Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning-Aided Frequency Offset Estimation Method for 5G System based on Synchronization Signal

We offer an effective frequency offset estimation approach for orthogonal frequency division multiplexing (OFDM) systems in 5G, supported by a deep neural network. The deep fusion architecture was a conventional, fully connected multilayer network divided into two stages: offline training and online inference. Initially, a time-domain cross-correlation frequency offset estimation approach based on PSS and SSS was presented, and the training sample set was created under the initial frequency offset predicted by this method. The deep fusion network is offline trained using the training sample pack to get the ideal network weights. The online inference is employed to execute calculations on the newly selected initial frequency offset group using the trained fusion network to determine the final frequency offset. The simulation results demonstrate that the performance of the suggested approach is significantly better than that of the current methods and that improved performance can be obtained at a relatively low additional cost. The deep learning-assisted frequency offset estimation algorithm also increases the frequency offset estimation range from ±1.752 kHz to ±7 kHz, offering excellent accuracy and stability in frequency offset estimation, particularly concerning having a more vital processing capability of border frequency offset.
Development of Tropical Algebraic Accelerator with Energy Efficient Time-Domain Computing for Combinatorial Optimization and Machine Learning

Tropical algebra solves complex problems with only sum and min/max operations replacing expensive multiplication and addition in linear algebra. Due to the low computing cost, tropical algebra has recently gained significant attention in a broad range of areas such as combinatorial optimization, scheduling, machine learning, etc. In this paper, we propose a generic hardware accelerator architecture for tropical algebra supporting a wide range of applications. Novel time-domain (TD) computing accelerators with special mapping, precision expansion and, unrolling techniques are proposed to further improve hardware efficiency. Test results on various tropical calculations including linear regression, dynamic programming, and neural network are shown to demonstrate an energy saving from 1.5X to 2.1X, latency saving from 2.6X to 5.2X, or an overall energy-delay-product (EDP) improvement from 3.9X-10.5X compared with conventional digital implementation manifesting the promise of the algebraic solution on low power edge devices.
Traffic sign detection based on depth improved YOLOV5

Traffic sign detection is an important part of the intelligent transportation system, which is of great significance to assisted driving, unmanned driving and intelligent driving. The sign detection algorithm needs to be lightweight. In order to solve these problems, reduce the amount of network calculations and improve the detection speed of the network without affecting the accuracy of the algorithm, this paper proposes an algorithm using the lightweight network model Ghost net as the backbone network. By embedding the CBAM attention mechanism in the network, the influence of interference information is reduced, and the feature extraction ability of the network is improved. At the same time, the weight-removed BIFPN module is introduced to strengthen the feature fusion ability of the network and improve the detection effect of small targets. Experiments show that the improved algorithm has achieved an excellent level of 80.2% in the Map_0. 5 performance index on the CCTSDB China traffic sign detection dataset, and the FPS index has increased by 37% compared with the classic algorithm.
Battery Charge State Estimation Based on Support Vector Machine Hyperparameter Optimization

Estimation of State of Charge (SOC) of the battery is an important part of battery design, and accurate power estimation is important to improve the efficiency of power equipment and the full utilization of batteries. In order to improve the accuracy and stability of Support Vector Regression (SVR) in SOC estimation, taking Improved Grey Wolf Algorithm (IGWO) to optimize SVR hyperparameters as an example, a process of finding the optimal SVR hyperparameter interval is established with the help of MATLAB automatic optimization, grid search and Occam's razor principle. Finally, after applying the IGWO-SVR model obtained from the optimized search interval to the National Aeronautics and Space Administration (NASA) Li-ion battery dataset for SOC prediction, the results show that the average error of the model after the optimized search interval is 1% on the test set, and the maximum error under different operating conditions is also minimized, which proves the good performance of the model in practical applications.
Finite-Time Bumpless Transfer Control of Switched Positive Systems

The finite time bumpless transfer control issue is handled via the co-design of controllers and a switching mechanism for switched positive systems, where each subsystem has not to require the finite time bounded property. The key objective of this article is that the control bumpless arising from switching, by restricting the magnitude of the controller, is decreased. Sufficient conditions are also established on the basis of the construction of the state-dependent switching mechanism and the feedback controllers for subsystems, which ensures the bumpless transfer performance, the positivity as well as the finite-time boundedness of the addressed system. Additionally, a simulation example is supplied to demonstrate the efficacy of the presented finite time methodology.
Catch the Intruder: Collaborative and Personalized Malware Detection By On-Device Application Fingerprinting

