Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Birds with One Stone: Efficient Partitioning Attacks on Interdependent Cryptocurrency Networks

The biased distribution of cryptocurrency nodes across Autonomous Systems (ASes) increases the risk of spatial partitioning attacks, allowing an adversary to isolate nodes by hijacking AS prefixes. Prior works on spatial partitioning attacks have mainly focused on the Bitcoin network, showing that the prominent cryptocurrency network can be paralyzed by disrupting the physical topology through BGP hijacks.Despite the persisting threat of BGP hijacks, Bitcoin and other cryptocurrencies have not been frequently targeted, likely due to their shielded overlay topology, which limits the exposure of physical network anomalies. In this paper, we present a new perspective by examining the security of cryptocurrency networks, considering shared network resources (network interdependence). We conduct measurements extending beyond the Bitcoin network and analyze commonalities in Bitcoin, Ethereum, and Ripple node hosting patterns. We observe that all three networks are highly centralized, predominantly sharing the common ASes. We also note that among the three cryptocurrencies, Ripple does not shield its overlay topology, which can be exploited to learn about the physical network anomalies. The observed network anomalies present practical attack strategies that can be launched to target all three cryptocurrencies simultaneously. 1 We supplement our analysis by surveying recent BGP attacks on high-profile ASes and recognizing a need for application-level countermeasures. We propose attack countermeasures that reduce the risk of spatial partitioning, notwithstanding the increasing centralization of nodes and network interdependence.
VADtalk: An Internet of Vehicles Platform Facilitating Anomaly Detection Modeling and Deployment for Self-Driving Vehicles

In recent years, self-driving vehicles have gradually been appearing on the road, but society has also begun to worry about the possibility of accidents caused by the anomaly self-driving system. Many researchers have begun to study the anomaly detection of self-driving vehicles, and each has proposed different detection algorithms. However, since self-driving vehicles are not yet popular, how to collect data, simulate attacks, and verify and compare multiple algorithms is a major obstacle to research. In this regard, we built an Internet of Vehicles platform, VADtalk, that facilitate anomaly detection modeling and deployment for self-driving vehicles. VADtalk contains programs such as anomaly detection model training and vehicle connection. When developers complete model uploading and setting through the GUI, the platform will automatically collect self-driving data, train the model, and even verify the operation of the model using a self-driving simulator, and then provide the results to the developer. After the developer determines the model, VADtalk can connect the trained model with the self-driving vehicle to actually perform real-time anomaly detection on it.
New Planar Magnetic Monopole Antenna with Periodically Arrayed Patches and Equilateral Slots for Reduced Size and Improved Bandwidth

In recent years, various researches onto microstrip patch antennas have been actively conducted because of a low profile, a light weight, a small volume and an easy fabrication [1]. As a microstrip patch antenna yields a narrow bandwidth [2], a stacked multi-resonator structure [3] and a coupled feed method [4] have been proposed to improve its bandwidth. However these schemes are complicated in a structure due to a multilayer configuration. In this paper, a new planar magnetic monopole antenna is proposed to improve the bandwidth and reduce size of the antenna via an electromagnetic band-gap (EBG) structure [5], [6]. The proposed antenna consists of isotropically periodic hexagonal patches with equilateral slots on the top side and equilateral triangular slots on the ground plane. This magnetic monopole antenna yields a different radiation pattern compared to a conventional patch antenna and a large bandwidth with a size reduction. The design and analysis of the proposed antenna is described in section 2 and experimental results are discussed in section 3.
Open-Source Security Testing Tools for IoT Protocols - MQTT and Zigbee

The Internet of Things (IoT) has become an integral part of our lives, but it also poses a major security threat. As the number of IoT devices grows and more data is collected about us, malicious actors are taking advantage and launching attacks on vulnerable systems. Offensive security testing is a proactive approach of securing computing infrastructure through adversary simulation. This involves, understanding different attack tools that are available and how they can be used to attack devices. This paper studies the state of open-source tools which are available to test IoT networks that use the Zigbee and MQTT protocols. Usage of these tools are explained with attack examples, challenges are listed and enhanced tooling capabilities are proposed.
GRASPE: Accurate Post-Synthesis Power Estimation from RTL using Graph Representation Learning

