Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Resilience of Machine Learning-Based IDS for Automotive Networks

Modern automotive functions are controlled by a large number of small computers called electronic control units (ECUs). These functions span from safety-critical autonomous driving to comfort and infotainment. ECUs communicate with one another over multiple internal networks using different technologies. Some, such as Controller Area Network (CAN), are very simple and provide minimal or no security services. Machine learning techniques can be used to detect anomalous activities in such networks. However, it is necessary that these machine learning techniques are not prone to adversarial attacks. In this paper, we investigate adversarial sample vulnerabilities in four different machine learning-based intrusion detection systems for automotive networks. We show that adversarial samples negatively impact three of the four studied solutions. Furthermore, we analyze transferability of adversarial samples between different systems. We also investigate detection performance and the attack success rate after using adversarial samples in the training. After analyzing these results, we discuss whether current solutions are mature enough for a use in modern vehicles.
Block Group Scheduling: A General Precision-scalable NPU Scheduling Technique with Capacity-aware Memory Allocation

Precision-scalable neural processing units (PSNPUs) efficiently provide native support for quantized neural networks. However, with the recent advancements of deep neural networks, PSNPUs are affected by a severe memory bottleneck owing to the need to perform an extreme number of simple computations simultaneously. In this study, we first analyze whether the memory bottleneck issue can be solved using conventional neural processing unit scheduling techniques. Subsequently, we introduce new capacity-aware memory allocation and block-level scheduling techniques to minimize the memory bottleneck. Compared with the baseline, the new method achieves up to 2.26× performance improvements by substantially relieving the memory pressure of low-precision computations without hardware overhead.
Bearing Degradation Process Prediction based on Feedforward Neural Network

As one of the most significant component of the rotary machinery, bearings play a vital role in smooth and reliable operation of the machinery. Estimating the remaining useful life (RUL) of bearings is essential for reducing the cost of maintenance and improving reliability. In this paper, a prognostics methodology based on artificial neural network (ANN) is proposed to improve the accuracy of RUL estimation for bearing. This is achieved by using features obtained from frequency, time and time-frequency domains. Popular techniques of Wavelet Packet Decomposition (WPD) and Fast Fourier Transform (FFT) are applied for feature extraction in time-frequency and frequency domains respectively. For effective prognostics, monotonicity and correlation-based feature selection criteria is used to discard redundant and unnecessary features. These features are then processed to be used as input into the ANN model. The model uses Feedforward Neural Network (FFNN) with the popular learning algorithm, Levenberg-Marquardt, for predicting the RUL. The results depict that this model is very effective for predicting the RUL of bearings. FFNN results are also compared with Gaussian Process Regression (GPR) algorithm results, showing the better performance of FFNN as compared to GPR.
Sensitivity Analysis of Dielectrically Modulated Hetero junction InSb/Si SOI-TFET based Biosensor for Cancer Detection.

Cancer is a major killer worldwide. Cancer death rates can be managed with early diagnosis. This study suggests an SOI TFET with a dielectric controlled dual material gate for the detection of cancer biomolecules. The nanocavity is introduced on the source and drain by an overlapping gate, which gives the cavity a broad area to accommodate and alters the device’s electrostatic properties. The tunneling rate of the electrons in the channel is enhanced by a dual material gate with two distinct work functions. Specifically, an heterojunction is implemented with a N+ pocket at the drain channel junction using InSb and si. Increasing the on current, InSb is placed at the power supply’s input. The suggested architecture is ideal for rapidly detecting cancer biomolecules because of its low power consumption, great adaptability, mobility, and sensitivity. The device decreases leakage current and allows for precise control over the channel. Ambipolar current, which is sensitive to the dielectric constant of the nanocavity, is used to detect cancer disease. Using high dielectric constants significantly improves the device’s sensitivity. Additionally, the device’s performance with partially filled, completely filled, and unfilled cavities has been rigorously examined.
Optimal re-distribution of demand responsive loads in Microgrids

The sudden surge in the fuel cost and increase in environmental impact causes a reduction in electricity generation through conventional energy sources. The power system is moving towards the incorporation of distributed energy resources (DERs) into the existing microgrid (MG). Distributed generation (DG), storage system and geographically scattered loads forms a MG. The stochasticity and unpredictable output associated with DGs may create imbalance in supply and demand. Further, the unexpected peak loads on the system mayjeopardize the system reliability. Hence, proper energy management system is obligatory for peak reduction so as to maintain the power balance. One of the emerging and efficient energy management techniques is demand response. While incorporating demand response, a part of curtailed loads i.e., shiftable loads at peak loads should be re-distributed to other off-peak hours where the load demand and electricity offer price is less. The re-distribution of loads at non-peak hours eventually increases the market clearing price (MCP). Increment in MCP mostly effects the inelastic loads as they cannot shift their schedule from one horizon to another since they are must run loads and of on/off in nature. In this context, the present work focuses on optimal rescheduling of the redistributed loads that doesn’t impact much MCP and operational cost (OC) of the MG much and should able to maintain the dynamic balance between supply and demand. A cost-based method was employed in which grey wolf optimization (GWO) algorithm is used to optimize the OC of the MG.
Thwarting Reverse Engineering Attacks through Keyless Logic Obfuscation

