Circuits, Systems and Signal Processing最新文献

Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data.

In this article, a set of equations is derived to find incident and load power explicitly in terms of load and source reflection coefficients in a lossless transmission line mismatched to both source and load impedances. A transmission line can be mismatched as the frequency varies if the source and load impedances are frequency dependent. Unlike in a scenario, where the transmission line is either matched to the source or load, the incident and load power depends on the length of the transmission line when both the source and load impedances are not matched to the line. The equations derived show that the power varies with the line length with a period of half wavelength. The maximum and minimum incident and load power with the corresponding line lengths are derived. The use of the Smith chart to find these lengths and the ratio of maximum to minimum is also described. Finally, three applications of the results including an additional version of the Friis transmission equation and the bandwidth improvement of power transfer for frequency dependent source and load impedances are presented.

The design of a Ternary Logic Processor using CNTFETs (Carbon-Nanotube-Field-Effect-Transistor) is a challenging task, but it also has the potential to offer significant advantages over the traditional binary logic processors based on CMOS (Complementary-Metal-Oxide-Semiconductor) technology. This paper presents the design and implementation of a Ternary Logic Processor (TLP) using CNTFETs. The TLP is a single-cycle processor that operates on three-trit data. An Instruction Set Architecture (ISA) is defined, at first, for this TLP that consists of instructions of the Register type, Load-store type, Immediate type, and branch type. Based on the ISA, the architecture of the CNTFET-based TLP is proposed and the transistor level designs of the TLPs’ fundamental blocks like the Ternary Instruction Fetch (TIF), Ternary Register File (TRF), Ternary Arithmetic and Logic Unit (TALU) and Ternary Data Memory (TDM) are presented. HSPICE simulations using a standard CNTFET model, are performed for the TLP and the TLPs’ individual blocks and the performance parameters like the power consumption, propagation delay, and the number of CNTFETs required are calculated. In addition to this, the functionality of the processor is verified using a few of the standard programs.

Atrial fibrillation (AF) is a life-threatening cardiac condition caused by inadequate blood flow, resulting in abnormal ECG records, blood clotting, and cardioembolic strokes. In recent years, physicians have been particularly concerned with early detection and diagnosis to overcome cardiogenic stroke. AF can be easily identified at the initial stages due to the development in computer-aided diagnosis. The performance of this method is affected by noise and the variations in pattern of the ECG, which leads to false diagnosis. Current signal processing and shallow machine learning (ML) approaches are severely limited in their ability to detect this condition accurately. Deep neural networks have been shown to be extremely effective at learning nonlinear patterns in a wide variety of problems, which include computer vision tasks. Deep learning models are computationally costly, non-explainable, and require a large quantity of data to discover characteristics. In contrast, ML approaches are explainable and require good feature extraction. In this manuscript, ML based supervised classification method is developed based on feature ensembling. ECG signals are preprocessed (mean subtraction followed by Butterworth filtering and computation of RR intervals) and subjected to feature extraction (by entropy-, wavelets-, & statistical-features). The variations due to AF are effectively captured and selective features are ensembled to perform classification by SVM and KNN. This method is experimented on five different databases (such as: PAF prediction Challenge, Long-Term AF, Intracardiac, AF termination Challenge, and MIT-BIH atrial fibrillation) and the classification performance is found to be the highest compared to the state of art. To evaluate the effectiveness of the proposed technique, AF-specific characteristics are retrieved from the ECG signal in the presence of artificially added noise and the features are fed to classifiers for classification. Performance of the proposed method is compared with the deep learning based approaches. The graphical abstract of the proposed atrial fibrillation detection method is presented. The overall accuracy of the proposed method was found to be 91.88(%) and 91.99(%) for wavelets-SVM and ensemble wavelet-SVM, respectively. This model attained 100(%) accuracy for entropy and statistical features with SVM and KNN, respectively.

For the output error (OE) models whose outputs are contaminated by colored process noises (i.e., correlated noises), this paper derives a new form of bias compensation recursive least squares (BCRLS) algorithm by means of the data filtering technology and the bias compensation principle. The basic idea is to firstly transform the OE model disturbed by colored process noise into a simple OE model with the white noise by adopting the data filtering technology at each recursive calculation, and then to calculate the bias compensation term, based on the new OE model with the bias-compensation technique. Finally, eliminate this bias term in the biased RLS parameter estimation of the OE model to be identified, thereby achieving its unbiased parameter estimation. Unlike the previous BCRLS algorithm, this algorithm can still achieve unbiased parameter estimation of OE systems in the presence of colored process noise without calculating complex noise correlation functions. The performance of the proposed algorithm is demonstrated through three digital simulation examples.

