Design, Analysis, and Application of Flipped Product Chaotic System

In this paper, a novel method is proposed to build an improved 1-D discrete chaotic map called flipped product chaotic system (FPCS) by multiplying the output of one map with the output of a vertically flipped second map. Two variants, each with nine combinations, are shown with trade-off between computational cost and performance. The chaotic properties are explored using the bifurcation diagram, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient. The proposed schemes offer a wider chaotic range and improved chaotic performance compared to the constituent maps and several prior works of similar nature. Wide chaotic window and improved chaotic complexity are two desired characteristics for several security applications as these two characteristics ensure enhanced design space with elevated entropic properties. We present a general Field-Programmable Gate Array (FPGA) design framework for the hardware implementation of the proposed flipped-product schemes and the results show good qualitative agreement with the numerical results from MATLAB simulation. Finally, we present a new Pseudo Random Number Generator (PRNG) using the two variants of the proposed chaotic map and validate their excellent randomness property using four standard statistical tests, namely NIST, FIPS, TestU01, and Diehard

Cascading CMOS-Based Chaotic Maps for Improved Performance and Its Application in Efficient RNG Design

We present a general framework for improving the chaotic properties of CMOS-based chaotic maps by cascading multiple maps in series. Along with two novel chaotic map topologies, we present the 45 $nm$ designs for four CMOS-based discrete-time chaotic map topologies. With the help of the bifurcation plot and three established entropy measures, namely, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient, we present an extensive chaotic performance analysis on eight unique map circuits (two under each topology) to show that under certain constraints, the cascading scheme can significantly elevate the chaotic performance. The improved chaotic entropy benefits many security applications and is demonstrated using a novel random number generator (RNG) design. Unlike conventional mathematical chaotic map-based digital pseudo-random number generators (PRNG), this proposed design is not completely deterministic due to the high susceptibility of the core analog circuit to inevitable noise that renders this design closer to a true random number generator (TRNG). By leveraging the improved chaotic performance of the transistor-level cascaded maps, significantly low area and power overhead are achieved in the RNG design. The cryptographic applicability of the RNG is verified as the generated random sequences pass four standard statistical tests namely, NIST, FIPS, Diehard, and TestU01

Semiconductor Device Modeling and Simulation for Electronic Circuit Design

This chapter covers different methods of semiconductor device modeling for electronic circuit simulation. It presents a discussion on physics-based analytical modeling approach to predict device operation at specific conditions such as applied bias (e.g., voltages and currents); environment (e.g., temperature, noise); and physical characteristics (e.g., geometry, doping levels). However, formulation of device model involves trade-off between accuracy and computational speed and for most practical operation such as for SPICE-based circuit simulator, empirical modeling approach is often preferred. Thus, this chapter also covers empirical modeling approaches to predict device operation by implementing mathematically fitted equations. In addition, it includes numerical device modeling approaches, which involve numerical device simulation using different types of commercial computer-based tools. Numerical models are used as virtual environment for device optimization under different conditions and the results can be used to validate the simulation models for other operating conditions

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity

Recent advancements in reservoir computing research have created a demand for analog devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional pre-processing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass it through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved a normalized mean square error of 0.0015 using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%

Bangladesh and SAARC Countries: Bilateral Trade and Flaring of Economic Cooperation

The primary purpose of this study is to observe the economic andinternational trade relationships between Bangladesh and SAARCcountries during the Fiscal Years 2015-2016 to 2020-2021. Thisresearch uses panel data to examine the relationship betweenthe nature and direction of exports and economic growth.The researcher found that economic cooperation with SAARCcountries impacted economic growth, but there was insufficientexport adaptation. The study also showed that economic relationshave strengthened in recent epochs, and bilateral trade hasincreased compared to previous years with SAARC countries.The obtained results also showed that Bangladesh has sufferedfrom a long-standing trade imbalance with India and is in agood position with Afghanistan, Sri Lanka, Bhutan, Maldives,Nepal, and Pakistan. Our findings are beneficial for internationaltrade stakeholders and suggest that steps should be taken toincrease exports to reduce the trade deficit. Concern authoritiesshould be more aware of the expansion of trade facilitation andinfrastructure development.JEL Classification: F5, F18, F23, F50, M1

Highly Sensitive Room-Temperature Sensor Based on Nanostructured K2W7O22 for Application in the Non-Invasive Diagnosis of Diabetes

Diabetes is one of the most rapidly-growing chronic diseases in the world. Acetone, a volatile organic compound in exhaled breath, shows a positive correlation with blood glucose and has proven to be a biomarker for type-1 diabetes. Measuring the level of acetone in exhaled breath can provide a non-invasive, low risk of infection, low cost, and convenient way to monitor the health condition of diabetics. There has been continuous demand for the improvement of this non-invasive, sensitive sensor system to provide a fast and real-time electronic readout of blood glucose levels. A novel nanostructured K2W7O22 has been recently used to test acetone with concentration from 0 parts-per-million (ppm) to 50 ppm at room temperature. The results revealed that a K2W7O22 sensor shows a sensitive response to acetone, but the detection limit is not ideal due to the limitations of the detection system of the device. In this paper, we report a K2W7O22 sensor with an improved sensitivity and detection limit by using an optimized circuit to minimize the electronic noise and increase the signal to noise ratio for the purpose of weak signal detection while the concentration of acetone is very low

