Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Integral Terminal Synergetic Control of a Disturbed Quadcopter with Variable Geometry

This paper, puts forward the control of a quadcopter with variable geometry exposed to external disturbances. Using a finite Integral Terminal Synergetic Control (ITSC) scheme, which was designed to solve the trajectory tracking problem. This controller has the advantages of: use a continuous control law that eliminates the chattering phenomenon, convergence of states in finite time, good tracking performance and simple structure. Moreover, it is well suited in practice to digital implementation. The dynamics of the proposed system are derived on the basis of Newton-Euler formalism, followed by a robust control method. Lyapunov framework is exploited to ensure the stability of the controlled system. Simulation results of the designed ITSC, are exhibiting the ability of the proposed control technique for rapidly achieving the desired trajectories in a robust manner. A comparative study in both quantitative and qualitative case is carried out between ITSC and Sliding Mode Controller (SMC). Overall, the findings show that the proposed control technique outperforms in terms of accuracy and robustness.
Cyber Hacking Breaches Prediction and Detection Using Machine Learning

Cyber hacking breaches prediction is one of the emerging technologies and it has been a quite challenging task to recognize breaches detection and prediction using computer algorithms. Making malware detection more responsive, scalable, and efficient than traditional systems that call for human involvement is the main goal of applying machine learning for breaches detection and prediction. Various types of cyber hacking attacks any of them will harm a person's information and financial reputation. Data from governmental and non-profit organizations, such as user and company information, may be compromised, posing a risk to their finances and reputation. The information can be collected from websites that can trigger cyberattack. Organizations like the healthcare industry are able to contain sensitive data that needs to be kept discreet and safe. Identity theft, fraud, and other losses may be caused by data breaches. The findings indicate that 70% of breaches affect numerous organizations, including the healthcare industry. The analysis displays the likelihood of a data breach. Due to increased usage of computer applications, the security for host and network is leading to the risk of data breaches. Machine learning methods can be used to find these assaults. By research, machine learning models are utilized to protect the website from security flaws. The dataset can be obtained from the Privacy Rights Clearinghouse. Data breaches can be decreased by educating staff on the use of modern security measures. This can aid in understanding the attacks knowledge and data security. The machine learning models like Random Forest, Decision Tree, k-means and Multi-layer Perceptron are used to predict the data breaches.
Enhancing Cyber Security in Industrial Internet of Things Systems: An Experimental Assessment

In the digital age, Industry Control Systems (ICS) and the Industrial Internet of Things (IIoT) are having an impact on the operations of industry sectors. Although, with the development of technology even cyber-attacks have advanced in methods and techniques. The systems, which manage and control critical infrastructures such as power plants, manufacturing facilities, and transportation networks, are exposed to cyber-attacks. Nevertheless, security breaches can come from various cyber-attacks that target industrial systems, and in some cases, an infected device can affect the entire infrastructure in more disastrous attacks. This article digs into the existing models of ICS where we compare in terms of cyber-attacks, timely response, and impact control in the system is presented. This investigative work helps identify differences and create a proactive approach to security response. Finally, we will make an experimental assessment of the impact of security on existing models, in terms of recommendations for the advancement of cyber security in ICS and IIoT systems. Furthermore, we will present the different approaches that are now used to protect ICS and IIoT systems from cyber-attacks. In conclusion, the test found that the IIoT sensor and system are vulnerable to attacks used against it but not from malicious code injection.
Design Enablement of 3-Dies Stacked 3D-ICs Using Fine-Pitch Hybrid-Bonding and TSVs

Multi-dies stack 3D-ICs are an extension of traditional 2-dies 3D-ICs to address the memory wall and footprint problems. This paper presents a complete Place-and-Route (PnR) flow to enable 3-dies stacked 3D-ICs from netlist partitioning to timing analysis, including original cross-dies co-optimization steps. The proposed flow is based on Integrity™ 3D-IC tool from Cadence. To demonstrate the flow, openPITON-T1 Tile design with IMEC N2 Process Design Kit (PDK) is used. The same design is implemented in normal 2D PnR flow and the proposed 3-dies stack flow. Our results show that a 3-dies stack design can achieve up to 11.4% increase in effective frequency and 50% less system footprint when compared with its 2D counterpart.
Parallelizing Non-Neural ML Algorithm for Edge-based Face Recognition on Parallel Ultra-Low Power (PULP) Cluster