The vulnerability of smartphones to cyber attacks has been a serious concern to users arising from the integrity of installed applications (mobile apps). These apps are to provide legitimate and diversified on-the-go services. However, some have uncovered ways to penetrate smartphones for malicious behaviors. While some development and distribution regulations, such as Google Play Protect are in place, their effectiveness is often limited due primarily to falling behind the emergence of new malware. This paper presents an Analytic-based deep neural network Android Malware detection (ADAM) to detect potentially dangerous apps based on a set of features and patterns, i.e., application fingerprints, extracted from mobile apps. In particular, ADAM uses these features and patterns to train feature-specific DNNs to have consensus on the application labels when their ground truth is unknown. In addition, ADAM leverages the transfer learning technique to obtain its adjustability to new applications across smartphones. This is done by reusing the pre-trained model(s) and making them more adaptable by model personalization and federated learning (FL) techniques. This adjustability facilitates collaborative detection and independent labeling across smartphones, assisted by FL guards, which protect ADAM against poisoning attacks through model analysis. ADAM relies on a diverse dataset containing more than 153000 applications with over 41000 extracted features for DNNs training. ADAM’s feature-specific DNNs, on average, achieved more than 98% accuracy compared to Play Protect and antivirus software, resulting in an outstanding performance against data manipulation attacks.
Enabling Resilient Access Equality for 6G LEO Satellite Swarm Networks

Low earth orbit (LEO) mega-constellations, integrating government space systems and commercial practices, become enabling technologies for the sixth generation (6G) networks due to their excellent merits of global coverage and ubiquitous services for military and civilian use cases. However, convergent LEO-based satellite networking infrastructures lack leveraging the synergy of space and terrestrial systems. This paper extends conventional cloud platforms with serverless edge learning architectures for 6G satellite swarm ecosystems and provides a new distributed training design from a networking perspective. The proposed method dynamically orchestrates communications, computation functionalities, and resources among heterogeneous physical units to efficiently fulfill multi-agent deep reinforcement learning for service-level agreements. Innovative ecosystem enhancements, including ultra-broadband access, anti-jamming transmissions, resilient networking, and related open challenges, are investigated for end-to-end connectivity, communications, and learning performance.
SwinCNN: A Hybrid Deep Learning Architecture for Accurate Cotton Disease Prediction

Cotton crop diseases can have a substantial impact on crop productivity and quality in the agricultural industry. The accurate and timely identification of these disorders is critical for optimal disease management. In this work, we present SwinCNN which is a new hybrid Deep Learning (DL) architecture for reliable cotton disease prediction. The SwinCNN architecture combines the strengths of Convolutional Neural Networks (CNN) and the Swin Transformer (ST), a recent advancement in self-attention (SA) mechanisms for computer vision tasks. We use a publicly available dataset of cotton crop images with various diseases to train and evaluate our proposed architecture. Our results show that the SwinCNN architecture outperforms both CNN and ST models individually, achieving an overall accuracy of 99.65% on the test set. The proposed architecture can be used as a valuable tool in smart farming to detect and predict cotton crop diseases, enabling farmers to take necessary actions to mitigate crop losses.
Design and Implementation of Experimental Platform for Multi-agent System

To achieve programmability, modularity, cost-effectiveness, and practical effectiveness in multi-agent body experimental processes, we propose an experimental control platform for multi-agent body robots based on ROS (Robot Operating System) and MATLAB/Simulink in this paper. Firstly, we discuss the experimental platform used in this study, which integrates NOKOV motion capture equipment and the Gazebo simulator. Information is transferred through the ROS-Simulink information bridge, allowing users to focus on designing control algorithms and interacting with experimental information during use. Furthermore, we provide various modeling and simulation examples under the designed experimental platform, including different types of multi-agent bodies such as UAV and UGV, different UGV models including the Turtle model in ROS and Mecanum wheel simulation model, and various trajectory tracking algorithms such as Pure Pursuit and self-designed tracking algorithms. Through simulating and controlling experimental examples, we have discovered that correct modeling of the multi-agent body under the experimental platform allows the model code built on the Simulink platform to be directly transferred to the ROS platform for implementation or cross-platform control. This significantly enhances the efficiency of developers in solving practical engineering problems and generates high utilization value and positive economic benefits.
Dynamic Graph Dismantling with Reinforcement Supervised Learning

There are always some “key” nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the “key” nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, $\epsilon$-greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization.
TransDUNet:Image segmentation of artificial joint based on TransUNet

