Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Step Interfrequency Interference Cancelation for Single-Carrier Underwater Acoustic Communications in Time-Varying Channels

Underwater acoustic environments pose significant challenges for frequency-domain equalization (FDE) due to the substantial impact of the Doppler effect, which causes significant inter-frequency interference. In such a condition, traditional FDE which carries out one-tap equalization in the frequency domain will not be able to achieve optimal results. To address this problem, we propose a two-step interfrequency interference cancelation (IFIC) strategy in FDE. This approach involves the division of the time-varying channel impulse response into two distinct modules: one module that gives rise to interfrequency interference (IFI) and another module that leads to inter-symbol interference (ISI). Before applying FDE, a linear minimum mean square (LMMSE) equalizer is employed to effectively eliminate the primary IFI. In the subsequent phase, we employ a hybrid time-frequency domain decision feedback equalization (DFE) technique to effectively mitigate inter-symbol interference (ISI) and any remaining interfrequency interference (IFI), which significantly enhances the overall communication performance. The feasibility and potential of the proposed scheme are proved in the simulation results.
Coupled electromagnetic-thermal analysis of electric machines including transient operation based on finite element techniques

Since the mass and volume of electric machines are heavily dependent on their thermal constraints, it is important to find ways to analyze and simultaneously optimize their electromagnetic (EM) and thermal performances. This paper presents an approach for coupling the finite element EM and thermal analyses of electrical machines using temperature-dependent material properties so that temperatures inside a candidate machine can be predicted simultaneously with its electromagnetic performance. In addition to steady-state conditions, the coupled analysis has been extended in this paper to transient operation for machines that are required to deliver high torque/power for short intervals. Three 30 kW 10-pole 12-slot surface PM machines optimized for maximum torque density, minimum cost, and maximum efficiency, respectively, have been investigated. This coupled EM-thermal analysis makes it easier for designers to maximize the winding current density to achieve the highest possible torque/power ratings within thermal limits set by the winding insulation or demagnetization limits.
GridEnclave: A Confidentiality Computing Platform for Secure Smart Grid and Multi-Party Application Execution

The existing solutions in smart grid utilizes standard encryption and decryption approach to transmit data across the network. This paradigm can only support limited security capabilities in distributed Grid and multi-party applications. To provide tamper proof application execution, data integrity and confidentiality management across distributed grid parties, a confidentiality computing platform called GridEnclave is implemented. GridEnclave creates a secure enclave (container) for application execution with confidential data access. In addition, it uses attestation framework to ensure that the desired code is executed in secure GridEnclave. In our experimental study, we have executed several smart grid applications from simple data aggregation (few lines of code) to complex machine learning algorithms (hundreds of lines of code). The execution performance of these application is compared on SCONE-Based enclave and Occlum-Based enclave. The proposed GridEnclave platform is very general and capable of executing any types of Multi-party application in a secure manner.
Machine learning Based Clustering Using Spotify Audio Features

In this paper, we study the association between different clustering algorithms. Clustering is a crucial technique for data mining applications and research. Spotify audio features were analysed using machine learning techniques that separate the provided data into various clusters according to how close or how far apart they are from one another. Without any prior understanding of the data being considered, clustering analysis offers a helpful technique to group items. This study uses k-means, fuzzy c-means (FCM), and Possibilistic C- means (PCM) algorithms to classify datasets extracted from the Spotify API of random songs into different clusters. The results show that the K-means algorithms outperform fuzzy c-means (FCM) and probabilistic c-means (PCM), using silhouette scores as a performance comparison criterion. Values of silhouette coefficient that performed the best was for K-means (n=6) and is 0.465. Additionally, we observed that K-means performed better when value of n was greater; however, FCM showed a decline in performance as n increased, whereas PCM showed a steady performance across all n clusters. Utilizing clustering algorithms for categorizing songs based on audio data gives insightful analysis and useful applications, which has been increasingly popular in recent years.
MFAM-Net:A Surface Defect Detection Network for Strip Steel via Multiscale Feature Fusion and Attention Mechanism