The multi-core parallel ultra-low power (PULP) cluster architecture allows the IoT edge node to shift toward near-sensor computing. In this paper, non-neural Eigenfaces-based face recognition (FR) is examined on an octa-core PULP cluster. It is possible to achieve high accuracy in the Eigenfaces-based algorithm without using a large data model. It is observed that the Eigenfaces-based face recognition algorithm achieved 93% accuracy on the PULP platform with a $4.55\times$ lesser model size compared to the state-of-the-art SqueezeNet1.1-based FR algorithm on GAP8 platform. Parallelization of Eigenfaces-based face recognition is done to achieve maximum speed-up on multi-core, reducing recognition time. Furthermore, DMA-based communication between the fabric controller and multi-core cluster reduces the recognition time by $50\times$ at the cost of a little degradation in speed-up on the multi-core. By adopting this technique, 165 faces per second are recognized with 93% accuracy on octa-core PULP cluster, which is $7.85\times$ faster than a single core RISC-V with DMA. Compared to the ARM Cortex-M7 architecture, the multi-core PULP cluster reduces recognition time by 89.89%. These results make the multi-core PULP cluster an efficient choice for Eigenfaces-based face recognition on the edge.
Coded Distributed Computing for Sparse Functions With Structured Support

Coded distributed computing (CDC), originally proposed by Li et al., leverages coded multicast messages to exchange computed intermediate values among the distributed computing nodes, such that the overall communication load could be reduced by a factor of r, the number of input files assigned to each node. However, in the original CDC framework, each output function/task is composed of intermediate values from all input files. In this paper, we propose a new CDC problem for sparse functions with structured support, where each output function depends on a subset of the input files. For a symmetric structured support for which the input files are divided into G equal-length batches and each output function depends on the same number of G′ batches, we propose a novel CDC scheme that is strictly better by a factor G/G′ than directly employing the original CDC scheme in the considered problem. Furthermore, by proposing a new converse bound, we prove that the communication load of the proposed CDC scheme is order optimal within a constant multiplicative factor of 6.
Hyperparameter-free Weighted Median Operator for Robust Spectral Analysis in Heavy-Tailed Noise Environment

The least absolute shrinkage and selection operator (LASSO) regression has been widely used for high-resolution spectral line estimation. Since the derivation of the LASSO is based on $l$-2 norm, it loses robustness in environments with heavy-tailed noise. combining the LAD and LASSO criteria, the least absolution deviation (LAD)-LASSO can effectively resist outliers and heavy-tailed errors. However, this method encounters the problem of optimal artificial parameter selection. In this paper, a robust Semi-Parametric/SParse Iterative Covariance-based Estimator (SPICE) is first introduced, which significantly eases parameter selection. Secondly, a weighted median algorithm is uesd to robustly estimate each frequency coefficient in a coordinate descent framework. The proposed method can effectively resist outliers and heavy-tailed errors without tuning user parameter, and greatly improve the computational efficiency compared with traditional SPICE. Simulations verify the effectiveness of the proposed algorithm.
A Statechart Template Library for Iot System Modelling

In this paper, we propose a library of statechart templates for designing IoT systems. We have developed atomic statechart components modelling the heterogeneous aspects of IoT systems including sensors, actuators, network, and controller. Base system units for smart systems have also been designed. A component for calculating power usage is available in the library. Additionally, a smart hub template that controls interactions among multiple IoT systems and manages power consumption has also been proposed. The templates aim to facilitate the modelling and simulation of IoT systems. Our work is demonstrated with a smart home system consisting of a smart light, a smart microwave, a smart TV, and a smart fire alarm sys-tem. We have created a multi statechart with Yakindu based on the proposed templates and components. A smart home simulator has been developed by generating controller code from the statechart and integrating it with a user interface.
A Latency-Optimal Task Offloading Scheme Using Genetic Algorithm for DAG Applications in Edge Computing

Edge computing technology presents an opportunity for embedded devices with limited computing power to effectively process even the most complex of applications. Scheduling tasks to the edge server for processing can effectively reduce task execution latency for the user device. However, current task offloading approaches overlook the unique topological relationships and scheduling within tasks by treating user device tasks as a single entity, leading to the underutilization of computing resources. In this paper, the fine-grained task offloading problem is addressed by considering the offloading with precedence constraints among tasks. This approach enhances task parallelism between edge servers and user devices by allowing tasks to be offloaded and executed on different processors. However, this also makes the problem more challenging since the task scheduling sequence and decision-making process become more complex. A lightweight and efficient offloading decision is proposed in this paper for the single-server scenario. This decision enables scheduling of multi-user tasks in sequence, choosing the most appropriate location for execution. The approach was then extended to a multi-server scenario, where the optimal server for each user device is determined using Genetic Algorithm(GA) optimization techniques, resulting in the minimum average computational latency of the task. Experimental results demonstrate that this approach outperforms existing schemes in terms of task execution delay and offloading efficiency.
AV-HuBERT with Multi-Resolution Attention