In this paper, we propose GRASPE, a graph representation learning-based methodology to accurately estimate post-synthesis average power consumption from the RTL to expedite the time to market in the ASIC design. Our proposed methodology uses novel graph neural network architecture (GNN) to work on unoptimized and unmapped post-translated netlist files. The GRASPE learns to propagate the average toggle rates with embedded feature values as vectors on each logic cell during training and then predicts the average toggle rates of a new design during testing. We attain a mean improvement of 19.84% and 4.42% in average toggle rates prediction, 14.12% and 2.67% in average power estimation over the commercial RTL power estimation tool and Graph Convolutional Network as GNN, respectively and 17.96X faster than the commercial gate-level power estimation tool. Subsequently, we evaluate GRASPE with the state-of-the-art GRANNITE for inference latency and average power estimation and demonstrate an average improvement of 3.985X and 1.28%, respectively.
DiCent: A Distributed Credit Incentive Mechanism for Opportunistic Networks

Incentive mechanisms are increasingly needed in opportunistic networks that contain nodes with selfish behavior. For this, credit-based mechanisms need a virtual bank (central entity) to promote the incentive. However, the existence of this central entity in an opportunistic network may be challenging. Therefore, we propose a credit incentive mechanism in this paper. The main contribution is that the mechanism does not use a virtual bank to distribute credits (reward for forwarding messages) but rather a decentralized approach. In addition, we propose mathematical modeling to represent the collection and distribution of credits to avoid Edge Insertion attacks in some instances. Finally, the proposed mechanism was evaluated through simulation using real mobility traces and different routing protocols and compared its performance with the RELICS incentive mechanism. The obtained results show that the proposed mechanism is promising in diminishing the occurrence of selfish nodes.
Experimental Modelling of Multiple Support Excitation of Long Span Bridges

Eurocode 8 Pt 2 recommends that bridges longer than 400m, or bridges built on significantly varying ground types, should be assessed under the effects of multiple support excitation (MSE). However, previous analytical work has suggested that for shorter bridges MSE can also have an effect on the response. This paper outlines the results of experimental tests that confirm that for bridges as short as 200m, multiple support excitation can have a significant effect on the response of the bridge. For the bridge configurations tested, the displacements caused by multiple support excitation were up to 36% larger than when multiple support excitation was not taken into consideration. To generate the multiple input motions to each of the piers the bridge model was shaken by five parallel single axis shaking tables. The response of the bridge to three different sets of input time histories are presented in this paper. Type 1A and 1D response spectrum compatible time histories, which correspond to hard and soft soil conditions, and real data from the SMART1 array in Taiwan. The tests show that when a time delay (caused by a finite ground wave velocity) is introduced between the input motions to each pier, there is a larger response in the first and third piers, whilst there is no reduction in response of the middle pier. This occurs because the synchronous input (with all piers exactly experiencing the same input motion) only excites the symmetrical first and third modes, whilst the time-delayed input excites all of the first three modes. These typical results reveal that if we consider synchronous excitations only in the design of bridges, the response of some piers may be underestimated and hence the design may be unsafe. At present, the Eurocode 8 pt 2 does not require bridges of the length being modelled to be analyzed for MSE if the ground conditions are homogenous. However, these results reveal that even though the input motion was the same for all inputs, with only a small time delay between inputs at each pier, the response of the outer piers was significantly increased.
A 110nW Always-on Keyword Spotting Chip using Spiking CNN in 40nm CMOS

