Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Assessment of Logic Locking Mechanisms using Pseudo-Boolean Optimization Techniques

Nowadays, the manufacturing of Integrated Circuits (ICs) is highly distributed over different foundries yielding untrustworthy supply chains. This circumstance leads to concerns regarding the security, privacy, and reliability of the fabricated ICs, e.g., malicious usage and counterfeiting. Logic Locking (LL) is a prominent protection technique to safeguard against such concerns. Recently, the emerging technology of Reconfigurable Field-Effect Transistors (RFETs) has been utilized to implement new mechanisms based on Polymorphic Logic Gates (PLGs) to protect Intellectual Property (IP). The mechanisms’ assessment is indispensable to reinforce the newly introduced logic obfuscation and, hence, avoid any security breaches. So far, formal SAT-based and approximate Hamming Distance (HD)-based assessment techniques have been used for determining the protection quality. While the approximate and formal approaches can detect many security threats [1], they are still unable to detect optimization-based attacks. This work proposes a novel formal approach based on Pseudo Boolean Optimization (PBO) to assess the quality of LL structures for sequential circuits, enabling the detection of currently unconsidered security breaches. In particular, the proposed approach leverages formal techniques to analyze the key and state space of a sequential circuit to evaluate the security against optimization-based attacks. The experimental evaluation validates that the proposed scheme unveils weaknesses of the protection structure, which remain undetected when using existing techniques.
Design and Numerical Optimization for High-Performance IPM Synchronous Motor Using PSO Search Algorithm

This paper uses particle swarm search optimization (PSO) to improve the performance behavior of the interior permanent magnet synchronous permanent (IPMSM) motor. Because of their inherent advantages, permanent magnet synchronous motors are increasingly used in high-power applications, especially in electric vehicles. There are many ways to optimize motor design, especially from the rotor side, to have optimal geometric placement of the permanent magnet to improve performance. Also, we can reduce the PM costs and improve the motor’s performance. This paper improves IPMSM performance for high torque output capabilities using permanent magnets and a high saliency ratio to operate in a wide speed range. A multiobjective optimization with PSO search algorithms is performed to determine the magnets’ optimum size and position at the magnet’s highest drop rate. This design produced a better engine in the chassis than the original specification. The FEA method was used to check the accuracy of the results. Finally, we evaluate the regenerative rotor motor structure and validate the simulations.
On Automatic Target Recognition (ATR) using Inverse Synthetic Aperture Radar Images

Inverse Synthetic Aperture Radar (ISAR) is used to image sea surface targets during day/night and all-weather capabilities for applications such as coastal surveillance, ship self-defense, suppression of drug trafficking etc. Hence automating classification of ships by means of machine learning methods has become more significant. Typical classification approaches consist of pre-processing, feature extraction and processing by classifiers. Image processing techniques are applied for pre-processing ISAR images. Transformation invariant features are then extracted to which classifiers such as SVM, Neural Networks (NNs) are applied The performance of these algorithms depend on the manually chosen features and is trained to perform well for different target profiles. The target image (profile of target) varies depending on the target type, aspect angle and motion introduced due to different sea states. In addition, Deep learning methods are also being explored for classification of ships. The challenge is to classify ships for different sea conditions and image acquisition parameters with limited database and processing resource.
DTOC: integrating Deep-learning driven Timing Optimization into the state-of-the-art Commercial EDA tool

Recently, deep-learning (DL) models have paid a considerable attention to timing prediction in the placement and routing (P&R) flow. As yet, the DL-based prior works are confined to timing prediction at the time-consuming global routing stage, and very few have addressed the timing prediction problem at the placement, i.e., at the pre-route stage. This is because it is not easy to “accurately” predict various timing parameters at the pre-route stage. Moreover, no work has addressed a seamless link of timing prediction at the pre-route stage to the final timing optimization through making use of commercial P&R tools. In this work, we propose a framework called DTOC, to be used at the pre-route stage for this end. Precisely, the framework is composed of two models: (1) a DL-driven arc delay and arc output slew prediction model, performing in two levels: (level-1) predicting net resistance (R), net capacitance (C), and arc length (Len), followed by (level-2) predicting arc delay and arc output slew from the R/C/Len prediction obtained in (level-1); (2) a timing optimization model, which uses the inference outcomes in our DL-driven prediction model to enable the commercial P&R tools to calculate the full path delays, setting update timing margins on paths, so that the P&R tools should use more accurate margins on timing optimization. Experimental results show that, by using our DTOC framework during timing optimization in P&R, we improve the pre-route prediction accuracy on arc delay and arc output slew by 20~26% on average, and improve the WNS, TNS, and the number of timing violation paths by 50~63 % on average.
Gait-Face Based Human Recognition From Distant Video

