Miracle-dz/UniFAR-FTUs huggingface dataset:
Memristor-based Offset Cancellation Technique in Analog Crossbars

Analog computing platforms have been a popular and promising research area that suggest efficient ways of computation compared to its digital counterparts. Memristor based crossbars drew attention by computing the vector-matrix calculation intensive tasks such as Artificial Intelligence (AI) and Machine Learning (ML) in one time step. Although they provide an energy efficient way of computing these tasks, analog computation in general suffers from non-idealities and systematic errors in the circuitry, which could degrade the performance and accuracy significantly. One of the issues is the random offset associated with the op-amps in the system resulting from the process and mismatch variations. In this paper, a novel technique is offered to reduce the negative effects of the random offset and increase the output accuracy. This newly proposed system uses minimum extra circuitry and additional power consumption and only requires the crossbar to be enlarged by two extra rows. The intrinsic issue of the analog crossbars, interconnect parasitics, must be incorporated into the problem, and a way to separate the offset and wire resistance issues from each other is offered. The functionality of the system has been shown with a case study in the results section where the op-amps have $\sigma_{offset}=3mV$. The effectiveness of the offered technique demonstrates a 6× better accuracy with the mitigation of the offset problem. The proposed method can be used in memristor and other analog crossbars to achieve a greater performance and thus improve their competitiveness.
Real Estate Property Price Estimator Using Machine Learning

Property price estimator is a mobile application by creating a model using machine learning to forecast the sale price of homes in a Bangalore. This model will be built using data on a variety of house features, such as location, size, age, and condition, as well as the sale prices of the houses. In order to commence, we have gathered and prepared the required data so that it is available for analysis. Managing missing or inaccurate numbers and making sure the data is consistent and appropriately reflects the properties and their sale prices may be involved in this. Next, we will perform exploratory data analysis (EDA) to better understand the relationships between the various house features and the sale prices. This involves visualizing the data and identifying patterns and trends that may be useful for building the prediction model. After having a good understanding of the data, next comes building and training the prediction model. This involves selecting and optimizing an appropriate machine learning algorithm, such as linear regression or a decision tree, as well as selecting and engineering relevant features for the model. The model will then be put through its paces using suitable metrics such as R squared and any necessary corrections will be made to increase its accuracy. Users can inquiry price by giving a series of features which describes their ideal house and can get the recommended price. This will be a responsive inquiry system that can greatly improve user's experience.
A Ripple-Based Constant On-Time Controlled DC-DC Buck Converter with Inductor Current Sensing Technique

This letter implements a ripple-based constant on-time (RBCOT) buck converter with a fast transient response fabricated in the TSMC $0.18 \mu \mathrm{m}$ CMOS process. The steady-state measurement shows that this chip can regulate output voltage from 0.9V to 1.8V while the input voltage is set at 3.3V and the output load current varies from 0.1A to 1A. The load transient response shows that when the output voltage is 0.9V, the undershoot voltage is 78m V and the overshoot voltage is 126m V. The settling time is $2.8\mu \mathrm{s}$ for a step-up load and $2.5\mu \mathrm{s}$ for a step-down load. The chip area is 0.922 mm2, The maximum efficiency is 92.32%. This work improves the traditional inductor current ramp compensation technique. Here, we utilize an accurate transconductance amplifier to amplify the inductor current, increasing system stability and efficiency. By adopting negative inductor current feedback to improve the transient response. Through the V2 controlled dual loop structures to eliminate the output dc voltage offset issues.
Wheat leaf disease detection using CNN in Smart Agriculture

Plant diseases are one of the most significant threats to the yield and quality of agriculture in the world. Conventional plant disease detection techniques rely heavily on expert diagnosis, which can easily lead to delays in crop disease control and field management. Therefore, early implementation of protective measures is the most effective way to control these diseases and minimize crop damage. To this end, a wheat leaf disease detection and classification method based on a Convolutional Neural Network model is proposed to increase the speed and accuracy of disease classification. The presented work used various image augmentation approaches to eliminate the overfitting problem. We achieved a testing accuracy of 94%. We also demonstrated the viability of our method for real-time detection of wheat disease in resource-constrained environments, where prompt disease detection and management are essential for sustainable agriculture.
Contract Cheating – Dead or Reborn?

