Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtualization and Validation of Emulated STM-32 Blue Pill Using the QEMU Open-Source Framework

Hardware emulation is a technique often employed during system testing and evaluation. It provides the ability to closely replicate the functionality of a physical system, granting both evaluation efficiency and financial benefits over standard hardware testing. However, the extent to which emulation can accurately recreate the behavior of a physical microcontroller remains a topic of great debate for the public and private sector alike. This paper seeks to investigate and quantify the accuracy of an emulated microcontroller to its physical counterpart. The performance of the STM-32 Blue Pill (STM32f103C8T6), a commonly deployed general-purpose microcontroller, is contrasted in both the physical and emulated form factors. The Dhrystone 2.1 benchmark is employed to obtain computational and instruction-completion performance data to assess emulation capacity. This research aims to provide insight into the benefits of emulation to aid in the fields of cybersecurity, for the emulation of environments, and digital forensics, for the facile replication of the various attacks, to gain further insight on how best to mitigate these threats.
Application of Multidimensional Chain classifiers to Eddy Current Images for Defect Characterization

Multidimensional learning problem deals with learning a function that maps a vector of input features to a vector of class labels. Dependency between the classes is not taken into account while constructing independent classifiers for each component class of vector. To counteract this limitation, Chain Classifiers (CC) approach for multidimensional learning is proposed in this study. In this approach, the information of class dependency is passed along a chain. Radial Basis Functions (RBF) and Support Vector Machines (SVM) are used as core for CC. Studies on multidimensional dataset of images obtained from simulated eddy current non-destructive evaluation of a stainless steel plate with sub-surface defects clearly indicate that the performance of the chain classifier is superior to the independent classifiers.
Optimal Cost and Component Configuration Analysis of Micro-grid using SSO Algorithm

The efficiency and optimal size of a micro-grid can be evaluated through economic analysis. Optimization is crucial for the sustainable development and upkeep of a micro-grid from a financial perspective. T he total cost of production can be lowered by taking advantage of available subsidies for things like capital, operations, pollution, and renewable energy, as well as satisfying a number of equality and inequality standards. The social spider optimization (SSO) method is an effective and versatile way to save money. In some cases, SSO is used in conjunction with other AI-based optimization techniques. In this work, we present a methodology for assessing the economic and environmental sustainability of grid-connected energy systems. The mathematical function of a micro-grid may consist of recycling the electricity generated each hour in relation to the resources available and storing the surplus in a super capacitor battery bank. Using a model developed for the Halishahar area of Chattogram, Bangladesh, we maximize the performance of a hybrid system consisting of photovoltaic cells, wind turbines, biomass, and a super capacitor battery. Solar panels, wind turbines, super capacitor batteries, and biomass are just some of the sustainable energy sources that the designers are thinking about. The area is estimated to consume roughly 107,150 MWh of electricity each year. We employ a social spider optimization strategy to determine which configuration settings w ill yield the lowest annual cost. More than a year's worth of electricity for Halishahar can be generated by this micro-grid. With this configuration, t he L COE for electricity i so nly 0. 127 $/kWh (dollar per kilowatt-hour). It's more effective at cutting carbon dioxide emissions than conventional power.
Voice-Controlled Robot using Arduino and Bluetooth

This paper proposes a voice-controlled robotic system that uses Bluetooth to follow human commands. The voice commands are given to an android app built using MIT App Inventor. These commands are then sent to the Bluetooth module which then sends them to the controller interfaced with it. This interfacing was done using Universal Asynchronous Receiver-Transmitter (UART) Protocol. After processing the commands, the microcontroller controls the movement of the robot in different directions. An open-source hardware and software is used in the proposed research work. Further, the proposed model can be implemented by almost every student for educational and understanding purposes as it is both economical and easy-to-use. This study considers the domain of Natural Language Processing (NLP) as well as communication using Bluetooth, both of which have high possibilities in future based on the technological advancement.
An Approach For Short Term Electricity Load Forecasting

