Circuits, Systems and Signal Processing最新文献

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems.

In this text, we discuss the filter banks used for speech analysis and propose a novel filter bank for speech processing applications. Filter banks are building blocks of speech processing applications. Multiple filter strategies have been proposed, including Mel, PLP, Seneff, Lyon, and Gammatone filters. MFCC is a transformed version of Mel filters and is still a state-of-the-art method for speech recognition applications. However, 40 years after their debut, time is running out to launch new structures as novel speech features. The proposed acoustic filter banks (AFB) are innovative alternatives to dethrone Mel filters, PLP filters, and MFCC features. Foundations of AFB filters are based on the formant regions of vowels and consonants. In this study, we pioneer an acoustic filter bank comprising 11 frequency regions and conduct experiments using the VGG16 model on the TIMIT and Speech Command V2 datasets. The outcomes of the study concretely indicate that MFCC, Mel, and PLP filters can effectively be replaced with novel AFB filter bank features.

A new passive RC analog of a coupled double tuned circuit, as is commonly used in broad-band RF amplifiers, is presented in this paper. It consists of a cascade of bandstop Wien network and a bandpass parallel-T network. A by-product of this study is that by inverting this circuit, we get a wide-band rejection circuit. By interchanging the order of the cascade, we get different frequency responses. All of these circuits are amenable for IC fabrication, and have several advantages over the existing active RC simulation circuits for the purpose.

As an adaptive finite impulse response filtering algorithm, the maximum total correntropy (MTC) algorithm plays an important role in parameter estimation of the errors-in-variables model where both input and output signals are contaminated with impulsive noises. However, the MTC algorithm is difficult to obtain a sufficiently high estimation accuracy under impulsive noises because the MTC cost function contains second-order moments of the error signal and its first-order gradient is susceptible to large outliers in the input noise. In this paper, a maximum total fractional-order correntropy (MTFOC) cost function is proposed and then a fractional-order gradient based MTFOC adaptive filtering algorithm is developed to improve the estimation accuracy of MTC. Moreover, the local stability and computational complexity of the proposed algorithm are analyzed. Simulation results indicate that the estimation accuracy and robustness of the MTFOC algorithm are superior to previous algorithms in both Gaussian mixture noise environments and (alpha )-stable distribution noise environments.

Efficiently scanning for space signals and accurately detecting them from noisy environment is essential in space communication. Various unwanted interferences also present in space that may hamper the perfect detection process. This paper proposes a novel near-field circular beamformer (NCB) that will perfectly detect the desired source signal from any direction and position in space. To improve the robustness of NCB against Direction of Arrival (DOA) error, distance error, unwanted interferences and noises, this work also offers robust NCBs (RNCB) using robust Optimal Diagonal Loading (ODL) and Variable Diagonal Loading (VDL) techniques. While searching for wanted signal, the beamformer provides a primary lobe at the look direction and shows some secondary unwanted side lobes for noise and interference. Sometimes these undesired side lobe levels (SLL) become so severe that it may create conflict in locating the precise position of the desired source. To reduce these SLL, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) techniques have been applied to RNCB. The simulation results show that the optimized RNCB significantly diminishes the objectionable SLL of non-optimized RNCB by choosing appropriate weight vector of antenna array without affecting the other antenna parameters. Artificial Neural Network (ANN) have also been used to predict the weight vector for minimum SLL.

This paper presents a methodology for finding a pseudo-upper or lower triangular state-space realization (SSR) from an incommensurate fractional-order transfer function. This SSR is obtained for a particular case of incommensurate fractional-order systems that can be represented by pseudo-upper or lower triangular multi-order state-space equations, which are derived by drawing the block diagram of the transfer functions. The obtained realization is very similar to the controllability canonical form for ordinary transfer functions. It is demonstrated that the obtained realization is controllable. Thus, the state feedback controllers can be systematically designed for these systems.

Research in the domain of digital audio tampering detection has advanced significantly with the use of Electrical Network Frequency (ENF) analysis, presenting notable benefits for crime prevention and the enhancement of judicial integrity. However, the existing methodologies, particularly those analyzing ENF phase and frequency, are impeded by data clutter, redundancy, and incompatibilities with standard classification algorithms, leading to decreased detection efficacy. This study proposes a novel methodology employing Discriminant Component Analysis (DCA) for the fusion of ENF features, aiming to address these issues directly. By analyzing the distinct characteristics of ENF phase and frequency spectra, our approach uses DCA to merge these features effectively. This fusion not only amplifies the correlation between the features of phase and frequency but also simplifies the feature space through efficient dimensionality reduction. Additionally, to bridge the gap with traditional classification methods, we introduce a cascaded deep random forest algorithm, designed for intricate representational learning of the fused features. This sequential processing enhances the precision of our classification model significantly. Experimental results on both the Carioca and New Spanish public datasets demonstrate that our approach surpasses current state-of-the-art methods in terms of accuracy and robustness, establishing its superiority in the field of digital audio tampering detection. By integrating the DCA algorithm to accentuate feature uniqueness and maximize inter-feature correlation, alongside advanced representational learning via the deep random forest algorithm, our methodology markedly improves the accuracy of digital audio tampering detection.

In this paper, we study several tighter uncertainty principles (UPs) for the non-isotropic angular Stockwell transform (NAST). By employing the polar coordinate representation of signal functions, we establish the tighter Heisenberg-Weyl UPs in spatial and directional settings, the tighter concentration Heisenberg-Weyl UP of position and scale concentration, as well as the tighter local-type Heisenberg-Weyl UP for the NAST. Quantitative and qualitative analysis indicates that our results give rise to a new understanding for the NAST with more precise lower bounds so as to offer more valuable insights for signal processing and image analysis.

Switching-mode power amplifiers have provided unprecedented opportunities for modern wireless communication technology. However, its single-ended structure suffers from significant harmonic interferences on signal transmission; in some cases, higher return loss caused by the impedance mismatch may damage or even destroy the device directly. Here it shows, analytically and experimentally, a broadband push–pull parallel-circuit (PC) Class-E power amplifier (PA) which can present high efficiency and flatness gain over a wide frequency range. Based on the broadband capability of the proposed structure, a highly-efficient push–pull PC Class-E PA combining single reactance compensation technique and Chebyshev low-pass impedance matching network (MN) is designed and fabricated. Experimental results demonstrated that a drain efficiency of 93.71–94.62% operating from 7.0 to 9.4 MHz, as well as output power (P_out) of 42.1–44.57 dBm and power gain of 14.1–16.57 dB are obtained. The measurement results show good agreements with the simulation results, which may be a potential candidate for electronic article surveillance (EAS) applications operating in the high-frequency band.

This paper is concerned with the event-triggered control problem for Roesser model-based two-dimensional (2D) Markov jump systems with multiple communication channels. In order to optimize the utilization of communication resources, an event-triggered rule (ETR) is proposed based on the structural characteristics of the 2D Markov jump plant. In addition, a stochastic communication protocol (SCP) is utilized to schedule transmissions from the controller to the actuators. Under the ETR and the SCP, an asynchronous state-feedback control scheme is formulated, allowing for mode mismatches between the controller and the 2D plant. A sufficient condition on both stochastic stability and a predefined ({mathcal {H}}_{infty }) disturbance-attenuation performance of the closed-loop 2D system is established. Building upon this condition and linear matrix inequalities, a computationally efficient method is developed for designing the required state-feedback controller gains. Finally, the feasibility and effectiveness of the proposed event-triggered control approach are demonstrated by the application of a simulation example involving the Darboux equation.