Online question-answering sites such as Chegg and Bartleby made contract cheating affordable to a wider audience by outsourcing answer services to developing countries such as India and often providing answers within 30 minutes. GPT (Generative Pre-trained Transformer) language models can now give such answers in real-time and for free (or nearly free). This paper assesses the quality and promptness of answers provided by Chegg to a range of questions from CS1 and CS2 over a two-year period, comparing them to GPT-generated answers. Results demonstrate that GPT answers are of equal or better quality compared to those provided by human experts, and thus provide an equitable platform for students seeking answers. The paper considers some of the implications from a pedagogical standpoint.
Restraining effect of film thickness on the behaviour of amplified spontaneous emission from methylammonium lead iodide perovskite

The authors report amplified spontaneous emission (ASE) behaviour from methylammonium lead iodide (CH 3 NH 3 PbI 3 ) perovskite films of different thicknesses. The ASE threshold carrier density noticeably decreased with thickness, indicating the existence of different traps with perovskite films of smaller thicknesses. We attribute this behaviour to the presence of surface states, whose origin can result from different practical fabrication steps with samples of small thicknesses. The ASE threshold carrier density increased from 3.29 × 10 18 cm -3 at a film thickness of 650 nm to 7.73 × 10 18 cm -3 at a film thickness of 80 nm. This work warns that while decreasing the film thickness is of practical importance to reduce the ASE threshold pump current, e.g. in electrically driven light-emitting diodes, the solution processing of perovskites, newly re-discovered for their potential photonic and photovoltaic applications, can be a restraining factor. Band gap renormalisation (BGR) is also observed in the prepared films as a redshift in the ASE peak with increasing the pump power, and the BGR coefficient is estimated to be ~ 6.3 × 10 -8 eV cm.
Direct numerical simulation of axisymmetric turbulence

The dynamics of decaying strictly axisymmetric, incompressible turbulence is investigated using direct numerical simulations. It is found that the angular momentum is a robust invariant of the system. It is further shown that long-lived coherent structures are generated by the flow, associated with stationary solutions of the Euler equations. The structures obey relations in agreement with predictions from selective decay principles, compatible with the decay laws of the system. Two different types of decay scenarios are highlighted. The first case results in a quasi-two-dimensional flow with a dynamical behaviour in the poloidal plane similar to freely decaying two-dimensional turbulence. In a second regime, the long-time dynamics is dominated by a single three-dimensional mode.
AsteRISC: A Size-Optimized RISC-V Core for Design Space Exploration

The RISC-V open source instruction set architecture is a promising solution for applications related to low power embedded systems. This paper presents a configurable RISC-V processor architecture providing a compromise between the number of clock cycles required to execute an instruction, the maximum operating frequency, the resource utilization and the power consumption. This architectural flexibility enables the processor to be adapted to fit application constraints, on either FPGA or ASIC targets.
Intelligent Adaptive RRT* Path Planning Algorithm for Mobile Robots

In this paper a new variant of the widely used Rapidly exploring Random Tree (RRT*) algorithm is proposed. The main goal of this variant is to improve the efficiency of the generated path, in both computation time and the quality of the path. In a previous work by the authors, an improved version RRT*N was proposed, where a normal probability distribution was used to control the generation of the random nodes. The proposed variant utilizes an intelligent fuzzy logic system (FLS) to control the generation of the random nodes and commands the robot to the required target introducing an intelligent fuzzy adaptive RRT*N path planning approach (FA-RRT*N). The proposed approach not only reduced the time required about 29.5 % of the time required by the RRT*, but also generally resulted in shorter path about 63% of the path generated by the traditional RRT*.
An Immuno-Inspired Approach Towards Post-Processing of OCR Errors

Errors are part and parcel of Computer Vision applications like Optical Character Recognition(OCR). Unfortunately, the noise produced by these errors only proliferates further down the stages of Natural Language Processing pipelines. Among the reported works for post-processing of OCR texts, most involved Lexical approaches, Feature-based machine learning models, Merging OCR outputs, or using other language Models. This paper proposes an Isolated-Word-based approach to detect OCR errors that rely on the principles of the Artificial Immune System(AIS). The problem of OCR error detection is treated as a classification problem where OCR errors are treated as pathogens and correct words as host cells. The Negative Selection Algorithm is used to classify any new token as an OCR error (pathogen) or good term (host cell). A series of experiments illustrate that it is possible to construct such a system to help identify OCR errors independent of the language.
An Innovative Preamble Sub-Block Structure for Channel Estimation in FBMC/OQAM Systems

Due to the existence of imaginary interference in filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems, channel estimation technology is a difficult task. In this paper, an innovative preamble structure is suggested to increase the channel estimation accuracy. The suggested preamble structure employs five FBMC/OQAM symbols and every three adjacent subcarriers constitute a preamble sub-block (PSB). According to interference factors, the special pilots are designed for PSB, which can increase the central point’s pseudo-pilot power and therefore improve channel estimation performance. Experimental results testify that the suggested approach works better than its conventional counterparts.
An Advanced Intrusion Detection Algorithm for Network Traffic Using Convolution Neural Network