This paper presents an ultra-low power keyword spotting (KWS) chip for Artificial Intelligence of Things (AIoT) device's always-on ambient sensing function. The core KWS engine is based on a spiking convolutional neural network (SCNN) model for its attractive features of sparse activation and addition-only operations inside the spiking neurons. The proposed SCNN model improves the existing framewise incremental computation flow by adding a spike processing unit (SPU) to reduce the computing cycles. The power and latency of the whole system are reduced by 16.5% and 43.2% respectively. Extensive network quantization reduces the weight bit-length to 4-bit and only 1-bit activation is required. The chip also supports power gating by an energy-based voice activity detection (VAD) module to further reduce power consumption in random and sparse event (RSE) scenarios. Full chip simulation results show that the chip consumes only 110nW with 2.15% False alarm rate and 3.00% False reject rate in a 10% voice event stream test. It achieves state-of-art recognition accuracy of 99% and 96% for one and two keyword detection tasks.
A $2.53 \mu \mathrm{W}/\text{channel}$ Event-Driven Neural Spike Sorting Processor with Sparsity-Aware Computing-In-Memory Macros

Spike sorting processors with high energy efficiency are widely used in large-scale neural signal processing tasks to monitor the activity of neurons in brains. This paper presents a low-power processor for high-accuracy spike sorting and on-chip incremental learning using an algorithm-hardware co-design approach. The processor introduces an event-driven mechanism with adaptive-threshold detection to conditionally activate the system in order to reduce power consumption. Sparsity-aware computing-in-memory (CIM) macros are also developed in our design to store templates and perform complicated computations efficiently. The prototype is designed using 28nm technology with an area of 0.018 mm2/channel and an overall power efficiency of $\mathbf{2.53} \mu \mathbf{W}/\mathbf{channel}$ and 84nW/(channel.cluster) at the voltage of 0.72V. Moreover, the accuracy of the whole design can reach 94.5% in a 32-channel scenario.
Performance Analysis of Speed Control System using Voice Command and Ultra Sonic Sensor Based Approaches

This paper represents an experimental comparison of controlling the speed of the DC motor using voice commands and ultrasonic sensor-based control. The aim of the experiment was to compare the performance of both control methods in terms of speed control, distance measurement, and precision. An Arduino microcontroller, a DC motor, a microphone, and an ultrasonic sensor were used in the experiment. The Arduino was programmed to recognize certain keywords from the voice commands and adjust the speed of DC motor accordingly, in the case of voice command-based control. Regarding the ultrasonic sensor-based control, the Arduino was programmed to gauge the distance to an object using the ultrasonic sensor, and then modify the speed of the DC motor accordingly. The outcomes demonstrated that although both control methods could handle the DC motor's velocity, the ultrasonic sensor-based control performed better in terms of precision and distance measurement. However, the voice command-based control had the advantage of being more userfriendly and intuitive. The paper includes a detailed comparison of the performance of voice command-based control and ultrasonic sensor-based controlling of speed control of DC motor. It highlights the advantages and disadvantages of each method, and it can be used as a reference for selecting the appropriate control method for a specific application.
A Multi-Lidar-based Point Cloud Acquisition Platform and Data Fusion for Autonomous Vehicle in Complex Urban Environment

Autonomous driving has become the focus of research and development in recent years. However, the capability of precise positioning and obstacle avoidance for autonomous driving often rely on accurate and dense point cloud images. In particular, the environment in the metropolitan area is relatively complex and there are many viaducts and expressways in modern cities. The spatial features obtained from flat roads are no longer sufficient. If we can obtain the features of higher buildings or higher landforms, a better positioning information and security can be provided when autonomous vehicles are driving on expressways or elevated roads. In addition, the high density of vehicles in the metropolitan area and vision blind spots of vehicles also form a great safety concern for autonomous driving. In order to provide higher density point cloud information so that autonomous driving can ensure the accuracy and safety of navigation and positioning when operating in metropolitan areas, we propose in this paper a data acquisition platform base on multi-LiDAR (VLP-16) as well as a data calibration and fusion algorithm to solve the problems of vision blind spots, low vertical resolution, and sparse high-level point clouds in most of the LiDAR-based system. The proposed system adopts the industrial computer (IPC) of x86 architecture and Robotic Operating System (ROS) for overall operation. In addition, the homogeneous transformation is applied for the calibration and fusion of multi-Lidar point cloud coordinate system. Experimental results have proved that the proposed system can obtain reliable point cloud data, effectively improve the vertical resolution of the point cloud, and increase the point cloud density by more than three times, which can effectively improve the positioning reliability of autonomous driving in metropolitan areas.
Bootstrapped Complementary Switches for High-Precision Sampling