This paper presents a non-intrusive video-based identification system with a single camera view placed at 90° angle to the subject. The aim is to efficiently extract biometric features from a low-resolution video (acquired from a common CCTV camera) for recognizing individuals. The proposed method uses gait cues and face profile features for identification of individuals. A fusion rule is applied to these feature sets to obtain a new set of attributes. Thus, three different recognition models are developed using the gait, face, and fused feature sets. An ensemble technique is defined over the three classification models based on these sets of cues to identify an individual. This approach is experimentally validated on the CASIA-B dataset that achieves 99.33% identification accuracy.
Analysis of Machine Learning Technique for Crop Selection and Prediction of Crop Cultivation

Agriculture is a fundamental and basic source of living of many people and it is considered as a main part in economy and finance of any country. It is highly dependent on climate, weather and environment factors. Efficiency in crop productivity depends on agricultural elements embracing soil, water, temperature, and climate changes. The challenges in agriculture field, specifically crop prediction are complex and a machine-learning approach for crop cultivation is proposed for the achievement of production goals. This paper is proposed to design a machine learning-based system for crop selection. It can analyze the quality of soil and water, agro-climatic conditions, and also the necessary crop requirements for selecting suitable crops using the database. Also, supervising crop growth is proposed for monitoring the proper growth of the crop. A machine learning technique to develop crop prediction and soil quality check models. This model works on Machine Learning Techniques like Support Vector Machine (SVM) algorithm. The classifiers like Random Forest (RF), Gaussian Naive Baye's (NB), and k Nearest Neighbor (kNN) are used in crop prediction process. Based on machine learning algorithm and prediction accuracy, this model can help farmers at different locations for crop cultivation management.
ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training

Stochastic rounding is crucial in the low-bit (e.g., 8-bit) training of deep neural networks (DNNs) to achieve high accuracy. One of the drawbacks of prior studies is that they require a large number of high-precision stochastic rounding units (SRUs) to guarantee low-bit DNN accuracy, which involves considerable hardware overhead. In this paper, we use extremely low-bit SRUs (ESRUs) to save a large number of hardware resources during low-bit DNN training. However, a naively designed ESRU introduces a biased distribution of random numbers, causing accuracy degradation. To address this issue, we further propose an ESRU design with a plateau-shape distribution. The plateau-shape distribution in our ESRU design is implemented with the combination of an LFSR (linear-feedback shift register) and an inverted LFSR, which avoids LFSR packing and turns an inherent LFSR drawback into an advantage in our efficient ESRU design. Experimental results using state-of-the-art DNN models demonstrate that, compared to the prior 24-bit SRU with 24-bit pseudo-random number generators (PRNG), our 8-bit ESRU with 3-bit PRNG reduces the SRU hardware resource usage by 9.75x while achieving slightly higher accuracy.
Automatic Refinery System Framework for Industries; Power Generation System Perspective

The lack of chemical-free water is a problem that plagues many areas of the world today. Approximately 884 million people suffer each day from insufficient quantities of clean water. The use of chemical processing mechanisms for purification is an affordable solution. The plan is to construct a water filtration system with an integrated power system that can utilize its own energy while remaining low-cost and sustainable. The system will accept chemically mixed water here, passing through a reservoir and then being treated by a refiner. With the aid of this apparatus, neutralized chemical water from chemically mixed water was prepared from industrial water waste. When the water pressure reaches its maximum, the water detector will send a signal to open the gate as it needs to run the turbine at full speed with high pressure to generate electricity using the generator, charge a battery with the help of the charge controller, and provide a small amount of electricity for industrial uses. This rechargeable battery will supply energy to pH level detectors, water level indicators, and automatic gates. Here, two types of solutions will be stored in the reservoir: one will be an acidic solution, and the other will be an alkaline solution. This solution is used to neutralize the water according to the pH level of the refinery reservoir solution. The remaining chemical-free water will be discharged into the nearest dumping zone.
A Novel Method to Detect Lung Cancer using Deep Learning