In the context of self-supervised representation learning for audio-visual speech understanding, we hypothesize that the value of historical context is non-uniform like it is in language, varying in relevance to the prediction of the next output segment in a sequential model architecture, and that attention weights would benefit from similar non-uniformity. To this end, we have augmented the multi-modal (speech and video) AV-HuBERT architecture with adaptive context pooling (ACP) to incorporate a form of multi-resolution attention in the decoder. ACP previously has been shown to improve the performance of machine learning models for neural machine translation, language modeling, and image classification, and should be applicable here. Through this modification, we expected to reduce the word error rate (WER) on a downstream lip-reading task using the Lip Reading Sentences 3 (LRS3) dataset as compared to the original AV-HuBERT implementation which uses standard self-attention in its transformer layers. The results of our augmentation suggest that ACP affords only a small benefit to AV-HuBERT in terms of WER during inference of speech from mouth-cropped videos of talkers, but increases the model training time by a factor between 5 and 10. Surprisingly, performance metrics for training the model, such as sub-word accuracy and perplexity, were not well correlated with WER at inference time.
A Task Scheduling for Digital Array Radar Based on Reinforcement learning

Target tracking is the key technology of the Digital Array Radar (DAR). The adaptive radar task scheduling interval can exert the advantage of digital array beams, significantly improving the target tracking performance. This article introduces the adaptive task scheduling interval technology in the task scheduling algorithm. This technology is an adaptive task scheduling interval scheme based on Reinforcement Learning (RL), realizing better resource scheduling through priority competition mechanisms. The adaptive task scheduling interval can not only timely compensate for the target movement status information during the tracking process but also increase the measurement quantity to improve the stability of tracking maneuverability. Due to the limited radar resources, reducing the tracking task scheduling interval restricts the system tracking capacity and the ability to complete other functions. Adaptive scheduling interval technologies should maintain a certain degree of balance between search and tracking. Finally, the adaptive scheduling interval control technology is simulated and verified.
Hyperspectral Anomaly Detection via Structured Sparsity Plus Enhanced Low-Rankness

Hyperspectral anomaly detection (HAD), distinguishing anomalous pixels or subpixels from the background, has received increasing attention in recent years. Low-rank representation (LRR)-based methods have also been promoted rapidly for HAD, but they may encounter three challenges: 1) they adopted the nuclear norm as the convex approximation, yet a suboptimal solution of the rank function; 2) they overlook the structured spatial correlation of anomalous pixels; and 3) they fail to comprehensively explore the local structure details of the original background. To address these challenges, in this article, we proposed the structured sparsity plus enhanced low-rankness (<inline-formula> <tex-math notation="LaTeX">$\text{S}^{2}$ </tex-math></inline-formula>ELR) method for HAD. Specifically, our <inline-formula> <tex-math notation="LaTeX">$\text{S}^{2}$ </tex-math></inline-formula>ELR method adopts the weighted tensor Schatten-<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> norm, acting as an enhanced approximation of the rank function than the tensor nuclear norm (TNN), and the structured sparse norm to characterize the low-rank properties of the background and the sparsity of the abnormal pixels, respectively. To preserve the local structural details, the position-based Laplace regularizer is accompanied. An iterative algorithm is derived from the popular alternating direction methods of multipliers. Compared to the existing state-of-the-art HAD methods, the experimental results have demonstrated the superiority of our proposed <inline-formula> <tex-math notation="LaTeX">$\text{S}^{2}$ </tex-math></inline-formula>ELR method.
Intelligent Prediction and Analysis System of Green Building Energy Consumption Based on BP_AdaBoost Algorithm

By predicting the future energy consumption of buildings, energy managers can judge the energy consumption trend of buildings in advance and implement energy procurement and energy regulation strategies in a planned way. It is the most basic work in building energy conservation projects. There are many methods to predict building energy consumption, including neural network method, software simulation method, gray system method, etc. This paper puts forward BP_AdaBoost algorithm (BPAA), studies and analyzes the IP and analysis system of green building(GB) energy consumption, and briefly introduces the classification of energy consumption prediction models and building design factors; Proposed BPAA, and analyzes the recognition process and steps of the algorithm. Finally, BP is established AdaBoost algorithm GB energy consumption IP model, through the comparative experiment with BP algorithm model, the results show that considering the overall effect, BP-The prediction effect of AdaBoost model is better than that of BP model. The BP proposed in this paper is verified- AdaBoost model has good prediction accuracy and convergence effect.
Generalization Properties of Adversarial Training for -ℓ0 Bounded Adversarial Attacks