This paper presents an analog switch circuit for high-precision sampling in switched-capacitor-based analog-to-digital converters (ADCs). The proposed circuit is based on a bootstrapped complementary CMOS switch, which minimizes errors from channel charge injection while maintaining a constant conductance over the entire input range. The residual error caused by the coupling from the clock line is further reduced by an advanced clock-boosting scheme using two additional bootstrapping capacitors. An alternative design implementation based on a new gate drive circuit is also proposed, which circumvents the need for the charge pump voltage booster. Capacitive trimming DACs are included in the switch to calibrate and correct variations of charge injection over process corners. The proposed switch has been designed in a standard 180-nm CMOS technology and verified by simulation. It demonstrates better accuracy than a conventional CMOS switch and a unilateral bootstrapped n-type switch.
Deep Reinforcement Learning Based on Parked Vehicles-Assisted for Task Offloading in Vehicle Edge Computing

Vehicles may produce a lot of data that is timesensitive and computationally intensive due to the quick development of on-board applications. Due to the limitation of vehicle computing power and battery capacity, these data cannot be processed in time. Vehicle edge computing (VEC) is increasingly used to solve this problem because of its greater computing power. This paper proposes a VEC task offloading model based on improved Q-learning algorithm. First of all, we establish the system model. Due to the complexity of the system model environment, we adopted the reinforcement learning (RL) algorithm, but the environment space and action space in RL are large, which will lead to slow convergence of the model. We combine RL and deep learning (DL) to increase convergence efficiency, and to convert the task of maintaining the value function table, we utilize deep reinforcement learning (DRL) by training a neural network model. The revised Q-learning algorithm’s solving procedure is then thoroughly introduced. The simulation outcomes demonstrate that as training times are increased, the training loss of the enhanced Q-learning algorithm gradually declines and tends to converge to zero. It has been confirmed that our suggested approach does a goodjob of evaluating the system cost. As the number of vehicles on the route increases, the system experiences an increase in time cost. We also compare the traditional Q-learning algorithm to the improved Q-learning strategy. The simulation results show that the enhanced Q-learning algorithm is much faster than the conventional Q-learning method.
WOMN: WOrMhole Attack DetectioN and Mitigation Using Lightweight Distributed IDS in IoT Network

Nowadays, the Internet of Things (IoT) is an emerging technology with a wide range of IoT devices. However, the diverse, energy-dependent, and resource-limited nature of IoT devices poses significant challenges to implementing security measures, making them susceptible to cyber threats. A particularly debilitating attack at the network layer of the IoT protocol stack is the WormHole Attack (WHA), which disrupts routing algorithms by feeding the network incorrect route information and compromising location-dependent protocols. This paper presents the development and application of a novel intrusion detection system (IDS) agent designed specifically for IoT environments, aimed at detecting and identifying both the occurrence of a WHA and the attacker nodes. The system uses the path of the data packet, time delay, and hop count to detect the presence of a WHA. Given that distributed IDS agents tend to generate substantial network traffic in an IoT context, this study introduces the utilization of the MapReduce technique to enhance the efficacy of the approach. The map and reduce functions work in tandem to scrutinize traffic and establish thresholds for detecting wormhole attacks in the IoT network. This proposed security solution is not only energy-efficient and scalable, making it ideal for IoT environments with limited resources, but it also boasts a rapid attack detection time (18-26.2 Sec.), high accuracy, and enhanced network performance.
Laser-driven strong magnetic fields and high discharge currents : measurements and applications to charged particle transport