There has been rapid advancement in networks and technology over the past few years, and Internet service providers are now available anywhere in the world. The number of thefts has increased, and many current systems have been breached. As a result, the need for creating information security technology to spot new attacks has grown significantly. An Intrusion Detection System (IDS) combines machine learning and deep learning algorithms to discover network abnormalities, is an essential information security technology. The central concept of this study is to find the elusive assault bundle by using a high-level intrusion detection system with high network performance. Likewise, in this approach, the attack detection is completed. The proposed system has shown that empowering brings incredible precision.
Data-Driven Offline Optimization of Deep CNN models for EEG and ECoG Decoding

A better understanding of ElectroEncephaloGraphy (EEG) and ElectroCorticoGram (ECoG) signals would get us closer to comprehending brain functionality, creating new avenues for treating brain abnormalities and developing novel Brain-Computer Interface (BCI)-related applications. Deep Convolutional Neural Networks (deep CNNs) have lately been employed with remarkable success to decode EEG/ECoG signals. However, the optimal architectural/training parameter values in these deep CNN architectures have received little attention. In addition, new data-driven optimization methodologies that leverage significant advancements in Machine Learning, such as the Transformer model, have recently been proposed. Because an exhaustive search on all possible architectural/training parameter values of the state-of-the-art deep CNN model (our baseline model) decoding the motor imagery EEG and finger tension ECoG signals comprising the BCI IV 2a and 4 datasets, respectively, would require prohibitively much time, this paper proposes a model-based optimization technique based on the Transformer model for the discovery of the optimal architectural/training parameter values for that model. Our findings indicate that we could pick better values for the architectural/training parameters of the baseline model, enhancing the accuracy of the baseline model by 3.4% in the BCI IV 2a dataset and by 29.8% in the BCI IV 4 dataset.
Performance Comparison of Ovarian Tumour Classification Using Deep Neural Network

Ovarian tumour originates in ovaries posing a serious threat to women. As a result, it leads to abnormal cells which have the ability to spread to the other regions of the body. Ovarian tumour is a sort of risky improvement that impacts ovaries in females, and is hard to perceive at early the phase because of which it stays as one of the guideline wellsprings of illness end. The ultrasound imaging is the low cost and best to detect tumour in ovaries. To improve tumour detection accuracy in ultrasound ovarian images, enhanced preprocessing, image segmentation, feature extraction and feature selection, and classification method are proposed. The enhanced preprocessing, tumour segmentation, feature extraction and feature selection, and tumour classification methods have been analyzed to segment and classify the tumour effectively in ultrasound ovarian tumour images. The Anisotropic Diffusion Filter (ADF) has been applied for the preprocessing of the ultrasound ovarian image to improve the image quality for further processing such as segmentation, feature extraction and feature selection, and classification. The Improved Whale Search Optimization (IWSO) segmentation method has been applied to segment tumour in the ultrasound image efficiently. The Deep Neural Network (DNN) method has given more accuracy in classifying the ultrasound ovarian tumour image into benign or malignant. The DNN has high sensitivity, high specificity, high accuracy, high PPV, high NPV, low FPR, low FNR, low FDR, low time complexity, low error rate, and low false classification ratio during the tumour classification compared with the existing methods.
A Fully-Integrated LDO with Two-Stage Cross-Coupled Error Amplifier for High-Speed Communications in 28-nm CMOS

This paper presents a fully-integrated flipped-voltage-follower-based low-dropout regulator (LDO), with proposed high-gain two-stage cross-coupled error amplifier (XCEA). Besides, the effectiveness of bypass capacitors and diversified load capacitors is discussed. Consuming $\mathbf{170}-\boldsymbol{\mu} \mathbf{A}$ quiescent current and occupying area of 0.019 mm2, the LDO features 25-MHz unity-gain bandwidth (UGB) at 20-mA load to satisfy fast response requirement in high-speed transmitter. The simulated voltage undershoot is 38.85 mV for a load transient current stepping from $\mathbf{1}\ \boldsymbol{\mu} \mathbf{A}$ to 25 mA in 50 ps with $\boldsymbol{C}_{\mathbf{L}} =\mathbf{100}\ \mathbf{pF}$. Owing to the proposed XCEA and the filter capacitor, the PSR is measured to be -41 dB at 100 kHz and -36 dB at 1 MHz.
Implementing Cloud Computing in Smart Cities

With the development of the Internet of Things, the number of devices connected to the Internet has increased exponentially, leading to a huge increase in the amount of data to be processed in the cloud, with a significant increase in traffic at the expense of service quality. This, along with the low latency and mobility requirements of many web applications, has led to the emergence of computing options that are closer to network devices. Cloud computing has emerged as a new paradigm to address these issues, extending traditional cloud computing architecture to the boundaries of the network. Despite the rapid development of cloud computing, there are challenges associated with its implementation in real-world scenarios such as smart cities, where the heterogeneity and mobility of devices, as well as the required scalability and interoperability need to be considered. Accordingly, this paper presents a procedure to guide the implementation of this technology in smart cities. As a partial test, the proposed procedure is applied to the use case of rubbish collection
Comprehensive Reliability Analysis of 22nm FDSOI SRAM from Device Physics to Deep Learning

