Miracle-dz/UniFAR-FTUs huggingface dataset:
A Graph-Based Accelerator of Retinex Model with Bit-Serial Computing for Image Processing

This work implements the Poisson equation formulation of the Retinex model for image enhancements using a graph hardware accelerator performing finite difference updates on a 2D lattice graph PE array. A single clock gating control signal manages the data flow, data sharing, and reuse pattern among neighboring PEs during massively parallel updates. With increasing user-configurable update count, image noise and shadow can be progressively removed with the inevitable loss of image details. Accommodating a non-overlap image mapping scheme in which a $20\times 20$ image tile can be processed without external memory access at a time, the proposed accelerator consists of 18 $\times 18$ regular PEs surrounded by $4\times 20$ boundary PEs with reconfigurable data flow and 4 boundary cache registers. Fabricated using a 65nm technology, the test chip occupies 0.2955mm2 core area, and consumes 2.191mW operating at 1V, 25.6MHz, and a reconfigurable 10- or 14-bit precision.
Efficient Transmission and Secure Sharing of Sensing data under Distributed ISAC Conditions

To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for largescale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.
Machine and Deep Learning Approaches For Brain Tumor Identification: Technologies, Applications, and Future Directions

Brain tumor classification and detection utilizing machine learning and deep learning are increasingly becoming the focus of medical image analysis research. Thanks to the advancement of modern imaging technology and machine learning algorithms, it is now possible to accurately and efficiently classify and diagnose brain tumors using a range of methodologies. Machine learning and deep learning algorithms are currently utilized to assess medical images and categorize tumors based on traits such as shape, size, and texture. Convolutional neural networks (CNNs), a popular deep learning technique, are utilized to identify brain tumors in medical pictures precisely. These models were created using large datasets of brain MRI scans and are incredibly accurate in spotting the presence of cancer. These models have been tested and trained using a variety of freely available datasets, such as the Brain Tumour Segmentation (BraTS) dataset and the LGG/GBM dataset. Researchers have developed several methods for preprocessing the data, increasing the dataset, and fine-tuning the models to improve the models' accuracy further. Overall, categorizing and identifying brain tumors using machine learning and deep learning is a rapidly growing field promising to enhance patient outcomes. By accurately and successfully identifying brain tumors, these techniques can lead to earlier detection and more tailored treatments, ultimately improving patient outcomes. The categorization and detection of brain tumors have been enhanced thanks to machine learning and deep learning on various datasets.
A 14–28 Gb/s Reference-less Baud-rate CDR with Integrator-based Stochastic Phase and Frequency Detector

This paper presents a 14–28 G/bs reference-less Baud-rate clock and data recovery (CDR) with a stochastic phase and frequency detector (PFD). To achieve phase and frequency detection, optimum weight is determined through the histogram-based correlation of various data patterns. Many data patterns within a wide frequency range are utilized to demonstrate robust operation. The reference-less Baud-rate CDR is implemented utilizing data samples and phase error samples obtained from the integrator. The proposed CDR is designed to achieve a data rate of up to 28 Gb/s employing a continuous-time linear equalizer (CTLE) under a 4.7-dB data loss channel at Nyquist frequency. Fabricated in 28-nm CMOS technology, the proposed CDR achieves a bit error rate (BER) < 10−12 and energy efficiency of 1.06 pJ/b.
Energy-/Area-Efficient Spintronic ANN-based Digit Recognition via Progressive Modular Redundancy

Neural networks offer viable alternatives for energy versus accuracy tradeoffs, in particular with regards to the precision of the computational circuit. This paper explores use of progressive modular redundancy of intrinsically low energy, low precision circuits as an alternative to more complex networks yielding higher accuracy directly. Results indicate that a lower footprint temporal modular redundancy, which is applied progressively as needed, can have lower footprint and reduced energy consumption at comparable or slightly reduced accuracy as more complex neural networks. This provides an alternative to binarization and other model compression options for intelligence at the edge of the network. Our Progressive Modular Redundancy approach using varied activations implemented using a $\mathbf{784}\times \mathbf{100}\times \mathbf{10}$ network shows a 3% improvement in accuracy compared to the baseline case of $\mathbf{784}\times \mathbf{500}\times \mathbf{500}\times \mathbf{10}$ network with sigmoidal activation, at 86.1% and 87% reduction in power and weighted crossbar normalized area overhead, respectively, 87.5% reduction in power error product (PEP) at the cost of ~2.6x increased throughput latency.
The global asymptotic stability of a system of difference equations

Abstract This paper deals with the global asymptotic stability of the unique positive equilibrium point and the rate of convergence of positive solutions of the system of two recursive sequences , and where , and are arbitrary positive numbers for Also, we present some results about the general behaviour of solutions of aforementioned system. Finally, some numerical examples are given to demonstrate the effectiveness of the results obtained.
An Area Efficient Built-in Phase Testing Equipment for Phased Array Front-End ICs