Detecting defects on the surface of steel strip is a critical step in the production field of the steel industry. However, due to the diversity of defect types, scales, texture structures and the irregular distribution of defects in strip steel images, existing detection methods are difficult to achieve accurate detection. To this end, we propose a steel strip surface defect detection network (MFAM-Net) based on an YOLOv5 model. Firstly, we design a multi-scale feature extraction module (MSC3) to capture more complete and comprehensive defect texture features for strip defect objects with large scale changes. Secondly, we add the coordinate attention module (CA) to the network, while the spatial pyramid pooling structure (SPPF) of the backbone network is also replaced to increase the network's ability to locate defect features. Third, we introduce a new detection branch to improve the network's detection performance for faults of various sizes. Finally, we introduce the Wise-IoU (WIoU) loss function to focus the detection network on ordinary quality anchor boxes, and enhance the network's regression capability for defect positions. Experimental results show that on the NEU-DET and GC10-DET datasets, our method achieves an mean average precision of 83.5% and 66.7%, respectively, which is 7% and 2.9% higher than the baseline network YOLOv5, and also outperforms other defect detection models.
Efficient Authentication System Based On Blockchain Using eID card

With the continuous advancement of smart cities, Internet of Things(IoT) technology is flourishing at an unprecedented rate. This has led to a rapid increase in the number of IoT devices and a consequent rise in data interactions between these devices. As a result, the management and verification of device identities have become increasingly crucial. However, current systems still rely on vast databases to store identity data and require manual intervention for authentication. This reliance on databases limits the efficiency of authentication, particularly in environments with a large number of devices. To address this challenge, we have integrated government public key infrastructure(PKI) with smart contract technology to develop an efficient and automated authentication system. Our system utilizes electronic identity cards(eID cards) as a storage medium for certificates, providing reliable identity proof. Smart contracts are then used to automatically verify the certificates and implement access control over the verification results. Our system offers not only stronger universality, reduced storage overhead, and faster authentication speed, but also higher scalability. This innovative approach paves the way for authentication in IoT devices.
Agent-Based Decision Support in Borderless fab Scenarios in Semiconductor Manufacturing

The design and the implementation of a multi-agent system (MAS) for a borderless fab scenario is described. In such a scenario, lots are transferred from one wafer fab to a nearby one to perform process steps of the transferred lots. Production planning is carried out individually for each of the wafer fabs. The modeling of the available and requested capacity in the production planning models of the participating wafer fabs is affected by the lot transfer. The transfer of the route information from one wafer fab to another to automatically generate the linear programming models is described. Production planning is carried out in a rolling horizon setting using a cloud-based infrastructure. We show by simulation experiments with the MAS with a correct modeling of the capacity in production planning results in improved profit compared to a setting where the lot transfer is not taken into account in the planning formulations.
APT Detection and Attack Scenario Reconstruction Based on Big Data Analysis

This paper firstly introduces Advanced Persistent Threat, and carries out research on the attack scenario reconstruction technique, which is the key technique for detecting Advanced Persistent Threat attacks based on big data. An alert similarity-based de-redundancy method and an SVM-based large-scale alert correlation method are designed and implemented to establish correlations for malicious attacks in the dataset. Using this method, a more intuitive and clearer intrusion attack process can be shown for network security administrators, and it is easier to understand the logical sequence of attack events. At the same time, it can realize the traceability of malicious attacks in big data, determine the source of their occurrence, and take effective preventive measures based on this.
Project Simulation, Validation and Deployment with DEVS. IoT Framework for Blooms Monitoring and Alert

Harmful Algal and Cyanobacterial Blooms (HABs) constitute a relevant public health and ecological hazard due to their frequent production of toxic metabolites, which is increased by the current vulnerability of water resources to environmental changes such as global warming, population growth, and eutrophication. These blooms have been typically assessed by combining predictive models with manual collection. However, these processes are generally independent and do not provide data with sufficient resolution to apply proactive policies. In this work, we propose a novel and integrative framework to straightforwardly combine the conception, design, and deployment of advanced Early-Warning Systems (EWSs) that will allow us to automate all the processes involved in HABs detection and management and apply proactive policies. The framework is built upon solid Modeling and Simulation (M&S) principles, through Model Based Systems Engineering (MBSE) as the driving methodology and Discrete Event System Specification (DEVS) as the M&S formalism.
Online Tuning Method for the Frequency Droop Control Parameter of a Wind Turbine Generator

