Reviewerly/exHarmony huggingface dataset:
AT-CycleGAN: Historical Tibetan Document Characters Generation Using Attention and Triplet Loss's CycleGAN

In the study of historical Tibetan document image analysis and recognition, the complete set of characters is the basis of recognition. However, character segmentation presents certain challenges due to stroke connections between characters in the same line and across different lines, making it difficult to obtain an authentic and complete set of characters from historical Tibetan documents. To address this issue, we propose AT-CycleGAN, a method for generating characters from historical Tibetan documents that achieve zero-shot character image generation. This method leverages style transformation between Tibetan print fonts(source fonts) and historical Tibetan document fonts (target fonts) while incorporating an attention module (AM) to fuse high-level and low-level features, thereby enhancing the accuracy and style consistency of the generated images. Additionally, triplet loss is employed to further improve the generation quality of similar characters. Finally, we construct a complete category of character sets from historical Tibetan documents, providing valuable data and technical support for subsequent application-oriented research.
Research on Path Recognition Algorithm for Autonomous Driving Model Vehicles

The path recognition algorithm in autonomous driving technology is crucial in controlling the navigation and tracking of vehicles. In this article, a simulated road is constructed in the laboratory using a model car. Firstly, images are captured using a camera and a TC264DA chip is used to process and recognize the path of the acquired images. The TC264DA has a high main frequency of up to 200MHz and powerful performance, making it capable of completing data processing and vehicle control processes. Secondly, the images are preprocessed and the optimized OTSU algorithm is used to calculate the maximum inter-class variance of the grayscale in the entire image, and the image is binarized. Then, pixel filtering is applied to remove black noise in the lane. Finally, after the filtering process is completed, the lane boundaries are extracted and fitted to obtain the mid-line of the road, ensuring that the vehicle travels along the mid-line and is able to automatically recognize relevant paths, enabling the vehicle to select appropriate routes in complex road conditions.
Channel Robust RF Fingerprint Identification Method with Narrow Band Differential Constellation Trace Figure

RF fingerprint (RFF) identification in wideband wireless communication systems is vulnerable to interference from wireless channel variations. This paper proposes a channel robust RFF identification method with narrow band differential constellation trace figure (NB-DCTF). A particular narrowband filter bank is designed to extract narrow-band RFF features from 802.11 preamble signal. The RFF features of 12 subcarriers in the short training symbol are respectively extracted and drawn into the NB-DCTF. A convolutional neural network (CNN) is then designed to identify different devices using NB-DCTF features. In the experiment, we construct a testbed including 11 mobile phones and one universal software radio peripheral (USRP) platform as the receiver. The classification accuracy in static state can reach 98.9%, and that in mobile state can reach 92.6%.
Multimodal U-Net Breast Cancer Tumor Algorithm Based on Radio Frequency (RF) Data and Ultrasound Images

Breast cancer is a common cancer among the women worldwide,early screening is crucial for timely treatment and improved therapeutic effect. The unimodal algorithm has difficulty in accurately segmenting tumour regions when processing ultrasound images with poor imaging quality. Therefore, a multimodal M-UNet breast tumor segmentation algorithm based on radio frequency (RF) data and ultrasound images is proposed. Breast tumor ultrasound images and RF data are used as multimodal inputs, and the segmentation of breast tumor ultrasound images is performed by combining Multi-Head Attention in Transformer with U-Net network. The proposed algorithm is tested on the Open Access Series of Breast Ultrasonic Data (OASBUD), a publicly available tumor dataset from the Ultrasound Department of the Institute of Basic Technologies of the Polish Academy of Sciences, and yields a Dice coefficient of 66.0% and a cross-merge ratio coefficient (IoU) of 52.7%. Compared with the unimodal U-Net method, the proposed algorithm improves the Dice coefficient and IoU by 8% and 3% respectively. Therefore, this method is expected to play an important auxiliary role in the early screening of breast cancer.
Image Denoising of Fishing Nets Underwater with Range Selection Based on Convolutional Sparse Coding

