suitable and can be used to determine the sensitizability of a given path in a parallel processing environment. We also presented an implementation of parallel DT-algorithm that can be executed on a shared memory multiprocessor. The experimental results show that a reasonable speed-up can be obtained.
Classical in vivo kinetics are based on the analysis of the blood disappearance curve of a substance, This curve is determined by the interplay of the substance transport in the circulatory system and the kinetics of ...
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Classical in vivo kinetics are based on the analysis of the blood disappearance curve of a substance, This curve is determined by the interplay of the substance transport in the circulatory system and the kinetics of the body tissues, The characteristic response of the body tissues, that is, their single-pass impulse response, is obscured in the disappearance curve by multiple recirculations in the heart-systemic loop, The single-pass impulse response can be appropriately determined with circulatory models, This paper investigates the theoretical aspects of the determination of the impulse response and proposes suitable algorithms for the analysis of experimental data with circulatory models, The problem of what information the most typical experimental configurations yield on the single-pass impulse response, and what assumptions are necessary for determining the impulse response in a given configuration is studied first, It is shown that assumptions are usually necessary, unless a proper experiment design is adopted, Two algorithms are then developed for the determination of the impulse response from experimental data, in the general case, The first calculates the response from an exponential description of the disappearance curve, while the second is based on a state space representation of circulatory models, The first method has the important limitation that it does not always ensure physiologically plausible results;the state space approach overcomes this limitation, and is the method of choice, With the state space approach, the determination of the single-pass impulse response is a standard parameter estimation problem with finite dimensional linear systems. The analysis of real data from a study on glucose kinetics illustrates the practical relevance of the results.
This paper presents a new method for designing IIR digital filters with optimum magnitude response in the Chebyshev sense and different order numerator and denominator. The proposed procedure is based on the formulati...
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This paper presents a new method for designing IIR digital filters with optimum magnitude response in the Chebyshev sense and different order numerator and denominator. The proposed procedure is based on the formulation of a generalized eigenvalue problem by using Remez exchange algorithm. Since there exist more than one eigenvalue in the general eigenvalue problem, we introduce a very simple selection rule for the eigenvalue to be sought for where the rational interpolation is performed if and only if the positive minimum eigenvalue is chosen. Therefore, the solution of the rational interpolation problem can be obtained by computing only one eigenvector corresponding to the positive minimum eigenvalue, and the optimal filter coefficients are easily obtained through a few iterations, The designalgorithm proposed in this paper not only retains the speed inherent in the Remez exchange algorithm but also simplifies the interpolation step because it has been reduced to the computation of the positive minimum eigenvalue. Some properties of the filters such as lowpass filters, bandpass filters, and so on are discussed, and several design examples are presented to demonstrate the effectiveness of this method.
Existing mobile visual location recognition (MVLR) applications typically rely on bag-of-features (BOF) representation, which shows superior performance in retrieval accuracy. However, although the BOF framework is pr...
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Existing mobile visual location recognition (MVLR) applications typically rely on bag-of-features (BOF) representation, which shows superior performance in retrieval accuracy. However, although the BOF framework is promising, it is not compact enough for on-device MVLR. The authors have made two contributions to the design of a BOF-based on-device MVLR system. First, to generate BOF descriptors, they propose a memory-efficient approximate nearest-neighbor search algorithm by combining residual vector quantization (RVQ) and tree-structured RVQ (TSRVQ). Second, they implemented a GPS-based and heading-aware RankBoost algorithm to reduce the dimensionality of the BOF descriptors. The authors evaluate the effectiveness of the proposed algorithms on an HTC mobile phone. Their work applies to on-device MVLR in city-scale workspaces.
Some new, efficient, and numerically stable algorithms for the recursive solution of matrix problems arising in optimal beam-forming and direction finding are described and analyzed. The matrix problems considered are...
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Some new, efficient, and numerically stable algorithms for the recursive solution of matrix problems arising in optimal beam-forming and direction finding are described and analyzed. The matrix problems considered are systems of linear equations and spectral decomposition. While recursive solution procedures based on the matrix inversion lemma may be unstable, ours are stable. Furthermore, these algorithms are extremely fast.
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and appl...
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Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth-observation (EO) missions, from low-level vision tasks like superresolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. Although AI techniques enable researchers to observe and understand the earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety critical. This article reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning (FL), uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors' knowledge, this article is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the article to move this vibrant field of research forward.
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