Electricity load forecasting is important from both a production and utilization standpoint. Short-term electricity forecasting is particularly meaningful, as electricity usage varies significantly over longer periods, and accurate forecasts can help us to address emergency situations. A study of existing short-term electricity forecasting approaches reveals a need for further improvement. In this work, we present a new technique for short-term electricity load forecasting using LSTM (long short-term memory). The study introduces various load forecasting techniques based on the prediction time period and discusses the time series model of load forecasting. We also discuss the difficulties of predicting electricity load and the factors that affect load forecasting, as well as the drawbacks of using simple forecasting methods such as curve fitting using numerical methods. We explore the use of machine learning models, such as neural networks and backpropagation, to tackle the problem, and we discuss an approach using LSTM, a variant of recurrent neural networks. We analyze the results and discuss the advantages and drawbacks of the technique, as well as steps that can be taken to improve results and the future scope of the project.
Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
A Structurally Enhanced Neck Exoskeleton to Assist with Head-Neck Motion

This paper presents the design of a neck exoskeleton to assist with head-neck motion for patients with amyotrophic lateral sclerosis (ALS). Motor neuron degeneration caused by ALS can lead to neck muscle weakness, resulting in head drop (chin-on-chest posture). Current treatment using static neck collars is inadequate because these collars completely immobilize the head. A powered neck exoskeleton (Columbia exoskeleton) was previously developed to assist with head-neck movements but its structural limitations hindered its usability for patients with severe head drop. In this paper, we introduce the Utah neck exoskeleton which improved the structural stability of the previous Columbia design by (1) optimizing the transmission efficiency and range of motion, and (2) using more precise mechanical components. We quantified the structural stability of the Utah neck exoskeleton and demonstrated its usability with a healthy volunteer. The results show that the Utah neck exoskeleton has a suitable structure to potentially assist with head-neck movements for patients with severe ALS head drop.
FPGA Based Register Access and Memory Control for 5G Beamforming Processing

The Evolution of radio communication during the last decades has brought major improvements in exploring new types of architecture and technologies to meet the growing demands of the consumer. A fifth generation (5G) network, actively standardized by 3rd Generation Partnership Project (3GPP) under Release 15, exploits the millimeter wave frequencies to dramatically increase the network bandwidth. Besides continuous improvements in terms of data capacity, a technique called beamforming is brought up which is a distinguishable feature from the previous generations. In the current approach, we design 4 Transmitter & 8 Receiver (4T8R) macro-O-RAN Radio Unit (O-RU) to implement beamforming which extracts the beam weights generated by O-RAN Distributed Unit (O-DU) and sent via C-plane packets, and the address calculation to store the beam weights is calculated with respect to the beam ID. O-RU needs to fetch the beam weights associated with that beam ID from the internal memory. The beam ID serves as a pointer to read from beam weight memory and performs complex multiplication and addition of those to indicate which beam to be applied for further processing. The proposed design is carried out using Verilog HDL and the design is tested on Xilinx Zynq UltraScale+ MPSoC that demonstrates the implementation which achieves high gain beam intended for the specific user.
A Compact Wireless Headstage based Optogenetic Neuromodulation and 32-channel Electrophysiological Recording System

This paper demonstrates a commercial off-the-shelf components (COTS)-based miniaturized wireless optogenetic headstage with simultaneous optical stimulation and electro-physiological recording capability for freely moving rodents. The proposed headstage contains 32 recording channels. The optical stimulation system is a battery-powered neural stimulator, comprised of a low dropout regulator (LDO), an oscillator, and a µLED. The electrophysiological signal recording system includes an intracortical neural probe made of a GaN-on-silicon substrate, an array of neural amplifiers with an integrated analog-to-digital converter (ADC), a transceiver integrated circuit, and a ceramic antenna. The integrated MUX with the ADC allows sampling of the amplified voltage at a sampling rate of 4000 kSamples/s. By placing the headstage on the head of a rodent and recording the neural signals from the Ventral Tegmental area of the brain, the system is experimentally validated in in-vivo. Experimental result shows that the proposed headstage can trigger neuron activity while collecting and detecting single-cell microvolt amplitude activity from multiple channels.
Prediction of Stock Prices using Prophet Model with Hyperparameters tuning

As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement.
On the bases of Zn lattice