Traditional convolution has limited ability to capture long-range dependencies of images, which is crucial for semantic segmentation in medical images, where spatial relationships between different anatomical structures are compound and non-local. The long-range dependencies in medical images can be captured by multi-head self-attention mechanisms, which is essential for complex and non-local spatial relationships between different anatomical structures. The receptive field of standard up-sampling convolutions is limited, which can affect the accuracy of segmentation predictions. Dilated convolutions can expand the receptive field and capture multi-scale contextual information. This paper proposes a new deep learning architecture called TransDUNet for semantic segmentation of artificial joint implants in medical images. Based on TransUNet, We replace the traditional convolution in the encoder up-sampling module of TransUNet with the module consisting of multiple dilated convolutions with different dilation rates to Incorporating multi-scale information, Dense skip connections are utilized to enhance feature reuse and gradient flow, thereby improving the performance and generalization ability of the model. The proposed algorithm’s segmentation performance outperforms recently developed networks, according to experiments, which demonstrate that its mDSC and mIoU are 94.1% and 88.68%, respectively.
Control path recovery after controller failure in SDN

Software-defined networking (SDN) is a new network paradigm that has promoted the development of the Internet and network multimedia. Currently, software-defined networks mostly use multi-controller technology due to load balancing and fault tolerance requirements. However, the fault tolerance and recovery of the software-defined network for the in-band control plane is a big challenge. This article describes the restoration strategy of the control path when the controller fails in the in-band network control plane, elicits the corresponding mathematical model, proposes the corresponding path restoration algorithm, and finally shows the corresponding experiments.
Aging Mitigation in FPGAs Considering Delay, Power, and Temperature

Field programmable gate array (FPGA) devices are highly susceptible to transistor aging, mainly through the bias temperature instability (BTI) phenomenon. BTI can be modeled as threshold voltage increase in MOSFET transistors, which leads to the degradation in device performance. As technology scales, leakage power has turned into a major portion of FPGA total power consumption. In this paper, we study the mutual effects of BTI and leakage power by considering the temperature changes in the basic components of FPGAs. Our analysis shows that while the leakage-power reduction caused by BTI may be considered desirable, a bit-flipping scheme should still be employed to mitigate device degradation. We present an optimization problem to optimize the device performance over the device's lifetime. A postrouting aging-aware timing analysis method is also proposed to find the best flipping frequency. The simulation results show that bit flipping at a proper frequency may reduce the power consumption of device by about 5% while keeping the critical path delay below a given constraint.
An Arrhythmia Classification Method Combining ECG Dynamic Feature and CNN

Cardiovascular disease has become the number one killer disease threatening human health and the number of cases is increasing year by year. An electrocardiogram (ECG) signal is an important physiological signal that reflects the heart activity. In this paper, we apply deep learning techniques with dynamic feature and data resampling techniques to ECG classification. The ECG dynamic feature is extracted using deterministic learning and used as unique features for ECG classification. A one-dimensional convolutional neural network (1D CNN) is designed to classify heartbeats with dynamics as input features. Data resampling is used to balance the training dataset. The results of the experiments achieved the highest classification accuracy of 99.22% based on the MIT-BIH arrythmia database. The proposed method uses dynamic feature and a lightweight 1D CNN model to obtain a better cost-benefit ratio compared to other methods.
Comparison of Machine Learning Optimization Techniques for EEG-Based Confusion Emotion Recognition

Machine Learning (ML) and Optimization techniques are considered two distinct fields of study in Computer Science. However, with the rapid development of Artificial Intelligence, these two fields are now frequently interacting with each other to enhance the performance of learning and searching/classifying capabilities of the algorithms. In this study, an optimization performance comparison of the current, widely used hyperparameter tuning algorithms on classifier models of confusion emotion using EEG signals. Two Evolutionary algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were compared with two Non-Evolutionary Algorithms: Random Search and Bayesian Optimization. The four algorithms were made to optimize a Support Vector Machine (SVM) which was trained to be a confusion emotion classifier using Wang et al's [19] dataset. Performance was assessed by calculating the prediction accuracy and error produced by the SVM that was trained using the yielded parameters by each hyperparameter tuning algorithm. Results showed that Evolutionary hyperparameter tuning algorithms were more effective than Non-Evolutionary algorithms because they yielded optimized parameters that produced the highest prediction accuracy and the lowest prediction error.
Traffic light detection based on improved YOLOV5