A Built-in Test Equipment (BITE) to measure the phase of the channel in front-end phased array ICs is presented. A built-in measurement algorithm is employed which enables a compact design approach. A frequency divider is used as quadrature generator, amplifier, limiter, and frequency scaler (all-in-one), for reduced circuitry and complexity. A proof of concept of the proposed BITE is fabricated in an in-house BiCMOS process. The size of the BITE is 0.026 square millimeters, and the measured phase accuracy at 30.5 GHz is with an RMS error of 5.2 degrees.
Advancing Political Bias Detection: A Novel High-Accuracy Model

Media bias is a persistent departure from the core values of objectivity and accuracy in journalism. Instead of presenting the facts objectively, media outlets and reporters frequently choose the storylines and specifics that will be included in their articles. Whether established or learned, biases have the power to dramatically influence people's views and attitudes in different societies and regions, which can lead to skewed assessments. This study intends to address the issue of biased and closed-minded attitudes in the media by introducing a novel approach leveraging deep learning. This approach beats the previous state-of-the-art models on accuracy on multiple datasets to identify political bias in the media. By highlighting these biases in political reporting we aim to offer a more unbiased evaluation of media coverage.
Tracking Funnel Control Applied on a 4-DOF Robot Manipulator: A Comparative Study

Funnel control has been proposed as an adaptive solution for systems with uncertainties without the need to repeatedly adjust the controller parameters. This work presents a funnel controller for a 4 DOF robot manipulator for tracking trajectories. The controller maintains the correct position angle of the joint while tracking a desired trajectory in the workspace of the robot. Moreover, the controller will allow predetermining and optimizing the transient response and ensures that the tracking error evolves inside a performance funnel boundary predefined by the user. Simulation results prove that the funnel controller has overcome the PID controller in reducing the overshoot and the settling time. Moreover, the funnel controller was able to effectively follow the set points with accuracy.
Computational thinking through an online game to develop soft and hard skills

Currently we see globalization and technological advances accelerating worldwide, transforming the world of work and human coexistence. We still notice that the educational systems, in their majority, are disconnected from these global realities. In this sense, it is necessary to have an educational formation more aligned to the contemporary world, preparing young people for the challenges of the 21st century, how to deal with technological resources and processes, developing soft and hard skills. In this way, it can be observed that in recent years computational thinking has gained relevance in this scenario, being a field of research in the most varied areas of knowledge, developing communication skills, creativity, leadership, problem solving, familiarity with technologies, management, transforming young people into autonomous individuals who are prepared for the technological changes that the world requires. Taking these transformations as a reference, this paper describes/presents the development of an online tool to stimulate computational thinking in elementary school, through online games. he use of the ThinkinGame makes it possible to identify the pillars to be worked on with young people, after its use, thus improving the approaches to be given in the formation of these young people, being able to turn attention to the challenge of teaching additional skills, such as sophisticated thinking and flexible problem solving.
An Ultra-Miniaturised CMOS Clock and Data Recovery System for Wireless ASK Transmission

Over the years, several clock and data recovery architectures have been proposed for wireless Amplitude Shift Keying (ASK) transmitted signals. State-of-the-art architectures mainly rely on synchronous phase-locked loop circuits or self- sampling systems, both resulting in large area consumption. This work presents a novel CMOS architecture for Clock and Data Recovery (CDR) in miniaturised and wirelessly powered implants. The proposed CDR architecture works at 433.92 MHz and includes: an ASK-demodulator, an on-chip oscillator, a power-on-reset, a control and a recovering block operating in feedback-loop. The ASK-demodulator works for a data rate as high as 6 Mbps and a modulation index in the range of 9-30%. A novel communication protocol is presented for a separated clock and data transmission. The entire CDR architecture occupies $17 \times 89 \mu m^{2}$ and consumes $15.01 \mu W$ while operating with a clock rate of 6 Mbps.
An Extremely Low-voltage Floating Gate Artificial Neuron

This paper presents an artificial neuron design, based on a floating gate (flash) transistor array, that operates with an extremely low supply voltage domain $VDD_{lo}$ (which we chose to be 100 mV). Neuron inputs and outputs are in another domain $VDD_{hi}$ (which we chose to be 0.8 V). Since $VDD_{hi}$ is the nominal supply voltage for our process, input and output data can be stored in registers without the need for level shifters. Neuron weights are stored in-memory in a flash transistor array, using a novel differential conductance encoding scheme. Our neuron performs operations in the analog voltage domain. We compare our design against a recent flash-based mixed-signal neuron design, which has reported the best results (in terms of power and energy, with insignificant error) thus far. We demonstrate that our neuron design beats the previous design in static power consumption $\mathbf{(52.9}\times$ less), static energy consumption $\mathbf{(1.41}\times$ less), and layout area (44% less). The goal of this work is to design a low-power, low-energy neuron which could be used to implement an entire Neural Network (NN). Our focus is on the neuron, and not the whole NN.
Automated and Secure Control of Industrial Internet of Things: from WSN to Open RAN Solutions