Logic obfuscation protects semiconductor IPs against reverse engineering threats by concealing IP implementation details using a tamper-proof key. With the continuous evolution of key recovery attacks exploiting functional, structural, and physical key exposures, the typical assumption of key secrecy becomes increasingly untenable. This work aims to end the tug of war between key-based defenses and key recovery attacks by delivering reverse engineering resilience through a novel keyless obfuscation approach that demands no external secret. The proposed solution locks the full functionality of a design using internally-generated and constantly-changing secrets that can be only extracted from the FSM transition history. The intrinsic secrets are secure against reverse engineering attempts due to the hardness of identifying valid transition paths to a target state from the obfuscated gate-level netlist. We develop an algorithm to synthesize the obfuscated FSM logic and the dynamic key update logic which jointly activate the design for all valid sequential queries so as to deliver unimpeded functionality for legal users. The algorithm enforces key consistency through equivalence-preserving FSM transformation and constraint-based state encoding to handle complex reconverging transition paths. Experimental results on MCNC benchmarks confirm the practicality and security of the proposed keyless logic obfuscation methodology.
TAM: A Computing in Memory based on Tandem Array within STT-MRAM for Energy-Efficient Analog MAC Operation

Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher bit density, for which the spin transfer torque magnetic RAM (STT-MRAM) with high bit density and performance arises as an up-and-coming candidate solution. In this work, we propose an analog CIM scheme based on tandem array within STT-MRAM (TAM) to further improve energy efficiency while achieving high bit density. First, the resistance summation based analog MAC operation minimizes the effect of low tunnel magnetoresistance (TMR) by the serial magnetic tunnel junctions (MTJs) structure in the proposed tandem array with smaller area overhead. Moreover, a read scheme of resistive-to-binary is designed to achieve the MAC results accurately and reliably. Besides, the data-dependent error caused by MTJs in series has been eliminated with a proposed dynamic selection circuit. Simulation results of a 2Kb TAM architecture show 113.2 TOPS/W and 63.7 TOPS/W for 4-bit and 8-bit input/weight precision, respectively, and reduction by 39.3% for bit-cell area compared with existing array of MTJs in series.
CAPEC: A Cellular Automata Guided FSM-based IP Authentication Scheme

The ever-increasing propensity for intellectual property (IP) reuse has reduced the design productivity gap in the supply chain. As a consequence, protecting IPs has become more difficult since IP vendors now make their IPs more flexible so that they can be reused in other designs for greater profits. This has made IP piracy and infringement easier than ever. IP watermarking can detect IP piracy and infringement and it has been an active research topic for the past decade. Various watermarking techniques have been discussed in the literature that embed circuitry into IP to provide proof of ownership. But, in most RT-level watermarking methods, the watermarking circuit is separate from IP functionality and can be easily identified and tampered with. In this paper, we propose CAPEC, a Cellular Automata (CA) guided watermarking technique that embeds watermarking circuits into the don’t care states of the FSM. The watermarking function is a set of configurable CA rules tightly coupled with the functional states of the FSM. CAPEC generates a signature in a challenge-response-based protocol, is resistant to identification, tampering, and removal attacks, and has minimal overhead. We also analyze and evaluate the efficiency of the technique and its resilience to different attacks for varying challenge size and CA rules. After watermarking different benchmarks, the watermark overhead was found to be negligible and formal verification proved no changes to the functional circuit.
ResNet-18 comparative analysis of various activation functions for image classification

Deep neural network and Machine learning are a latest emerging concept in the field of data science. Due to multi-layer hierarchical feature extraction in conjunction with control variables like number of hidden layers, activation functions, and variable parameters like learning rates, initial weights, and decay functions, deep network models perform better than machine learning techniques. While most of these parameter control the learning dynamics or complexity of representation a neural network can deal with, it is only activation function which introduces non-linearity in a network and current state of activation function poses multiple challenges to both practitioners and researchers some of which are: •Vanishing & Exploding gradients during back-propagation •Zero-mean and range of outputs •Compute complexity of function •Predictive performance Due to this reason our objective in current work in focused to explain with reasoning and experiments the landscape of activation functions available. According to a recent study, we have enough cutting-edge activation functions to modify the architecture of the well-known deep network model. Building on top of widely adopted ResNet-18 network architecture in this study. Subsequently, we evaluate the effectiveness of ResNet-18 for image classification using various activation functions.
Robust Resistive Open Defect Identification Using Machine Learning with Efficient Feature Selection

