Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

https://www.mdpi.com/2079-4991/10/8/1437/pdf

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application.

Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required.

Computer vision based classroom attendance management system-with speech output using LBPH algorithm

Power consumption plays a crucial role in the design of portable wireless communication devices, as it has a direct influence on the battery weight and volume required for operation. This article p...

ASIC Implementation of Linear Equalizer Using Adaptive FIR Filter

Filtering techniques have been elaborated in the HA field to improve signal clarity and enhance the hearing capacity of deaf people. However, public sounds are highly noisy, so filtering those signals is not an easy task. Hence, the present article has aimed to develop a novel Ant Lion based power NoiseVariable Bandwidth Filter (ALPN-VBF) for the HA applications. Here, the proposed optimized power efficient filter has incorporated several functions like de-noising and frequency tuning based on the word features. Here, the signal’s noise has been removed with the maximum possible range with the help of High-pass-Filter (HPF) and low-pass filter (LPF). Finally, the developed model is tested with a few audiograms, and the filter parameters have been analyzed and compared with other models. The testing results have proved that the designed filter is better in frequency tuning and signal transmission than the previous approaches by attaining less delay and reduced power consumption rate. Keywords—Hearing aid system; variable bandwidth filter; audiograms; matching error; power consumption; signal filtering

/pdf/vhdl-based-design-of-an-efficient-hearing-aid-filter-using-2r42brv3.pdf

VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter

Low Area High-speed Hardware Implementation of Fast FIR Algorithm for Intelligent Signal Processing application in Complex Industrial Systems

This brief presents a decimation filter for hearing aid application using distributed arithmetic (DA) approach. In this paper, we propose a reconfigurable offset-binary code (OBC) DA based finite impulse response (FIR) filter with a shared look-up table (LUT) updating scheme. The size of the LUTs in DA increases exponentially with the order of filters. Shared LUT based DA structure is a solution to reduce this large memory requirement for higher order filters. The proposed shared LUT updating scheme uses LUT partitioning in which coefficients are spilt into small length vectors and it ensures a drastic reduction in the size of LUTs. The proposed DA filter is synthesized on CMOS 90 nm technology using Synapsis ASIC Design Compiler. The proposed design achieves high speed at a reduced area-delay product (ADP) when compared with recent designs. The proposed architecture is implemented and tested on Virtex 5vsx95-1ff1136 FPGA and the results show that the proposed design involves less number of slices and offers high speed than existing designs. A three-stage decimation filter of hearing aids is designed with the proposed FIR filter and is implemented on the target device by Matlab simulink and Xilinx system generator.

High speed low area OBC DA based decimation filter for hearing aids application

This paper presents a low power architecture for linear periodically time varying (LPTV) filter by decomposing into finite computational threads. An N-tap, M period LPTV filter architecture is minimized into a single LTI filter by enabling the thread decomposition (TD) of the LPTV filtering operation. The proposed architecture is a generalization to the transposed form structure. A new insight, derived from TD enabled this generalization, which is otherwise not possible. Implementing the LPTV filter with multiplier less functional blocks based on binary common sub-expression elimination (BCSE) algorithm reduced the critical path delay. Experimental results show that the proposed design offers 48.9% reduction in area delay product (ADP) and 14.2% reduction in power delay product (PDP). The LPTV filtering operations in various applications can be realized with the proposed architecture. This work is the first attempt to the ASIC implementation of an efficient architecture for LPTV filter.

ASIC Implementation of Linear Periodically Time Varying Filter by Thread Decomposition

Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth an attempt to concentrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm (IWDA) combined with an efficient machine learning approach-Support Vector Regression (SVR) for task failure prognostication which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications. The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows. The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques. 

Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

Enhanced with digital capabilities over the preceding ten years, especially on the internet, there has been a surge in cyber attackers attempting to profit from consumers’ private information. One of the most recent approaches to solving this problem is to devise a model to help us detect acts of intrusion in a network known as Intrusion Detection Systems (IDS). There has been a surge in the development of advanced artificial algorithms in recent years, and adding their intelligence makes it much easier to prevent network intrusions. In general, Deep learning methods outperform machine learning techniques in terms of performance metrics for detecting network intrusions. This paper suggests the implementation of five distinct variants of deep learning models that have been extensively trained with the UNSW NB-15 benchmark dataset by optimizing different hyperparameters to provide improved intrusion detection. Amongst variants, the Bi-Directional Long-Short Term Memory model with ADAM optimizer is efficient in intrusion detection with the highest accuracy of 95.28%.

Sriadibhatla Sridevi

Papers

High speed low area OBC DA based decimation filter for hearing aids application

ASIC Implementation of Linear Periodically Time Varying Filter by Thread Decomposition

Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

Analysis of Hybrid Deep Learning Models for Efficient Intrusion Detection