Highly Sensitive Room Temperature Sensor Based on Nanostructured K2W7O22 for Diagnosis Diabetes

Diabetes is one of the most rapidly-growing chronic diseases in the world. Acetone, a volatile organic compound in exhaled breath, shows a correlation with blood glucose and has proven to be a biomarker for type-1 diabetes. Measuring the level of acetone in exhaled breath can provide a non-invasive, low risk of infection, low cost, and convenient way to monitor the health condition of diabetics. There has been continuous demand for the improvement of this non-invasive, sensitive sensor system to provide a fast and real-time electronic readout of blood glucose levels. A novel nanostructured K2W7O22 (potassium tungsten oxide) has been recently used to test acetone with concentration from 0 parts-per-million (ppm) to 50 ppm at room temperature. This thesis work involves in designing K2W7O22 sensor with an improved sensitivity and detection limit .For future work, a device has proposed to detect low concentration of acetone for practical use purpose

Bio-Inspired Memory Device Based Physical Reservoir Computing System to Solve Temporal and Classification Problems

In recent years, technological advancements in the field of computing have been limited by the slowing down of Moore’s law, which predicts the doubling of the number of transistors on a microchip every two years. The limitations of traditional solid-state electronics have become increasingly evident, as it is becoming difficult to increase the number of transistors while maintaining their reliability and performance. The breakdown of Dennard Scaling, which describes the power-density relationship in CMOS transistors, and the von Neumann bottleneck, which refers to the limited bandwidth between the central processing unit (CPU) and memory, have further exacerbated the situation. Moreover, the ever-increasing computing demands have led researchers to explore alternative computing mechanisms, such as neuromorphic computing. In this context, there is a growing interest in developing novel memory devices that possess non-linear current-voltage characteristics and inherent memory properties, such as bio-inspired memory devices. These devices are inspired by the functioning of biological synapses in the brain, which enable neurons to communicate with each other and form complex networks. Bio-mem devices use biomolecules, such as proteins or DNA, as their active material and can exhibit a range of non-linear behaviors, including hysteresis, threshold switching, and negative differential resistance. Additionally, they have inherent memory properties that allow them to retain their state even after the power is turned off. The unique features of bio-mem devices make them a promising candidate for solving classification and temporal pattern recognition tasks. Unlike their solid-state counterparts, bio-mem devices feature a similar structure, switching mechanism, and ionic transport modality as biological synapses, while consuming considerably lower power. The use of bio-mem devices in computing can lead to the development of more efficient and powerful computing systems that can handle complex tasks more effectively. One promising computing paradigm that has emerged in recent years is Reservoir Computing (RC), which is a type of recurrent neural network that uses a fixed, random, and sparse connectivity matrix called a ”reservoir.” The reservoir provides a non-linear mapping of the input data, and the output is obtained by training a linear readout layer using a simple learning algorithm, such as ridge regression or support vector regression. RC has been shown to be effective in solving a range of classification and temporal problems, such as speech recognition, time-series prediction, and image classification. This work aims to explore the use of biomolecular devices in RC to solve classification and temporal problems, which could lead to the development of more efficient and powerful computing systems

Highly Sensitive Room-Temperature Sensor Based on Nanostructured K₂W₇O₂₂ for Application in the Non-Invasive Diagnosis of Diabetes

Diabetes is one of the most rapidly-growing chronic diseases in the world. Acetone, a volatile organic compound in exhaled breath, shows a positive correlation with blood glucose and has proven to be a biomarker for type-1 diabetes. Measuring the level of acetone in exhaled breath can provide a non-invasive, low risk of infection, low cost, and convenient way to monitor the health condition of diabetics. There has been continuous demand for the improvement of this non-invasive, sensitive sensor system to provide a fast and real-time electronic readout of blood glucose levels. A novel nanostructured K2W7O22 has been recently used to test acetone with concentration from 0 parts-per-million (ppm) to 50 ppm at room temperature. The results revealed that a K2W7O22 sensor shows a sensitive response to acetone, but the detection limit is not ideal due to the limitations of the detection system of the device. In this paper, we report a K2W7O22 sensor with an improved sensitivity and detection limit by using an optimized circuit to minimize the electronic noise and increase the signal to noise ratio for the purpose of weak signal detection while the concentration of acetone is very low

A Belief Rule Based Expert System to Assess Clinical Bronchopneumonia Suspicion

Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or interstitial are affected. Usually the diagnosis of Bronchopneumonia is carried out using signs and symptoms of this disease, which cannot be measured since they consist of various types of uncertainty. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, development and application of an expert system for assessing the suspicion of Bronchopneumonia under uncertainty. The Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than from the results generated by a manual system.A belief-rule-based DSS to assess flood risks by using wireless sensor network