The ${\mathbb{Z}^n}$ lattice is the lattice generated by the set of all orthogonal unit integer vectors. Since it has an orthonormal basis, the shortest vector problem and the closest vector problem are easy to solve in this particular lattice. But, these problems are hard to solve when we consider a rotation of ${\mathbb{Z}^n}$ lattice. In-fact, even though it is known that the ${\mathbb{Z}^n}$-isomorphism problem is in NP ∩ Co-NP, we still don’t have an efficient algorithm to solve it. Motivated by the above, in this paper we investigate the properties of the bases of ${\mathbb{Z}^n}$ lattice which are the sets of column/row vectors of unimodular matrices. We show that an integer primitive vector of norm strictly greater than 1 can be extended to a unimodular matrix U such that the remaining vectors have norm strictly smaller than the initial primitive vector. We also show a reduction from SVP in any lattice isomorphic to ${\mathbb{Z}^n}$ to SVP in n − 1 dimensional sublattice of ${\mathbb{Z}^n}$. We define two new classes of lattice bases and show certain results related to ${\mathbb{Z}^n}$ bases. Finally, we study the relation between any solution to Successive Minima Problem and the set of Voronoi relevant vectors and present some bounds related to the compact bases of ${\mathbb{Z}^n}$.
EEG Signal Classification using Memristor-based Reservoir Computing System

Investigations into aberrant brain activities arising from seizure events utilize time series Electroencephalogram (EEG) signals recorded from various regions within the brain. Reservoir Computing (RC) has emerged as an effective machine learning approach for categorizing temporal signals, offering reduced training expenses compared to conventional recurrent neural networks. Recently, memristors have gained significant traction in neuromorphic applications due to their appealing similarity to biological synapses. As a biological application, EEG signal classification is a compatible problem to analyze using memristor-based RC system. This work aims to classify EEG signals (Epileptic vs. Healthy) using a volatile memristor-based reservoir computing system in simulation platform. We have proposed a new RC framework that helps reduce the feature size in the readout layer for classification and achieves 100% accuracy.
A Reliable Model System Design to Predict Elderly People Falling Based on Drowsiness and Sleep Patterns

The Internet of Things aids in the monitoring of real-time sensing information via implanted or wearable sensors on the human body. A gateway will be connected to the internet and online data sensing will be possible thanks to the system it facilitates. Accidental or temporary loss of consciousness can result in a fall, and if help isn't given immediately, the results can be fatal for the elderly. Older people's mobility is limited since they need to be accompanied by a caretaker at all times out of concern that they will collapse. Those who cannot afford a career at home often have no choice but to go to institutional care. The need to seek assistance on an as-needed basis, rather than being constantly monitored, is the subject of this research. The sleep habits of the elderly are examined in this work since they are an important indicator of fatigue. Insufficient sleep is a common problem among the elderly. This factor allowed us to accurately forecast the elderly's physical condition and served as a useful piece of information in preventing elderly falls. As a result, we have applied a machine learning model to the problem of predicting the health of the elderly and are now able to do so. Model accuracy was confirmed by application to real-world data.
A Privacy-Preserving Protocol Level Approach to Prevent Machine Learning Modelling Attacks on PUFs in the Presence of Semi-Honest Verifiers

With the ubiquitous and distributed nature of the Internet-of-Things (IoT), various qualities of traditional communication methods for end devices and their verifiers prove insufficient in solving the challenges this new paradigm faces. Many new hardware and software technologies are proposed in an attempt to provide IoT systems with desired security properties while meeting performance requirements. Physically Unclonable Functions (PUFs) are one such technology receiving particular interest from the wider research community by promising to provide low-cost and highly secure key data to enable lightweight authentication protocols for devices operating over publicly accessible networks. PUFs have been the target of Machine Learning Modelling Attacks (ML-MA), which aim to clone the intrinsic behaviour of the PUF to undermine their security. While many PUF-based schemes have been proposed to defend against adversaries who are guaranteed to be dishonest, an area which has not seen significant consideration is one where a normal communication participant cannot always be assumed to act honestly. To the best of our knowledge, this work is the first to consider the concept of ‘semi-honest verifier’ for PUFbased authentication, taking initial steps to shed light on this prominent issue in IoT by proposing a privacy-preserving mutual authentication protocol which considers security against MLMA in the presence of such verifiers. Furthermore, this work describes hardware-level considerations for PUF obfuscation by utilising a combination of strong PUF, configurable One-Way Function (OWF) and secure DRAM-PUF and is, therefore, one of the first to integrate PUF obfuscation comprehensively at the protocol level.
Cross Protocol Attack on IPSec-based VPN