We have widely observed that neural networks are vulnerable to small additive perturbations to the input causing misclassification. In this paper, we focus on the ℓ0-bounded adversarial attacks, and aim to theoretically characterize the performance of adversarial training for an important class of truncated classifiers. Such classifiers are shown to have strong performance empirically, as well as theoretically in the Gaussian mixture model, in the ℓ0-adversarial setting. The main contribution of this paper is to prove a novel generalization bound for the binary classification setting with ℓ0-bounded adversarial perturbation that is distribution-independent. Deriving a generalization bound in this setting has two main challenges: (i) the truncated inner product which is highly non-linear; and (ii) maximization over the ℓ0 ball due to adversarial training is non-convex and highly non-smooth. To tackle these challenges, we develop new coding techniques for bounding the combinatorial dimension of the truncated hypothesis class.
Design and experiment of the pill-box window for 0.67THz TWT

This paper described the design and tested results of the pill-box window for the 0.67THz travelling wave tube (TWT) with the help of 3-D simulation software. The test results show that the design of the pill-box window satisfied the need of the TWT. The S21 parameter is -3.1 dB, and the S11 parameter is less than -10 dB from 649GHz to 672GHz. It bases the manufacture of the 0.67THz travelling wave tube.
Development of Nitride-Based Optical Phased Array at 1092 nm for the Optical Addressing of 88Sr+ Ion

Optical phased array (OPA) structures are designed and fabricated for the optical addressing of trapped <sup>88</sup>Sr<sup>+</sup> ions. Laser light of 1092 nm wavelength corresponding to the <inline-formula> <tex-math notation="LaTeX">$^{2}\text{D}_{3/2}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$^{2}\text{P}_{1/2}$ </tex-math></inline-formula> of <sup>88</sup>Sr<sub>+</sub> ion is used, where silicon nitride (SiN) platform with integrated titanium nitride (TiN) thermos-optic heaters are selected. Beam steering of <inline-formula> <tex-math notation="LaTeX">$\sim 33~\mu \text{m}$ </tex-math></inline-formula>, which corresponds to ~11.6° steering angle, is obtained by applying 10V on alternative TiN heaters in the 8-Channel OPA structure.
Real-Time Flame Detection Based on Video Semantic Statistical Features

Traditional fire detection methods are based on the analysis of the surrounding environment by physical sensors for fire detection, but there are problems such as weak anti-interference ability, easy to miss the alarm, and long response time. Video-based fire detection technology can detect fires in a more timely and accurately with high environmental adaptability. However, existing video fire detection methods still suffer from low accuracy and high false alarm rates. Therefore, in this paper, we propose a video fire detection method based on the combination of dynamic texture features and SLIC(simple linear iterative clustering) for fire detection. The method consists of three parts: superpixel generation, dynamic features extraction and support vector machine (SVM) classification. The main advantage of this method is that it can group pixels, reduce the complexity of subsequent processing tasks and comprehensively consider the temporal and spatial characteristics of flame. In order to prove the effectiveness, the accuracy of the proposed method was evaluated on three public datasets in fire detection field. The results show that the proposed method outperforms other latest similar methods.
Characterization of the Frequency Ramp Nonlinearity Impact on the Range Estimation Accuracy and Resolution in LFMCW Radars

The ranging accuracy and range resolution in linear-frequency-modulated continuous-wave (LFMCW) radars are vulnerable to the nonlinearity of the transmitted frequency ramp. The frequency ramp nonlinearity (FRN) leads to the degradation of accuracy in range estimation and deteriorates the range resolution in LFMCW radars, which becomes even worse as the target range increases. The uncertainty of the type of FRN makes the problem more complicated. This article proposes a new range-dependent resolution model and a new range-dependent accuracy model based on the theoretical analysis and experimental evaluations, which characterizes the impact of different types of FRN on the range resolution and accuracy in LFMCW radars. The proposed models have established the quantitative relationship between the FRN, the target distance, and the range accuracy and resolution. A nonlinearity index is utilized in the analysis to quantify the extent of FRN. Moreover, a novel design methodology is also proposed, which may guide the design of LFMCW radars under the influence of FRN. Four typical types of FRN that are reasonable in practical LFMCW radars are taken into consideration in both simulations and experiments to validate the proposed theory and methodology.
Performance of Fault Severity Estimation in 7-Phase Electrical Machines under Noisy Conditions

This work proposes a method for estimating fault severity in the presence of noise using the measured currents for a 7-phase electrical machine. The method is based on analytical models in stationary reference frames and analysis of the DC and fundamental components in the four fictitious machines. The slope of the decision function from the CUSUM algorithm, which will be noticeably different depending on the fault severity, is used to assess the performance of the fault severity estimation rapidly. The effects on the decision function’s slope of the fault severity estimation for different noise levels are evaluated. The simulation results show that even in presence of high noise levels, the decision function is an efficient fault estimation indicator. When the noise level is high, the decision function and its slope are noisier. Conversely, the decision function and its slope are less noisy when the noise level is low. The results also show that for the three fault types under study (gain fault, phase shift fault, and mean value fault), the current components of the fictitious machines in the stationary frames have distinct robustness to noise.