In this paper we confront traditional and novel software architectures regarding their suitability for the development of secure industrial IoT applications. First, we examine the traditional software stack of networked wireless sensor nodes deployed in an industrial scenario. Secondly, we examine edge-based architectures and 5G features standardized for demanding industrial IoT scenarios. Thirdly, we examine the O-RAN architecture and we evaluate this novel model with respect to automated network control and management capabilities. We conclude that the O-RAN model facilitates the development of intelligent data-driven applications and offers an alternative path for the efficient control of IIoT deployments.
Real-Time Detection of Crop Leaf Diseases Using Enhanced YOLOv8 algorithm

Agriculture is a mainstay of the Moroccan economy and the breadwinner for millions of farmers, offering them a broad array of crop varieties. However, a lack of resources and expertise renders it difficult for farmers to properly diagnose plant diseases. Consequently, valuable time and resources are often wasted trying to save diseased crops. To tackle this issue, a revolutionary method that uses the most recent breakthroughs in computer vision and deep learning technology to identify plant diseases in real-time has been presented. Our system employs YOLOv8 (You Only Look Once), an advanced object identification approach that analyses leaf images at a rate of 70 FPS (Frames Per Second). This innovative image analysis technique consists of splitting an image into numerous grid cells and uses a single neural network to predict the bounding box coordinates and class probabilities. This leads to a more efficient and accurate image assessment, resulting in quicker and more exact disease identification. Our results demonstrate the exceptional efficiency of YOLOv8, which outperforms conventional object identification algorithms in both speed and accuracy. This solidified YOLOv8's position as a leading solution for object detection and recognition in real-world scenarios.
Adaptive Probability Estimation Techniques for Context Adaptive Arithmetic Coding

Context-adaptive arithmetic coding is an essential entropy coding scheme used in all modern video codecs. An arithmetic coder with binary symbol size is used by video codecs like H.264/AVC, HEVC and VVC while AV1 utilizes an arithmetic coder with syntax adaptive M-ary symbols. Recently, the Alliance for Open Media (AOMedia) has initiated exploration activities towards next-generation video coding tools beyond AV1. In this regard, improvements on probability estimation techniques for the context-adaptive M-ary arithmetic coder in AV1, are explored in this paper. The proposed improvements are applied and tested on top of the reference implementation of the exploratory codec beyond AV1, known as AVM (AOMedia Video Model). Experimental results show that, compared to AVM, the proposed method achieves an average 0.27%, 0.34% and 0.32% overall BD-rate coding gains for All Intra (AI), Random Access (RA) and Low Delay (LD) coding configurations for a wide range of video content.
Hardware Efficient Reconfigurable Logic-in-Memory Circuit Based Neural Network Computing

With the explosive growth in processing and data storage capability, computing in memory (CIM) technology is considered a feasible method to mitigate the memory wall. Re-cently, a floating-gate field-effect transistor (FGFET) technology with single-layer MoS2 channel was proposed, which can flexibly transfer the memory and logic gate by setting the corresponding voltage. In this paper, based on the new FGFETs devices, we propose a hardware-efficient reconfigurable logic-in-memory (LIM) circuit to perform neural network (NN) computing. We first represent the basic FGFET array as a matrix form. Thereby, the adders, multipliers, registers, and PE units are designed by the matrix function of the arrays. The proposed circuits can dynamically transfer the memory to computing logic online when fewer data are required to be stored. A theoretical experiment based on the VGG16 network exhibits an efficiency increase of 85.35% in the same logic area compared with the traditional method.
Land Area Measurement System Design using Fastrax UP501 GPS Receiver Module

Land area measurement is now commonly required in various professions, such as agriculture and construction. For this, varieties of traditional and modern procedures are employed. Traditional processes are time-consuming and inaccurate, and they might lead to measurement errors. New methods are faster and more precise, but they are generally more expensive, and few of them needs internet access. As a result, an alternative method for measuring land area is needed. The GPS (Global Positioning System) has the potential to measure land area. GPS is a faster, more precise, simple to use, and more cost-effective denouement than any other currently available option. This research aims to develop a battery-operated easy portable GPS that can accurately measure the land area in a short amount of time, at a low cost, and with ease of use. Fastrax UP501B GPS receiver module is used in the proposed system to measure with high position accuracy.
A Comprehensive Study of Intrusion Detection within Internet of Things-based Smart Cities: Synthesis, Analysis and a Novel Approach