As the continuous increase in wind power penetration has introduced a series of frequency problems into power systems, the study of the frequency regulation (FR) of wind power generation systems is important for their stable and safe operation. First, through the measured wind speed of an actual wind power plant, the wind speed variation trend of the incremental power generation period of a wind turbine generator (WTG) participating in FR is analyzed. It is noted that it is inappropriate to use a fixed wind speed during WTG participation in FR. Subsequently, aiming at the favorable wind speed variation process that can bring incremental power to the WTG, a method for online tuning of the frequency droop control coefficient is proposed, and a calculation formula for parameter tuning is provided. Finally, the simulation results based on three kinds of measured favorable wind speed variation patterns verify the accuracy of the proposed online parameter tuning method.
La investigación y el desarrollo como determinantes diferenciados y complementarios de la innovación y la productividad

espanolLa relacion entre la inversion en i+D y los resultados innovadores y la productividad empresarial es uno de los temas mas analizados por la economia de la innovacion. sin embargo, la mayoria de los trabajos no tienen en cuenta que la i+D esta formada por dos actividades diferentes: la investigacion y el desarrollo. En este trabajo se analiza el efecto diferenciado que cada una de estas actividades tiene sobre los resultados innovadores, asi como su efecto complementario sobre la productividad empresarial. Los resultados muestran que la investigacion y el desarrollo tienen un efecto similar sobre la obtencion de patentes, pero existen diferencias significativas al analizar otros resultados innovadores. En este sentido, el efecto de las actividades de desarrollo es mayor en la obtencion de nuevos productos, asi como para el exito economico de los mismos. Finalmente, se encuentra evidencia a favor de un efecto complementario de la investigacion y del desarrollo sobre la productividad empresarial. EnglishThe analysis of the relationship between R&D and innovation results and productivity has a long tradition in the literature on economics of innovation. However, a drawback of this literature is that R&D is mostly treated as a homogenous activity, while it includes two different activities: research and development. This article analyzes the differentiated effect of research and development on different innovation outputs, and tests the existence of complementarity between these activities in determining productivity. our results show that the impact of research and development is similar when patent applications are analyzed, but we also find interesting differences when analyzing other innovation outputs. In this sense, the effect of development expenditures is much higher for the probability of introducing product innovations. Moreover, we find that development expenditures have a larger effect on sales from new products than research expenditures. Finally, we find evidence suggesting that there is complementarity between research and development in determining productivity.
Spectrum Sensing Using Cooperative Energy and Matched Filter Detectors in Cognitive Radio

The rapid growth in the population of internet connected devices needs more freer spectrum. Therefore, cognitive radio has previously been presented as a way to solve the issue of limited spectrum resources through making use of spectrum bands that are designated for primary users. This technique enables the secondary user to access the spectrum while the primary user is not utilizing it, without getting involved with the primary user. The main issue for the secondary users when detecting the spectrum is that the primary use is not visible due to complexity of sensing results and poor noise value. In this paper, cooperative energy and matched filter detectors using an adaptive threshold have been proposed to handle this issue. The metrics that have been used to evaluate the performance of the proposed work are the probabilities of detection, misdetection, and false alarm. In addition, we simulate the work with the MATLAB R2020a software and compare it to other related works in the literature.
Research on the Application of xR Extended Reality Shooting Technology in the Experimental Drama "Pipa Xing"

The virtual 3-Dimensional (Hereinafter referred to as 3D) scene presented by the Light Emitting Diode (Hereinafter referred to as LED) screen in the Extended Reality (Hereinafter referred to as xR) virtual studio field changes in real time with the changes of the performance and shooting of real actors, so that the creativity of directors, photographers, actors, stage artists and other creative personnel can be better played and expressed in the scene. In the limited environment, the virtual space extends freely with the creativity of the creator, and the narrative is presented artistically in the virtual image. It is shown as an image creation mode with the integration of immersive virtual scene and real performance. Compared with the traditional creation mode, xR technology has a broad space for innovation in production technology and creative methods, and the research on aesthetic experience has a great value of continuous exploration and experiment. Taking the experimental drama "Pipa Xing" recorded and filmed by the China Institute of Arts Science & Technology in 2022 as an example, The drama is an important scientific research achievement of the project "extended reality Technology (xR) Applied Research in Two Innovations of Traditional Culture" established by China Institute of Arts Science & Technology. The article discusses the important topics such as the immersive experience of xR technology in creating immersive experience and the "infinite expansion" of performance space for performing arts.
Adaptive Fuzzy Control Design and Implementation of Genetic Algorithm Based Robot System

