Circuits, Systems, and Signal Processing

Field-programmable gate arrays (FPGAs) are one attractive hardware platform for computing the eigenvalue decomposition of low-dimensional symmetric matrices. For this, one popular method is using the parallel Jacobi algorithm based on coordinate rotations digital computer (CORDIC). We here present a novel efficient FPGA architecture for computing the eigenvalue decomposition, whose main idea is from the fact that rotation matrices in Jacobi’s method belong to a category of special sparse matrices. Based on the above characteristic, matrix multiplications in the parallel Jacobi algorithm can be performed by FPGA efficiently. In addition, we provide one solution for Jacobi’s method to decompose the complex Hermitian matrix. Then, our proposed design is compared with state-of-the-arts on one Xilinx XC7V690T FPGA. Due to the high real-time requirement, we finally take the subspace-based direction of arrival (DOA) estimation in wireless communication as an application example.

This paper discusses the parameter estimation problems of nonlinear output error autoregressive systems and presents a data filtering-based multi-innovation stochastic gradient algorithm for improving the parameter estimation accuracy of the stochastic gradient algorithm by combining the multi-innovation identification theory and the data filtering technique. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm. The simulation results confirm this conclusion.

In this paper, we propose two novel recovery schemes for complex domain compressive sensing. Firstly, we present a new strategy to separate the real and imaginary parts of a complex signal for $$\ell _{1}$$ minimization. While the method is simple, simulation results show that it is quite efficient because it reduces the sampling rate. Secondly, the least squares (LS) sub-problem is a key part of the orthogonal matching pursuit (OMP) algorithm and accounts for a large part of the computational load. We employ the Landweber algorithm to efficiently solve the LS problem. Furthermore, we propose four new parameter options to accelerate the convergence. Our numerical experiments show that our method is competitive with the pseudo-inverse.

A method is proposed for time delay estimation (TDE) from mixed source (speaker) signals collected at two spatially separated microphones. The key idea in this proposal is that the crosscorrelation between corresponding segments of the mixed source signals is computed using the outputs of single frequency filtering (SFF) obtained at several frequencies, rather than using the collected waveforms directly. The advantage of the SFF output is that it will have high signal-to-noise ratio regions in both time and frequency domains. Also it gives multiple evidences, one from each of the SFF outputs. These multiple evidences are combined to obtain robustness in the TDE. The estimated time delays can be used to determine the number of speakers present in the mixed signals. The TDE is shown to be robust against different types and levels of degradations. The results are shown for actual mixed signals collected at two spatially separated microphones in a live laboratory environment, where the mixed signals contain speech from several spatially distributed speakers.

In this paper, a new technique for weak signal detection using stochastic resonance (SR) with approximated fractional integrator (AFI) has been investigated. To improve the performance of weak signal detector, AFI is convolved with noisy samples which (i.e., AFI) attenuates the noise because of its low-pass filtering nature. SR noise ( i.e., some fixed amount of noise) is added externally, which causes further improvement in detection. The external noise under SR has been obtained in Neyman–Pearson (NP) framework. For comparison, receiver operating characteristic curve has been analysed. The parameters, probability of detection ( $$P_D$$ ) and deflection coefficient ratio are also calculated at a fixed value of probability of false alarm ( $$P_{\mathrm{FA}}$$ ). It has been observed that the performance of the proposed detector is better or comparable to most of the state-of-the-art techniques.

This paper presents the VLSI architectures for three-level correlator design based on 1-μm CMOS technology. The architecture performs very high speed, real-time, three-level cross-correlation of signals. Two architectures, one for serial incoming samples of signals (serial data) and the other for stored signal samples (parallel data), are described in the paper.

In this work, a low-power voltage reference circuit has been developed using the principle that a thermal compensation of the threshold voltage of a diode-connected nMOSFET can be obtained by using the PTAT current. The proposed circuit is designed using $$0.18\,\upmu \hbox {m}$$ standard CMOS technology for the industrial temperature range of $$-40$$ to $$+85\,^\circ \hbox {C}$$ . The measurements have been done over a set of 10 samples in the given temperature range. The measured results show that the proposed circuit is capable of working in the supply voltage range of 1.2–1.8 V with the mean line sensitivity and total current consumption of 0.64%/V and $$115.4\,\hbox {nA}$$ , respectively, at $$22.5\,^\circ \hbox {C}$$ . The measured mean reference voltage obtained from the circuit is 435 mV with the mean temperature coefficient of $$67\,\hbox {ppm}{/}^\circ \hbox {C}$$ . The measured noise density at $$22.5\,^\circ \hbox {C}$$ without any filtering capacitor is $$42\,\upmu \hbox {V}{/}\sqrt{\text {Hz}}$$ at 100 Hz. The active area of the circuit is $$0.01008\,\hbox {mm}^2$$ .

This paper is concerned with the problem of stability and robust H ∞ control for 2-D stochastic systems with parameter uncertainties and sector nonlinearities. The class of systems under investigation is described by the 2-D state-space Roesser model. Our attention is focused on the design of a state feedback controller for 2-D stochastic system with sector nonlinearity, such that the closed-loop 2-D stochastic system is asymptotically stable and has a prescribed H ∞ disturbance attenuation performance. First, a sufficient condition is established for the 2-D nonlinear stochastic systems to be asymptotically stable. Then, we extend the bounded real lemma for 2-D systems to 2-D stochastic systems with sector nonlinearities. Based on this lemma, solvability conditions for the H ∞ control of 2-D nonlinear stochastic systems in the form of LMIs (linear matrix inequalities) are derived. A numerical example illustrates the effectiveness of the proposed results.