Traffic light detection is a part of modern intelligent driving, which affects the driving safety of smart cars. However, the object of traffic lights is small and the actual road conditions are complex. The classic object detection algorithm cannot achieve a good detection effect on small signal lights. For this situation, This paper proposes an improved yolov5 model. Because the object of the traffic light is small, the feature extracted by the P2 layer with a small receptive field is introduced. Inspired by the MOAT model, a C3_MOAT structure is proposed. At the same time, a structural re-parameterized RepConv module is added to the Head part of YOLOV5 to improve the feature extraction of the model. ability. Through the above improvements, the ability of the model to detect signal light objects is improved. Experiments show that the improved algorithm improves the map@. 5 and map@. 5:. 95 indicators in the Bosch dataset by 2.6% and 2.7% respectively, and in the S2TLD dataset map@. 5 and map@. 5:. 95 Indicators were increased by 2.2% and 1.6%. Compared with the YOLOV5 network, it has a good improvement.
Weathering and Strontium Contamination of Meteorites Recovered in the Sultanate of Oman

letin 84 in 2000 (Grossman, 2000) where 39 meteorites from Oman were described. More than 30 years ago the Institute of Geology at the University of Bern established partner-ship with the Sultanate of Oman. Due to this long-term collaboration with Oman, two of us (BAH and EG) initiated the Omani-Swiss meteorite search and research project with the help of Tjerk Peters (1936-2009), former professor of mineralogy, University of Bern. Ali Al-Kathiri was the first Omani PhD-student at the University of Bern and the Natural History Museum of Bern. Since 2001 ten field cam-paigns were conducted which resulted in approximately 5500 meteorite finds belonging to ~690 meteorite falls (Hofmann et al., 2011). A careful planning of the fieldwork is fundamental. Suit-able surfaces for meteorite recovery are selected using satel-lite images available from Google Earth and other sources. Based on our experience from former campaigns, we have learned to interpret the satellite images to plan our routes along the most suitable surfaces. During the campaigns 2009 and 2010 we mainly followed routes from the coast towards the interior of the country. The idea behind this was to find meteorites at various distances from the sea to study its influence on the weathering. Search for meteorites is performed visually by car or by foot. For security reasons we search with at least two cars. To obtain quantitative informa-tion for the meteorite find density we systematically search by foot numerous quadrangles of one quarter of a square kilometer each. Such a foot search takes about ~2 hours when 4-6 persons are involved and it is conducted in the morning hours. We perform our field campaigns in winter (between January and March) in view of the fact that the temperatures are relatively pleasant (typical daytime tempera-tures 25-30°C). When a meteorite is found the finders give signs to the other searchers and all members of the search group meet at the find location. Usually, searches are performed in sight distance but often one is very concentrated and the eyes are fixed to the ground with the consequence that one looses the beckons from the other team when they have found a meteorite. Our procedure of meteorite collection is standardized according to the following routine: record the coordinates by GPS (Fig. 1a), take photographs with a label containing the field number and a scale bar (Fig 1b), esti-
Landslide inventory and susceptibility and hazard zoning

Landslides are considered as a geological disaster that has an unfavourable effect on lives and properties, generating both direct and indirect economic and human losses every year. Compared to other geological disasters, landslides are considerably smaller in scale, more dispersed, but more disastrous in many cases. The presented methodology is based on a case–event system, which uses spatial analysis functions and artificial intelligence techniques, to evaluate potential instability problems concerning natural or artificial slopes. The methodology allows the user to examine new cases or areas of interest and compares them to previously recorded cases of instability problems that occur in the research area. The effectiveness of the methodology is evaluated in Kimi, Euboea, Greece, an area experienced substantial landslide events, where a well documented database of previous studies existed.
Deep Learning for Analysis of Two-Dimensional Materials in High-Resolution Transmission Electron Microscopy Image

Two-dimensional (2D) materials possess exceptional electrical, mechanical, thermal, and optical properties, making them widely applicable in fields of electronics, energy, optoelectronics, and medicine. The geometry of 2D materials at the nanoscale, such as the number of layers, interlayer spacing, and thickness of the layers are of particular interest, as they have great influence to the properties of the material. Transmission electron microscopy (TEM) with atomic resolution is an ideal research method for 2D materials. However, analyzing the properties of 2D materials by interpreting the high-resolution TEM images not only require rich expertise but also is a time-consuming and labor-intensive task. In this work, we propose a neural network based on the U-Net architecture for high efficiency TEM image analysis of the geometry of 2D materials. The effectiveness of this method is verified on MoS2 image datasets and obtained dice coefficients of 0.92. The physical parameters such as layer number, thickness, and interlayer spacing information of 2D materials can be analyzed subsequently. Our results provide an artificial intelligent approach to analyze 2D materials.