Resistive open defects in FinFET circuits are reliability threats and should be ruled out before deployment. The performance variations due to these defects are similar to the effect of process variations which are mostly benign. In order not to sacrifice yield for reliability the effect of defects should be distinguished from process variations. It has been shown that machine learning (ML) schemes are able to classify defective circuits with high accuracy based on the maximum frequencies $F_{max}$ obtained under multiple supply voltages $V_{dd} \in V_{op}$. The paper at hand presents a method to minimize the number of required measurements. Each supply voltage $V_{dd}$ defines a feature $F_{max}(V_{dd})$. A feature selection technique is presented, which uses also the already available $F_{max}$ measurements. It is shown that ML-based techniques can work efficiently and accurately with this reduced number of $F_{max}(V_{dd})$ measurements.
Speech Quality Assessment and Control in Indian Languages

Speech quality evaluation and control is an interdisciplinary field that incorporates techniques from signal processing, linguistics, and cognitive psychology. Speech quality assessment aims to measure a speech signal's quality in terms of a variety of factors, including understandability, naturalness, and listener satisfaction. Speech control, on the other hand, seeks to improve the quality of a speech signal through the use of various techniques such as noise reduction, equalization, and compression. Speech quality assessment and control in Indian languages can be used for a variety of applications such as speech recognition, text-to-speech synthesis, and speaker identification. Indian languages are known for their rich phonetic and phonological structures, and it is critical to develop assessment and control methods that take these complexities into account. The wide variety of dialects and languages spoken in India is one of the challenges in assessing and controlling speech quality in Indian languages. India is a multilingual country with over 21 officially recognized languages and thousands of dialects. Because each language has its own phonetic and phonological structure, developing a single method that works for all Indian languages can be difficult. Another issue is the scarcity of speech data and resources in Indian languages. The lack of large speech corpora and standard datasets for Indian languages can make developing and evaluating speech quality assessment and control methods for these languages difficult. Overall, speech quality assessment and control in Indian languages is a difficult but important area of research that has the potential to significantly impact the development of speech-based systems and applications for Indian languages.
ML-IBotD: Machine Learning based Intelligent Botnet Detection

With the advancements in communication technologies, an abundance of smart devices and internet-based applications in every walk of human life has resulted in the production of a huge number of data transmissions over the internet. In line with this emergence, the number of cybersecurity attacks is also rising. Among notable network attacks like mal ware, phishing, etc., we focused on botnet attacks which can cause huge damage on a large scale because botnet works in network form which appears as an adverse risk for the internet. In the botnet, there are many compromised systems known as bots controlled by the botmaster. On the other hand, Machine Learning (ML) is playing an important role in the detection of such network attacks with notable accuracy. In this paper, we select a dataset of CIC-IDS2017 due to its real interpretation of botnets. Then flows are extracted and then relevant four features are selected from the flows. In this paper, we apply four classifiers of SVM, KNN, DT, and Ensemble classifier on a real dataset of CIC-IDS2017. The highest achieved testing accuracy is 99.56% with the Ensemble classifier.
Pattern Reconfigurable Antenna using Programmable Metasurface

In this paper, a pattern reconfigurable antenna operating at 3.5 GHz frequency is proposed. A square-shaped patch serves as the radiator for this antenna while the superstrate layer is made up of a metasurface layer that contains a 4× 4 programmable square ring resonator. To control the radiation beam in the H-plane four PIN diodes are installed in each cell to alter the electric field intensity within the programmable metasurface layer. Performance of the proposed pattern reconfigurable antenna is analyzed in three operational states. Main beam of the antenna can be discretely switched from -30 ° to +30°. In all the operating modes the antenna shows good impedance matching with overall bandwidth from 3.37 to 3.54 GHz. A stable gain of 6.9 dBi is observed in all the operational states.
An Intelligent Computer-Aided Analysis Framework for the Impact of PTSD on Rescuers based on EEG Signal Visualization Algorithm

An intelligent computer-aided analysis framework for the impact of the PTSD on rescuers based on EEG signal visualization algorithm is designed and studied in this research work. Considering the EEG signal has limited information for the analysis, as conventional deep learning algorithms cannot achieve satisfactory results in the case of the data scarcity. Hence, this study considers the signal visualization and the classification models. Through the intelligent computer-aided analysis framework, the application scenarios on impact of PTSD on rescuer is selected as the testing scenario. Through the experimental simulation, the performance is tested.
CFU Playground: Want a faster ML processor? Do it yourself!