Network layer protocols that provide privacy, such as IPSec, are widely used in Virtual Private Network(VPN) services to provide security between two networks or hosts. This work analyzes the security of Internet Protocol Security (IPSec) based open-source VPNs and the vulnerabilities that may compromise their security. VPNs were configured with user IDs and RSA Certificates for authentication and username-password combinations to conduct security analysis of the IPSec setup. This work was tested only with IPSec-based VPNs and may not be generalizable to other VPNs or non-VPN applications utilizing IPSec. IPSec is a network layer protocol designed to increase data privacy between two networks. Application layer utilities may require this added privacy on the network layer. This work adds to the body of knowledge on the security of IPSec and provides valuable information for organizations using IPSec and for security professionals looking to improve its security. It highlights the importance of using certificate-based authentication rather than a Pre-Shared Key for authentication in ensuring the security of IPSec.
Implementation of Physical Layer Encryption For Wireless Communication System

The Adoption of Wireless Communication Systems has massively impacted everyday lives of the people. Securing privacy is an important aspect to consider in wireless communication systems due to their broadcast nature. The data transferred between two parties need to be protected by a process of encryption, which helps maintain its privacy. Encryption is usually only applied to the upper layers of the OSI model, which may be sufficient to protect the actual contents of the message. However, the Physical Layer (PHY) signal remains unprotected and consequently exposes critical information such as data rates, channel type, channel bandwidth, modulation, number of physical channels and traffic statistics, which can be used to deduce the channel capacity, transmission equipment and importance of the message. This issue can be mended by performing Physical Layer Encryption (PLE) to provide an additional layer of security. In this paper, a comparative analysis of different PLE techniques is provided along with their simulations. Additionally, these PLE techniques are also implemented in the wireless communication systems on the GNU Radio platform, and suitable output waveforms are illustrated with details.
Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges

In the last few decades, soft robotics technologies have challenged conventional approaches by introducing new, compliant bodies to the world of rigid robots. These technologies and systems may enable a wide range of applications, including human–robot interaction and dealing with complex environments. Soft bodies can adapt their shape to contact surfaces, distribute stress over a larger area, and increase the contact surface area, thus reducing impact forces.
Spectacular: Finding Laws from 25 Trillion Terms

We present Spectacular, a new tool for automatically discovering candidate laws for use in property-based testing. By using the recently-developed technique of ECTAs (Equality-Constrained Tree Automata), Spectacular improves upon previous approaches such as QuickSpec: it can explore vastly larger program spaces and start generating candidate laws within 20 seconds from a benchmark where QuickSpec runs for 45 minutes and then crashes (due to memory limits, even on a 256 GB machine). Thanks to the ability of ECTAs to efficiently search constrained program spaces, Spectacular is fast enough to find candidate laws in more generally typed settings than the monomorphized one, even for signatures with dozens of functions.
Severity-Aware Prioritization of System-Level Regression Tests in Automotive Software

In automotive software engineering, system-level regression testing is crucial to ensure proper integration of often- times safety-critical components. Due to the inherent complexity of such systems and components, testing is commonly performed manually and in a black-box manner, which is particularly costly and leads to slow feedback cycles between testers and developers. Regression Test Prioritization (RTP) aims to reduce feedback time by ordering tests to reveal faults earlier during the testing process. However, most prior RTP research does not incorporate varying fault severity, which must be taken into account when evaluating and designing appropriate RTP approaches for safety-critical automotive software systems. In this work, we present a case study at our industry partner MAN, a leading international provider of commercial vehicles. We design and instantiate a domain-specific, severity-aware RTP assessment model and comparatively assess state-of-the-art RTP approaches. Our results indicate that simple and partly well- known heuristics based on test history and test costs have the best cost-effectiveness, achieving between 85% and 90% of the maximum possible feedback time reduction. On the other hand, search-based and machine-learning-based RTP approaches do not perform better, especially if available test history is sparse.
Evaluating the effort of building a Machine Learning model for malware detection from ground zero

As the field of cybersecurity research evolved in the past decades, machine learning-based approaches for malware identification have become more frequent. The paper proposes a method of identifying which kind of features are the most valuable in terms of creating a machine learning-based model used for Portable Executable (PE) malware detection, taking into consideration the development time needed to extract the information and the quality of the extracted data based on the Fscore. This paper provides guidance in choosing what feature types new researchers should focus on first for achieving good results in detecting malware whether they need to use a small number of features or do not have any limitations regarding this.