Lung cancer is a type of cancer that starts in the cells of the lungs, and it is one of the leading causes of cancer- related deaths worldwide. It is often caused by smoking, exposure to radon gas, and exposure to certain toxins and pollutants in the environment. Risk factors for lung cancer include smoking tobacco, exposure to the smoke exhaled by smokers, lung cancer in individual with a family history of the disease, and people have damaged lung, such as COPD. Diagnosis of lung cancer typically involves a combination of imaging tests, such as CT scans and X-rays, and biopsy. detection of lung cancer allows for more options in terms of treatment and a better chance of curing the cancer. In general, studies on this topic aim to use advanced deep learning techniques have the potential to improve the accuracy and efficiency of lung cancer detection and diagnosis, which can ultimately lead to better patient outcomes. These techniques involves both CNNs and DBNs have shown great promise in the analysis of medical imaging data for the detection and diagnosis of lung cancer, as they are able to automatically identify and classify patterns in the images that are indicative of lung cancer. These techniques have the potential to improve the accuracy and efficiency of lung cancer diagnosis. The recent techniques are GPT -3, Generative adversarial network, Deep reinforcement learning. Here, CNN algorithm is used to detect lung cancer earlier, they are able to automatically identify and classify patterns in medical images that are indicative of lung cancer. CNN that are particularly well-suited for image analysis because they can learn to recognize features in images and gives accuracy that are important for classification and detection.
A Letter Box shaped UWB monopole antenna for future 5G communication systems

The proposed work comprises of an ultra-wideband (UWB) antenna having dimensions of $20 \times 23 \times 1.6 m m^{3}$ with stub components with in radiating patch alongside Defected Ground Structure (DGS) for WLAN band (5.2/5.8 GHz), Wi-MAX band (5.25 GHz –5.85 GHz), bands n77 (3300-4200 MHz), n78 (3300-3800 MHz), and n79 of the sub-6-GHz 5G NR (4400–5000 MHz). The suggested antenna shows the 8.09 GHz (2.99–11.08 GHz) impedance bandwidth with fractional bandwidth (FBW) of 114.99% with assistance of rectangular stubs to achieve lower resonating frequency and to increase operational bandwidth, an open-ended slit has been etched into the partial ground plane. The compact design is created on the economical and convenient FR-4 epoxy substrate material with a loss tangent of 0.02 and a relative permittivity of 4.3 shows a return loss of 24.09 dB at 3.5 GHz and 29.71 dB at 8.5 GHz. At 10.50 GHz, the designed antenna produces a peak gain of around 4.71 dBi and radiation efficiency of 84% at 8.5 GHz. Finally, the final antenna prototype is built, and simulation results are obtained to ensure good agreement with simulation findings.
Highly-Bespoke Robust Printed Neuromorphic Circuits

With the rapid growth of the Internet of Things, smart fast-moving consumer products, and wearable devices, requirements such as flexibility, non-toxicity, and low cost are desperately required. However, these requirements are usually beyond the reach of conventional rigid silicon technologies. In this regard, printed electronics offers a promising alternative. Combined with neuromorphic computing, printed neuromorphic circuits offer not only the aforementioned properties, but also compensate for some of the weaknesses of printed electronics, such as manufacturing variations, low device count, and high latency. Generally, (printed) neuromorphic circuits express their functionality through printed resistor crossbars to emulate matrix multiplication, and nonlinear circuitry to express activation functions. The values of the former are usually learned, while the latter is designed beforehand and considered fixed in training for all tasks. The additive manufacturing feature of printed electronics allows the design of highly-bespoke designs. In the case of printed neuromorphic circuits, the circuit is optimized to a particular dataset. Moreover, we explore an approach to learn not only the values of the crossbar resistances, but also the parameterization of the nonlinear components for a bespoke implementation. While providing additional flexibility of the functionality to be expressed, this will also allow an increased robustness against printing variation. The experiments show that the accuracy and robustness of printed neuromorphic circuits can be improved by 26% and 75% respectively under 10% variation of circuit components.
Simple Square Shape Electromagnetic band gap structure for wearable antenna

In this paper, design of Electromagnetic bandgap structure (EBG)at 3.45 GHz for wearable applications is proposed. For designing EBG structure the unit cell considered is of square shape with three different structures, first EBG is of square shape second is of square shape having inner circular cut and third is of square shape having inner square cut. The paper includes software simulation of the antenna with and without EBG on Ansoft HFSS. These EBG structures are integrated with antenna, to lessen the radiation that enters the human body. On a simulated human body, the effectiveness of antennas with and without EBG has been investigated and presented. The antenna’s suitability for wearable applications has also been verified by a specific absorption rate study. To lessen back radiation entering the human body, an EBG array of dimensions 41mm x 41mm with 3 x3 and 4 x4 is adopted. The measured SAR is well below than permitted limits.
ODLPIM: A Write-Optimized and Long-Lifetime ReRAM-Based Accelerator for Online Deep Learning

