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As technology size scales down, magnetic tunnel junctions (MTJs) as a promising technology are becoming more and more sensitive to process variation, especially in oxide barrier thickness. Process variation particularly affects the cell resistance and the critical switching current for the smaller dimensions. This article proposes an MTJ cell with one free and two pinned layers, which highly improves the process variation robustness. By employing the spin transfer torque (STT)-spin-Hall effect (SHE) switching method, our proposed MTJ cell improves the switching speed and lowers the switching power consumption. Per simulations, an MRAM cell built with the proposed MTJ cell offers up to 36% lower total power consumption, up to 32% higher write performance, and 35% higher read performance over the previous state-of-the-art MRAMs. Also, the error rate resulting from process variation is 1.4%, which is about one-tenth of the best examples reported in the literature. [ABSTRACT FROM AUTHOR]

Sound source localization has always been one of the most challenging subjects in different fields of engineering, one of the most important of which being tracking of flying objects. This article focuses on sound source localization using fuzzy fusion and a beamforming method. It proposes a new fuzzy-based algorithm for localizing a sound source using distributed sensor nodes. Eight low-cost sensor nodes have been constructed in this study each of which consists of a microphone array to capture sound waves. Each node is able to record audio signals synchronously on an SD card to evaluate different algorithms offline. However, the sensor nodes are designed to be able to estimate the location of the sound source in real-time. In the proposed algorithm, every node estimates the direction of the sound source. Moreover, a calibration algorithm is used for extracting the orientation of sensor nodes to calibrate the estimated directions. The calibrated directions are fuzzified and then used for localizing the sound source by fuzzy fusion. An experiment was designed based on localizing a flying quadcopter as a moving sound source to evaluate the performance of the proposed algorithm. The flying trajectory was then estimated and compared with the target trajectory extracted from the GPS module mounted on the quadcopter. Comparing the estimated sound source with the target location, a mean distance error of ${6.03}{m}$ was achieved in a wide-range outdoor environment with the size of ${240}\times {160}\times {80} \,\,{m}^{{3}}$. The achieved mean distance error is reasonable regarding the mean precision of the GPS module. The practical results illustrate the effectiveness of the proposed approach in localizing a sound source in a wide-range outdoor environment. [ABSTRACT FROM AUTHOR]

This paper presents a new inference algorithm for active learning method (ALM). ALM is a pattern-based algorithm for soft computing, which uses the ink drop spread (IDS) algorithm as its main engine for feature extraction. In this paper, a fuzzy number is extracted from each IDS plane rather than from the narrow path and the spread, as in previous approaches. This leads to a significant reduction in the hardware required to implement the inference part of the algorithm and real-time computation of the implemented hardware. A modified version of the memristor crossbar structure is used to solve the problem of varying shapes of the ink drops, as reported in previous studies. In order to compare performance of the algorithm and the proposed hardware with the one proposed in our previous work, two functions that are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as the previous one in modeling with reduced hardware complexity. [ABSTRACT FROM PUBLISHER]

In this paper, first we present a new explanation for the relationship between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. This shows us that neural networks are working in the same way as logical circuits when the connection between them is through the fuzzy logic. However, the main difference between them is that logical circuits can be constructed without using any kind of optimization-based learning methods. Based on these results, we propose a new neuro-fuzzy computing system. As verified by simulation results, it can effectively be implemented on the memristor crossbar structure and therefore can be a good approach to emulating the computing power of human brain. One important feature of the designed hardware is that it has the potential to be directly trained using the Hebbian learning rule and without the need for any optimization. The system is also very capable of dealing with a large number of input-out training data without facing problems like overtraining or sensitivity to outliers. [ABSTRACT FROM AUTHOR]

Ink drop spread (IDS) is the engine of an active learning method, which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system that is subjected to modeling. In spite of its excellent potential to solve problems such as classification and modeling compared with other soft-computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of the IDS method that is based on the memristor crossbar structure. In addition to simplicity, being completely real time, having low latency, and the ability to continue working properly after the occurrence of power failure are some of the advantages of our proposed circuit. Moreover, some of operations in the IDS method have fuzzy nature, and as we will show at the end of this paper, updation of rules in the IDS structure and spiky neural networks are very similar. Therefore, IDS can be considered as a new fuzzy implementation of artificial spiky neural networks as well. [ABSTRACT FROM AUTHOR]