The rise of machine learning (ML) has necessitated the development of innovative processing engines. However, devel-opment of specialized hardware accelerators can incur enormous one-time engineering expenses that should be avoided in low-cost embedded ML systems. In addition, embedded systems have tight resource constraints that prevent them from affording the “full-blown” machine learning (ML) accelerators seen in many cloud environments. In embedded situations, a custom function unit (CFU) that is more lightweight is preferable. We offer CFU Playground, an open-source toolchain for accelerating embedded machine learning (ML) on FPGAs through the use of CFUs.
A Multi-Task Learning Method Integrating Multi-Information Fusion for Non-Intrusive Load Monitoring

The aim of non-intrusive load monitoring (NILM) is to determine the energy consumption of each electric load within a range by analyzing the total electricity meter data for a particular area. Since existing methods don’t consider the interactions between various loads, it is difficult to fully explore load features from the limited total meter data. To address this problem, NILM model is proposed based on multi-information fusion and multi-task learning (MIFMTL-NILM). Specifically, 1) Enhancing the features of samples by extracting the load features inherent in the total electricity meter data of various windows. 2) Enhancing the accuracy of load disaggregation and reducing the resource overhead of edge devices, multi-task learning is used to share network parameters across various electrical loads. Finally, the performance and effectiveness of the proposed model are validated using the public dataset UK-DALE and the constructed dataset in the laboratory.
Design of MIMO Microstrip Antenna with Bandwidth Enhancement for 5G Applications

The introduction of 5G technology is one of the most notable instances of how MIMO is frequently employed in massive new applications. For 5G applications, MIMO microstrip antennas are ideal, as they are readily accessible and simple to manufacture. The suggested antenna consists of a 4-element MIMO microstrip antenna mounted on a portion of the ground plane and a low-cost FR-4 epoxy substrate on the opposite side. The suggested architecture has a 7.8 GHz bandwidth (covering c-band and x-band) with a -32.3 dB return loss. The size of the suggested design is compact and superior than earlier efforts. Designing and modelling the proposed MIMO antenna is done using HFSS software.
VE-FIDES: Designing Trustworthy Supply Chains Using Innovative Fingerprinting Implementations

The project VE-FIDES will contribute with a solution based on an innovative multi-level fingerprinting approach to secure electronics supply chains against the threats of malicious modification, piracy, and counterfeiting. Hardware-fingerprints are derived from minuscule, unavoidable process variations using the technology of Physical Unclonable Functions (PUFs). The derived fingerprints are processed to a system fingerprint enabling unique identification, not only of single components but also on PCB level. With the proposed concept, we show how the system fingerprint can enhance the trustworthiness of the overall system. For this purpose, the complete system including tiny sensors, a Secure Element and its interface to the application is considered in VE-FIDES. New insights into methodologies to derive component and system fingerprints are gained. These techniques for the verification of system integrity are complemented by methods for preventing reverse engineering. Two application scenarios are in the focus of VE-FIDES: Industrial control systems and an automotive use case are considered, giving insights to a wide spectrum of requirements for products built from components provided by international supply chains.
Embedded Skin Lesion Segmentation using Lightweight Encoder-Decoder Architectures

Skin cancer is a global health concern that might be alleviated by embedded device-assisted diagnosis due to the limited number of dermatologists in many regions. An embedded device integrated with a camera for skin cancer diagnosis is of great interest worldwide, potentially enhancing mobile dermo-scopic diagnosis for the most malignant skin cancer, melanoma. The automatic segmentation of skin lesions from images is a crucial step towards reaching this objective. Deep learning-based models provide state-of-the-art accuracy in dermoscopic image analysis and diagnosis. Deep learning, on the other hand, has high computation costs, making it difficult to integrate such models on an embedded platform with limited resources. For this purpose, we proposed lightweight encoder-decoder deep learning architectures, referred to as MobileUNet and EfficientUNet, with the encoder based on the MobileNetV2 bottleneck block and the EfficientNetB0 MBConv block, respectively, and the decoder being identical to the baseline UNet model. The proposed architectures are evaluated on two publicly available datasets, ISIC 2017 and ISIC 2018. Models run efficiently on embedded platforms, achieving up to 12 percent higher performance compared to the baseline model with minimal power and memory requirements without compromising accuracy or the Jaccard index compared to the baseline model.
Hardware Trojans in eNVM Neuromorphic Devices

Fast and energy-efficient execution of a DNN on traditional CPU- and GPU-based architectures is challenging due to excessive data movement and inefficient computation. Emerging non-volatile memory (eNVM)-based accelerators that mimic biological neuron computations in the analog domain have shown significant performance improvements. However, the potential security threats in the supply chain of such systems have been largely understudied. This work describes a hardware supply chain attack against analog eNVM neural accelerators by identifying potential Trojan insertion points and proposes a hardware Trojan design that stealthily leaks model parameters while evading detection. Our evaluation shows that such a hardware Trojan can recover over 90% of the synaptic weights.