ReRAM-based Processing-In-Memory (PIM) architectures have demonstrated high energy efficiency and performance in deep neural network (DNN) acceleration. Most of the existing PIM accelerators for DNN focus on offline batch learning (OBL) which requires the whole dataset to be available before training. However, in the real world, data instances arrive in sequential settings, and even the data pattern may change, which calls concept drift. OBL requires expensive retraining to solve concept drift, whereas online deep learning (ODL) is evidenced to be a better solution to keep the model evolving over streaming data. Unfortunately, when ODL optimizes models over a large-scale data stream in the PIM system, unbalanced writes are more severe than OBL, due to the heavier weight updates, resulting in the amplification of unbalanced writes and lifetime deterioration. In this work, we propose ODLPIM, an online deep learning PIM accelerator that extends the system lifetime through algorithm-hardware co-optimization. ODLPIM adopts a novel write-optimized parameter update (WARP) scheme that reduces the non-critical weight updates in hidden layers. Besides, a table-based inter-crossbar wear-leveling (TIWL) scheme is proposed and applied to the hardware controller to achieve wear-leveling between crossbars for lifetime improvement. Experiments show that WARP reduces weight updates on average to 15.25% and up to 24% compared to that without WARP, and eventually prolongs system lifetime on average to 9.65% and up to 26.81%, with a negligible rise in cumulative error rate (up to 0.31%). By combining WARP with TIWL, the lifetime of ODLPIM is improved by an average of $\mathbf{12}. \mathbf{59}\times$ and up to $\mathbf{17}. \mathbf{73}\times$.
The CNN vs. SNN Event-camera Dichotomy and Perspectives For Event-Graph Neural Networks

Since neuromorphic event-based pixels and cameras were first proposed, the technology has greatly advanced such that there now exists several industrial sensors, processors and toolchains. This has also paved the way for a blossoming new branch of AI dedicated to processing the event-based data these sensors generate. However, there is still much debate about which of these approaches can best harness the inherent sparsity, low-latency and fine spatiotemporal structure of event-data to obtain better performance and do so using the least time and energy. The latter is of particular importance since these algorithms will typically be employed near or inside of the sensor at the edge where the power supply may be heavily constrained. The two predominant methods to process visual events - convolutional and spiking neural networks - are fundamentally opposed in principle. The former converts events into static 2D frames such that they are compatible with 2D convolutions, while the latter computes in an event-driven fashion naturally compatible with the raw data. We review this dichotomy by studying recent algorithmic and hardware advances of both approaches. We conclude with a perspective on an emerging alternative approach whereby events are transformed into a graph data structure and thereafter processed using techniques from the domain of graph neural networks. Despite promising early results, algorithmic and hardware innovations are required before this approach can be applied close or within the Event-based sensor.
A Novel Cross Validated Information Security Framework for Digital Information Systems

Secure multi-party computation is one of the current hot spots in cryptographic research, which means that multiple participants work together to further detect the attack. Inspired by this idea, this study proposes the novel cross validated information security framework for digital information systems. At the same time, considering that the traditional measurement indicators are easy to operate and widely used, but they are all measurement methods based on a single threshold, so when evaluating the model, they are susceptible to the impact of category imbalance and mis-classification costs, and there may also be a measurement indicator paradoxical situation where a metric is high and another metric is low. Then, the discrete value is selected to evaluated the designed algorithm. The designed model is the combination of improved game model and cross validated algorithm. The test is conducted under different data size and the different comparison algorithms. From the discrete value perspective, the proposed model performance better.
A Model Predictive Control based Strategy for Voltage Control of a Standalone Microgrid

The voltage level of a power system can be impacted by the sporadic nature of renewable energy sources and fluctuating load requirements. A hybrid energy storage system (HESS) may be used to combat this problem, but maintaining the HESS's state of charge (SoC) could be challenging. The conventional method of assigning power to the HESS is to use a high pass or low pass filter. The proposed standalone microgrid includes a solar photovoltaic (SPV) generation system, a battery energy storage system (ESS), a supercapacitor, and a load. A model predictive control is used to mitigate the deviations in the DC link voltage. This proposed method is more effective than the conventional filter-based control. The system is simulated in MATLAB Simulink.
Exploiting assertions mining and fault analysis to guide RTL-level approximation

Approximate Computing (AxC) paradigm was introduced to achieve higher power efficiency, lower area and better performances w.r. t. a “classical” computing system at the cost of a degraded, but still acceptable, output accuracy [1]. AxC can be applied at several abstraction levels of a given computing system: from circuit to algorithm [1], leading to a wide design exploration space that quickly became the bottleneck for successfully deploying AxC. Indeed, the literature proposes many works to automatically trade-off between output accuracy and performances [2]. However, most of them lack the capability to identify resilient elements (e.g, HW component, HDL statements, etc.) of the design to be approximated. Consequently, exploring the design for AxC generally results in a long and tedious procedure. Existing approaches generate approximate variants of the Design Under Exploration (DUE). Every variant is then executed/simulated in order to determine the accuracy degradation [3], which depends on the application and requires a specific metric to be computed (e.g., similarity index, hamming distance, etc.).