An updated definition of group delay bandwidth in analog filters is introduced in this work. Unlike existing definitions, this new definition considers simultaneously the value of the group delay and filter gain, leading to minimized distortion in the filter output. In addition, it offers the capability of handling wide-band signals without introducing errors in the shape of their envelopes. Selected first- and second-order filters are studied and simulation results are provided to validate the efficiency of the new definition. © 2024 The Author(s). International Journal of Circuit Theory and

Impedance spectroscopy has become a standard electroanalytical technique to study (bio)electrochemical and physiological systems. From an instrumentation point of view, the measurement of impedance can be carried out either in the frequency domain using the classical frequency sweep method or in the time domain using a variety of broadband signals. While time-domain techniques can be implemented with relatively simple hardware and can achieve faster acquisition time, they are still not that popular because of their lower accuracy and modularity. In this work we present a method and an

Fractional-order Butterworth filters of order 1 + (Formula presented.) (0

Electrochemical Impedance Spectroscopy (EIS) has become an increasingly important diagnostic and monitoring tool in many industries. An obstacle that arises when employing EIS in low and ultra low sub-Hz frequencies is the long measurement time associated with using the conventional frequency-sweep method. One possible solution to this problem is to use wide-band signals that cover at once the entire frequency range of interest. In this work, we explore and validate the use of such a signal obtained from the Rudin-Shapiro polynomial over the frequency range 10 mHz to 10 Hz. The proposed signal

Electrochemical capacitors are a class of energy devices in which complex mechanisms of accumulation and dissipation of electric energy take place when connected to a charging or discharging power system. Reliably modeling their frequency-domain and time-domain behaviors is crucial for their proper design and integration in engineering applications, knowing that electrochemical capacitors in general exhibit anomalous tendency that cannot be adequately captured with the traditional RC-based models. In this study, we first review some of the widely used fractional-order models for the

Deep Neural Networks (DNNs) are computationally and memory intensive, which present a big challenge for hardware, especially for resource-constrained devices such as Internet-of-Things (IoT) nodes. This paper introduces a new method to improve DNNs performance by fusing approximate computing with data reuse techniques for image recognition applications. First, starting from the pre-Trained network, then the DNNs weights are approximated based on the linear and quadratic approximation methods during the retraining phase to reduce the DNN model size and number of arithmetic operations. Then, the

Decision trees are powerful tools for data classification. Accelerating the decision tree search is crucial for on-the-edge applications with limited power and latency budget. In this article, we propose a content-addressable memory compiler for decision tree inference acceleration. We propose a novel 'adaptive-precision' scheme that results in a compact implementation and enables an efficient bijective mapping to ternary content addressable memories while maintaining high inference accuracies. We also develop a resistive-based functional synthesizer to map the decision tree to resistive

Complex-order controllers are a generalized version of conventional integer-order controllers and are known to offer greater flexibility, better robustness, and improved system performance. This paper discusses the design of complex-order PI/PID controllers to control the speed of an induction motor drive and an electric vehicle. The speed-tracking performance of the complex-order controllers is compared with fractional-order controllers and conventional integer-order controllers. Implementing complex-order controllers is challenging due to commercial complex-order fractance element

Large deep neural network (DNN) models are computation and memory intensive, which limits their deployment especially on edge devices. Therefore, pruning, quantization, data sparsity and data reuse have been applied to DNNs to reduce memory and computation complexity at the expense of some accuracy loss. The reduction in the bit-precision results in loss of information, and the aggressive bit-width reduction could result in noticeable accuracy loss. This paper introduces Scaling-Weight-based Convolution (SWC) technique to reduce the DNN model size and the complexity and number of arithmetic

We report on the electrical, optical and physical properties of Cu2ZnSnSe4solar cells using an absorber layer fabricated by selenization of sputtered Cu, Zn and Cu10Sn90multilayers. A maximum active-area conversion efficiency of 10.4% under AM1.5G was measured with a maximum short circuit current density of 39.7 mA/cm2, an open circuit voltage of 394 mV and a fill factor of 66.4%. We perform electrical and optical characterization using photoluminescence spectroscopy, external quantum efficiency, current-voltage and admittance versus temperature measurements in order to derive information