The problem of strong quasi-static magnetic field generation is a challenge in laser-plasma interaction physics. Proposed 30 years ago, the use of the laser-driven capacitor-coil scheme, which stands out for its compact design while not needing any additional pulsed power source besides the laser power, only recently demonstrated its potential. This thesis work aims at characterizing the underlying physics and at developing this scheme. We demonstrated the generation of strong quasi-static magnetic fields by laser (500 J, 1 ns-duration and ~10^17 W/cm^2 intensity) of several hundreds of Teslas and duration of 2-3 ns. The B-field space- and time-evolutions were characterized using three independent diagnostics: B-dot probes, Faraday rotation and proton-deflectometry). The characterization of the underlying physical processes involved also X-ray diagnostics of the laser-irradiated zone and optical shadowgraphy of the coil rod expansion. A novel application of externally applied magnetic fields to guide relativistic electron beam in dense matter has been carried out and the obtained results set the ground for improved high-energy-density transport in matter. Indeed, allowing sufficient time for the dense target magnetization, a factor 5 improvement of the electron energy-density flux at 50 µm-depth was evidenced. Besides, the generation of high discharge currents consecutive to short laser pulse irradiation (50 J, <1 ps-duration and ~ `10^19 W/cm^2 intensity) was also pointed out. Proton imaging of the discharge permitted to measure the propagation of an electromagnetic wave at a velocity close to the speed of light. This wave, of ~40 ps-duration, was used as an electromagnetic lens to focalize and energy-select a narrow energy range within a multi-MeV proton beam (up to 12 MeV) passing through the coil. All-above experimental measurements and application results were thoroughly compared to both computer simulations and analytic modeling. The applications of this thesis work in a near future will concern: - inertial confinement fusion, by guiding relativistic electron beams up to the dense core nuclear fuel, and by confining particles depositing their energy in it, or even those resulting from the fusion reactions; - laboratory planetology and astrophysics, by generating secondary sources of energetic particles and radiation to reach the warm-dense-matter state or by magnetizing plasmas to reproduce astrophysical phenomena in scaled experiments; - and finally, the control of charged particle beams in vacuum, useful in particular for the development of laser-driven sources for distant applications in science, industry or even medecine.
Performance of Bio-Oil Modified Asphalt Binders Containing Warm-mix Additives

This study focuses on assessing the high-temperature performance of warm mix asphalt binder (WMB) modified with various quantities of date seeds oil (DSO). The penetration, softening point, rotating viscosity tests, and Dynamic Shear Rheometer (DSR) test at the high-temperature range were used for this assessment. The results showed that adding higher percentages of DSO to WMB is not advised; however, the results reveal an improvement in the performance at high temperatures compared to the control asphalt binder when (1.5% DSO) was used.
Comparative analysis of Hybrid adders with different Technology node using cadence virtuoso

Different Hybrid Full Adder cells with vari-ous design approaches have been proposed by researchers, each with its advantages and disadvantages and implemented in higher technology node. It is therefore important to compare FA cell performance in a systematic manner utilising a standard simulation platform. In this paper, we have given a thorough investigation of FA cells and implemented previous researchers work in 45nm Cadence Virtuoso tool, also validated the results in 90 and 180nm. we examined the performance of 9 existing 1-bit FA cells operating at various voltages and three distinct GPDK technology nodes. All the adders have been compared in terms of delay, power consumption, and power delay product (PDP). And after comparing all the hybrid adders we have observed that hybrid adder proposed by jyoti kandpal provides lower PDP compare to all the adders, adder proposed by jyoti kandpal provides improvement of 59.53%, in terms of PDP by lowering technology node 90nm to 45nm.