In order to improve the quality of human existence, comfort and efficiency are key objectives in smart environments. It is now possible to construct smart cities due to the latest advancements in Internet of Things (IoT) technology. Privacy and security are major concerns in IoT-based smart objects. Smart environments are at risk for safety from IoT-based technologies. Intrusion detection systems (IDSs) created for IoT environments are essential for preventing IoT-related security threats. Many cyber security systems use IDSs to find intrusions. Anomaly-based IDS learns the typical pattern of system activity and alerts on anomalous events as they happen as opposed to analyzing monitored events against a database of known intrusion events, as is the case with signature-based IDS. The installation of IDS on the IoT network is the main topic of this paper. Key design approach presented in this paper must be taken into consideration when developing an intrusion detection system for the Internet of Things. In this study, we use the Convolutional Neural Network (CNN) to identify attacks on nine commercial IoT devices. Using an actual N-BaIoT dataset that was taken from a real system and included both benign and harmful patterns, extensive empirical research was conducted. The testing results demonstrated a good accuracy of the CNN model in identifying botnet assaults from security cameras with accuracies of 90.25% and 91.76%. Overall, the CNN model was effective in accurately identifying botnet attacks from a variety of IoT devices.
A Review of Machine Learning Frameworks for Predicting Adverse Events in Cloud-based Healthcare Systems

The concept of “Smart Healthcare” has surfaced recently, owing to the progressions made in information technology. Implementing advanced information technologies, such as Cloud-Computing(CC), Artificial Intelligence(AI), Machine Learning(ML), and the Internet of Things (IoT), within the Healthcare sector has led to “Smart Healthcare. ” The primary objective of this approach is to enhance traditional medical practices across all domains. This study aims to comprehensively examine and evaluate the current models and frameworks centered on ML and CC in the context of Smart Healthcare, as put forth by multiple scholars. This study presents an algorithm for integrating Machine Learning with Cloud Computing.
Robocup MSL as a platform for Education and Applied Research

We report about a trajectory of 6 years in developing and improving our platform for learning and applied research on robotics at our University of Applied Sciences (UAS). The platform is driven by our research group to join the Robocup MSL (Middle Size League) competition. The research group has started to work on Robocup MSL with challenges in our regular educational program. After three semesters, we also defined challenges with students participating in an extracurricular project, as well as with students doing their graduation assignment. Currently, we are investigating how to apply the Robot Learning Environment with a self-directed student team also to sustain the learning environment. We look at the factors that affect the sustainability of the Robocup MSL at our UAS.
Blue’s Clues: Practical Discovery of Non-Discoverable Bluetooth Devices

Bluetooth is overwhelmingly the protocol of choice for personal area networking, and the Bluetooth Classic standard has been in continuous use for over 20 years. Bluetooth devices make themselves Discoverable to communicate, but best practice to protect privacy is to ensure that devices remain in Non-Discoverable mode. This paper demonstrates the futility of protecting devices by making them Non-Discoverable. We introduce the Blue’s Clues attack, which presents the first direct, non-disruptive approach to fully extracting the permanent, unique Bluetooth MAC identifier from targeted devices in Non-Discoverable mode. We also demonstrate that we can fully characterize device capabilities and retrieve identifiers, some of which we discover often contain identifying information about the device owner. We demonstrate Blue’s Clues using a software-defined radio and mounting the attack over the air against both our own devices and, with institutional approval, throughout a public building. We find that a wide variety of Bluetooth devices can be uniquely identified in less than 10 seconds on average, with affected devices ranging from smartphones and headphones to gas pump skimmers and nanny-cams, spanning all versions of the Bluetooth Classic standard. While we provide potential mitigation against attacks, Blue’s Clues forces a reassessment of over 20 years of best practices for protecting devices against discovery.
Security Scalability of Arbiter PUF Designs

Physically Unclonable Functions (PUFs) are hardware security primitives that can offer an alternative lightweight security solution for authenticating constrained Internet of Things (IoT) devices. However, PUFs are susceptible to modeling attacks, requiring the adoption of various design approaches to increase their resiliency. Many research efforts propose design approaches that offer better security against modeling attacks. This work investigates state-of-the-art modeling attacks performed on well-known Arbiter-based PUF architectures highlighting the best-fit modeling algorithm for different design approaches. Furthermore, the area efficiency of studied PUF designs is examined, and the optimal PUF design approaches for various area constraints are suggested. Such an assessment is required to evaluate PUF security accurately and guide the PUF community toward better practices. The findings revealed that some machine-learning algorithms performed better on a particular design. Additionally, when considering area overhead, we found that some PUF designs offer less security per area unit than their simpler counterparts. Accordingly, certain design elements are more efficient and add more security.