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

Nowadays, Machine Learning is commonly integrated into most daily life applications in various fields. The K Nearest Neighbor (KNN), which is a robust Machine Learning algorithm, is traditionally used in classification tasks for its simplicity and training-less nature. Hardware accelerators such as FPGAs and ASICs are greatly needed to meet the increased requirements of performance for these applications. It is well known that ASICs are non-programmable and only fabricated once with high expenses, this makes the fabrication of a complete chip for a specific classification problem inefficient

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

Industrial wastewater often contains considerable amounts of toxic pollutants that would endanger public health and the environment. In developing countries, these toxins are often discharged into natural ecosystems without pretreatment as it requires costly treatment processes, which causes long-term harmful socioeconomic impacts. Employing wastewater treatment plants using physical, biological, and chemical methods to clean the wastewater is considered by many nations the answer to the environmental crises. The treated water could be used for targeting the irrigation systems in its majority

Crystal violet is a basic dye that is widely used by various industries, such as textiles and paints. These industries discharge their effluents, contaminated with crystal violet, into water streams, and these effluents have an adverse effect on aquatic organisms, the environment, and human health. Crystal violet is a basic dye that is widely used by various industries, such as textiles and paints. These industries discharge their effluents, contaminated with crystal violet, into water streams, and these effluents have an adverse effect on aquatic organisms, the environment, and human health

With the end of Moore's law, new paradigms are investigated for more scalable computing systems. One of the promising directions is to examine the data representation toward higher data density per hardware element. Multiple valued logic (MVL) emerged as a promising system due to its advantages over binary data representation. MVL offers higher information processing within the same number of digits when compared with binary systems. Accessing memory is considered one of the most power- and time-consuming instructions within a microprocessor. In the quest for building an entire ternary

A novel kind of non-integer order bilinear filters, named double-order bilinear filters, is introduced in this work. They are based on the employment of two non-integer orders, offering the maximum design flexibility in comparison with their fractional-order and power-law counterparts. An attractive offered benefit is that this is achieved without increasing the circuit complexity, since the proposed structure is capable of realizing all non-integer kinds of filters. Two design examples are provided, where it is shown that lead/lag compensators utilized in control applications and low/high

In this chapter, a new technique has been proposed for reducing the harmonic content of a three-phase PWM rectifier connected to the networks with a unit power factor and also providing decoupled control of the active and reactive instantaneous power. This technique called direct power control (DPC) is based on artificial neural network (ANN) controller, without line voltage sensors. The control technique is based on well-known direct torque control (DTC) ideas for the induction motor, which is applied to eliminate the harmonic of the line current and compensate for the reactive power. 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

Non-integer order filters can be derived from a generalized structure presented in this work. More specifically, fractional-order and power-law filters of single- or double-order are special cases of non-integer order filters with three degrees of freedom and can be implemented using a Current Feedback Operational Amplifier as the active element. The transfer function is formed as a ratio of two impedances which can be synthesized using Foster or Cauer RC networks. A curve-fitting based technique is employed for approximating the magnitude and phase of each impedance. The behavior of the