This work investigates the joint impact of device variability and transistor aging on the data integrity of SRAM cells implemented using 22 FDSOI. Our analysis is based on well-calibrated TCAD simulations that reproduce measurements from a commercial 22nm FDSOI technology node. The calibrations are done against measurement data for both I-V characteristics and variability data. We perform error analysis for SRAMs during hold and read operations under three different scenarios: (i) Fresh: time-zero variation (PV) alone caused by manufacturing variability, (ii) Aged: combined impact of PV and aging-induced increase in the transistor threshold voltage ($V_{TH}$) at the room temperature, (iii) Aged@85°C: combined impact of PV and transistor aging but at an elevated temperature of 85°C. Further, we explore how SRAM errors are exacerbated when the voltage is scaled down due to the reductions in noise margins. All error analyses were accurately performed in TCAD mixed-mode simulations for a complete 6-T SRAM cell. Finally, to investigate further how such errors impact the system level, we explore the corresponding induced accuracy drop in Deep Neural Networks (DNNs). Different quantized NNs are studied, and their sensitivity to errors in weights and activations is also explored. We demonstrate that short-term aging (i.e., when aging effects are combined with voltage scaling) results in a noticeable accuracy drop when ResNet20 and ResNet18 DNN models are examined on the CIFAR100 and Imagenet datasets, respectively.
Axisymmetric wave propagation of thermoelastic transversely isotropic half-space under buried loading using potential functions

Abstract By virtue of a new scalar potential function and Hankel integral transforms, the wave propagation analysis of a thermoelastic transversely isotropic half-space is presented under buried loading and heat flux. The governing equations of the problem are the differential equations of motion and the energy equation of the coupled thermoelasticity theory. Using a scalar potential function, these coupled equations have been uncoupled and a six-order partial differential equation governing the potential function is received. The displacements, temperature, and stress components are obtained in terms of this potential function in cylindrical coordinate system. Applying the Hankel integral transform to suppress the radial variable, the governing equation for potential function is reduced to a six-order ordinary differential equation with respect to z. Solving that equation, the potential function and therefore displacements, temperature, and stresses are derived in the Hankel transformed domain for two regions. Using inversion of Hankel transform, these functions can be obtained in the real domain. The integrals of inversion Hankel transform are calculated numerically via Mathematica software. Our numerical results for displacement and temperature are calculated for surface excitations and compared with the results reported in the literature and a very good agreement is achieved.
Asynchronous Control for Tsetlin Machine with Binary Memristor-Transistor Array

Tsetlin machines (TMs) are a novel machine learning paradigm based on learning automata and Boolean logic inference, with better energy-efficiency and explainability than neural networks. This work exploits non-volatile ReRAM-transistor memory arrays to perform efficient in-memory TM computing. To accommodate the large timing variability of ReRAM devices and enhance energy efficiency and speed, the control path is implemented with quasi delay-insensitive (QDI) asynchronous circuits. The design of these circuits are derived and synthesized from their signal-transition graph specifications using the Workcraft tool. The resulting circuits offer high event-driven controllability and high variation tolerance for the mixed-signal ReRAM data path. Compared to state of the art TM hardware, the new TM design uses less than 5% of the power to achieve better than $4\times$ the performance.
Acute Inhalation Injury Signatures in Breathing Rate Abnormalities in Domestic Environment using RF Sensing

Toxic gases pose different risks to human health. Exposure to these toxic gases can be through inhalation of mists, fumes, aerosols, and dust. These inhaled gases can cause injuries, including skin burns, respiratory distress, and death. Nowadays, these harmful toxic agents have become more common in domestic environments, increasing the chances of toxic inhalation in daily life. In a domestic environment, gas toxicity is detected through the odor of toxic gases, as equipment for gas sensing is not readily available and expensive, requiring technical expertise for usage. This situation demands novel methods for avoiding toxic inhalation injuries. Furthermore, most toxic gases affect the breathing rate; thus, constant detection of breathing rate may provide an early warning for toxic gas inhalation and avoid injury. This research study develops a software-defined radio (SDR) system using radio frequency (RF) sensing to monitor abnormal breathing rates continuously by observing channel state information (CSI) variations in the domestic environment. This research work can effectively detect abnormal breathing rates for multi-person in the same environment. The Fast Fourier Transform (FFT) frequency domain analysis-based method is used to detect the abnormal breathing rates of multiple individuals. This study faithfully detects normal, slow, and fast breathing rates for multiple expected cases. The developed system shows noteworthy performance even for five persons in the same environment. This research work is notable because it can be deployed in domestic and occupational settings.