With the increasing demand for resources, the ocean has become the second most important base for the strategic deployment of resources after the land, and the motion control technology of underwater robots is of great importance to achieve various movements and stable driving underwater. The aim of this paper is to investigate the design and implementation of adaptive fuzzy control for robotic systems based on genetic algorithms. The method of optimising adaptive fuzzy control design by genetic algorithm is investigated and a fuzzy controller is designed for the underwater robot with improved genetic algorithm. The underwater robot control system is constructed through the keil software development environment. Further, experiments are conducted on the underwater robot platform to complete the path tracking control of the underwater robot, the optimised fuzzy controller can eliminate the deviation more quickly, verify the feasibility of the proposed algorithm.
A RISC-V Instruction Set Extension for Flexible Hardware/Software Protection of Cryptosystems Masked at High Orders

The paper presents an instruction set extension for the CV32E40P RISC-V processor core to protect cryptosystems against side channel attacks using masking at high orders. A first masking order is fixed in hardware at synthesis time. Then a higher masking order is obtained using software composition over the hardware one. The solution has been implemented, validated and evaluated in FPGA.
Optimizing Data Set Requirements For Pre-Eminent Wind Speed Predictions in Summer and Winter Seasons

Due to depleting fossil fuel reserves and increasing global weather concerns renewable energy resources such as solar and wind resources are the favorites of power engineers considering their ease of accessibility and abundance in nature. However, a major flaw lies in the uneven distribution of these resources around the year. Wind speed prediction enables us to predict the wind farm power output over a desired time range. In this paper wind speed has been predicted for 6–96 hours ahead, by developing the statistical time series model which predicts the current year data using the different combination of corresponding yearly data set of past N consecutive years. The paper aims at finding out the optimum value of N for each forecasting time horizon (6-96 hours) individually so that the best results are obtained. Wind speed data for location Mumbai (VABB airport) in summer and winter seasons was accumulated for analytical purposes.
Exploiting Context for Fault Detection in Solar Photovoltaic System using Context-Sensitive Recurrent Neural Network

Predictive maintenance of component (or equipment) in renewable energy systems are crucial as malfunction of one of the components (or equipment) can lead to power fluctuations, reduced performance, expensive downtime, failure of part of the system or the whole system, and even catastrophic failures. Massive amount of data is continuously generated by PV monitoring systems. The data can be used for diagnostics (fault detection and identification) and prognostics using Artificial Intelligence (AI) and Machine Learning (ML). The ML models can take the benefit of context available in the domain and acquired along with the data. This paper attempts to leverage context information towards developing a Recurrent Neural Network based model for fault detection in photovoltaic systems. We compared the performance of the proposed context sensitive model with baseline model using real dataset and obtained better results in terms of accuracy, true positive rate, and false positive rate.
Hyperspectral Image Classification with Small Number of Training Samples Based on Global Spatial-Spectral Attention Network

In recent years, deep learning has achieved significant success in hyperspectral image classification. However, deep learning heavily relies on large datasets with high-quality labels, making obtaining high-quality labeled big data difficult. Therefore, in response to the problem of small number of training samples, we proposed a hyperspectral image classification with small number of training samples based on global spatial-spectral attention network. This model includes a spatial-spectral siamese network and a global graph attention network. The spatial-spectral siamese network is mainly used to extract spatial and spectral information and obtain spatial-spectral information. By combining the global graph attention network with channel attention and position attention mechanisms, the graph attention coefficient is utilized to better integrate the correlation between global node features into the model. Simultaneously introducing superpixel segmentation, by converting pixels to superpixels, each superpixel serves as a graph node, greatly reducing the number and computational complexity of graph nodes. The method has lower sensitivity to the number of training samples and higher classification performance under the condition of limited training samples.
Decoding the Hidden: Direct Image Classification Using Coded Aperture Imaging

Coded aperture imaging has emerged as a solution to enhance light sensitivity and enable imaging in challenging conditions. However, the computational expense of image reconstruction poses limitations in processing efficiency. To address this, we propose a direct classification method using convolutional neural networks. By leveraging raw coded measurements, our approach eliminates the need for explicit image reconstruction, reducing computational overhead. We evaluate the effectiveness of this approach compared to traditional methods on the MNIST and CIFAR10 datasets. Our results demonstrate that direct image classification using raw coded measurements achieves comparable performance to traditional methods while reducing computational overhead and enabling real-time processing. These findings highlight the potential of machine learning in enhancing the decoding process and improving the overall performance of coded aperture imaging systems.