Low-light image enhancement algorithms have been widely developed. Nevertheless, using long exposure under low-light conditions will lead to motion blurs of the captured images, which presents a challenge to address low-light enhancement and deblurring jointly. A recent effort called LEDNet addresses these issues by designing a encoder-decoder pipeline. However, LEDNet relies on paired data during training, but capturing low-blur and normal-sharp images of the same visual scene simultaneously is challenging. To overcome these challenges, we propose a self-supervised normalizing flow called SSFlow for jointing low-light enhancement and deblurring. SSFlow consists of two modules: an orthogonal channel attention U-Net (OAtt-UNet) module for extracting features, and a normalizing flow for correcting color and denoising (CCD flow). During the training of the SSFlow, the two modules are connected to each other by a color map. Concretely, OAtt-UNet module is a variant of U-Net consisting of an encoder and a decoder. OAtt-UNet module takes a low-light blurry image as input, and incorporates an orthogonal channel attention block into the encoder to improve the representation ability of the overall network. The filter adaptive convolutional layer is integrated into the decoder, applying a dynamic convolution filter to each element of the feature for effective deblurring. To extract color information and denoise, the CCD flow makes full use of the powerful learning ability of the normalizing flow. We construct an unsupervised loss function, continuously optimizing the network by using the consistent color map between the two modules in the color space. The effectiveness of our proposed network is demonstrated through both qualitative and quantitative experiments. Code is available at https://github.com/shibaoshun/SSFlow.

In this paper, a novel sliding mode preview control (SMPC) problem with (H_{infty }) performance is investigated for a category of discrete-time singular Markovian jump systems (SMJSs). A novel augmented error system (AES) model is first developed for discrete-time SMJSs based on the analysis of preview information, and the problem of SMPC is reformulated as the stability problem of AES. Secondly, a novel mode-independent sliding surface function is established for AES such that the reachability of sliding mode surfaces (SMS) can always be achievable. Thirdly, sufficient conditions of the (H_{infty }) admissible stability for sliding mode dynamics is derived, based on which a suitable SMPC law is designed to satisfy discrete-time reachability condition. Finally, simulation results have shown that the proposed SMPC law is superior to the control law without previewable information.

Recent advancements in deep learning-based speech enhancement models have extensively used attention mechanisms to achieve state-of-the-art methods by demonstrating their effectiveness. This paper proposes a novel time-frequency attention (TFA) for speech enhancement that includes a multi-scale subconvolutional U-Net (MSCUNet). The TFA extracts valuable channels, frequencies, and time information from the feature sets and improves speech intelligibility and quality. Channel attention is first performed in TFA to learn weights representing the channels’ importance in the input feature set, followed by frequency and time attention mechanisms that are performed simultaneously, using learned weights, to capture both frequency and time attention. Additionally, a U-Net based multi-scale subconvolutional encoder-decoder model used different kernel sizes to extract local and contextual features from the noisy speech. The MSCUNet uses a feature calibration block acting as a gating network to control the information flow among the layers. This enables the scaled features to be weighted in order to retain speech and suppress the noise. Additionally, central layers are employed to exploit the interdependency among the past, current, and future frames to improve predictions. The experimental results show that the proposed TFAMSCUNet mode outperforms several state-of-the-art methods.

This paper presents a novel multiple-input single-output current-mode shadow filter and oscillator using current-controlled current conveyors (CCCIIs). The CCCII current gains are used to set the filter’s quality factor and natural frequency. The filter is resistorless with grounded capacitors, making it more suitable for integration. The filter offers low-pass, high-pass, band-pass, band-stop and all-pass transfer functions in a single topology by appropriately applying the input signals. To validate the functionality of the new topology, the proposed circuit is simulated by SPICE using bipolar transistors arrays AT&T ALA400-CBIC-R.

How to guarantee the confidentiality of sensitive data communicated over the Internet and restrict access to designated information is today’s key security and protection concern in telemedicine. In this work, we suggest a reliable and blind medical image watermarking method that combines integer wavelet transform (IWT) and singular value decomposition to keep such information private. A major drawback of current IWT-based watermarking systems is their low embedding capacity. This paper suggests an IWT-based secure large capacity watermarking solution to overcome this specific drawback. The proposed technique effectively preserves a considerable quality of watermarked images, and the watermark is resistant to the most frequently used attacks in watermarking, according to experiment results on imperceptibility and robustness.