The underwater detection imaging technology mainly includes sonar imaging and underwater optical imaging. In order to overcome the disadvantages of low resolution of sonar imaging and short detection range of traditional optical imaging, optical gating technology is applied to underwater fishing net detection. This technology has higher resolution compared to sonar imaging, and its detection range has also been shown to be three times larger than that of conventional optical imaging. However, underwater range gating imaging can suffer from large random noise and low signal-to-noise ratio. In order to solve this problem, this paper proposes a convolutional sparse coding denoising neural network based on deep learning to improve image quality. This network consists of a local residual channel and a global convolutional sparse coding channel. Compared with DnCNN, ECNDNet and other methods, the average Peak signal-to-noise ratio of the proposed method is improved by 1.57~2.11dB, which can effectively remove random noise and reconstruct clear underwater fishing net range-gated images.
Estimating the Reliability of a DApps-Based Files Storage System

One of the key features of blockchain is high reliability, due to its high redundancy nature. While creating many replications across nodes serves well for storing relatively small data entries, this cannot be used with big files, as it might be impossible to replicate many huge files across many nodes. Here we present a DApps-based system that can lead to improving the total reliability of distributed files storage systems by managing the internal processes using DApps instead of a classical centralized load balancer. For the same reason, known reliability calculation methods for distributed file storage systems are not suitable for assessing the reliability of DApps-based systems. They do not take into account the automatic recovery time of files. In this article, we will deduce the reliability method for DApps-based system, then compare the resulting reliability with other existing systems, and discuss the benefits gained in total.
Real-Time Cat Detection System using MobileNet-SSD V2

The development of a cat-repellent system needs a deep learning-based object detection method to detect cats, as the current system only uses PIR sensors and cannot identify specific kinds of objects. The system detects the presence of cats using the MobileNet-SSD v2 model, a combination of MobileNet v2 and Single Shot Detector (SSD). The model’s performance is tested using the parameters of precision, recall, and loss. The Node-Red platform and the TensorFlow.js library are used to implement models on Raspberry Pi 4. Based on the results and analysis, the MobileNet-SSD v2 model creation was successful, with an accuracy value of 0.91 and a recall value of 0.89. The resulting F1-score metric of 0.92 shows a balance between precision and recall. The test results indicate that the accuracy of the model created by the transfer learning method using the pre-trained MobileNet-SSD v2 model is 82%. However, environmental factors such as distance, brightness, and camera resolution can also impact the model’s efficacy. This model can be integrated with the ultrasonic and water sprayer sub-system to enhance cat repellents’ efficacy. Future research can compare the object detection model with other models to increase detection speed and accuracy.
Employing Deep Learning Framework to Support Location Management for the Audio-Visual Industry

This paper presents a specialized tool for film producers and managers to extract useful information from images of different locations. The resulting framework can be used for image retrieval and other applications related to film production and location scouting. The framework uses deep learning models to extract labels, colors, seasons, emotions, and building architecture from input images. The methodology is described in detail, outlining the different steps involved in image processing and feature extraction. The label extraction module uses a transfer learning approach with pre-trained CNN models fine-tuned on a custom dataset of 106 classes. The color extraction module uses the LAB color space, a device-independent and perceptually uniform color space, in image processing applications, to accurately identify the dominant color, while the season extraction module extracts seasonal information from input images. The emotion extraction module identifies the emotion portrayed in an image, and the architectural style module automatically identifies the style of buildings in photos. Finally, the embedding module combines various vectors representing different image aspects to create a general embedding for each image.
Small Target Detection Algorithm for UAV Based on Improved YOLOv5

The small and dense objects in unmanned aerial vehicle (UAV) images deteriorate the detection accuracy of neural networks. This article proposed an improved YOLOv5-based algorithm for small and dense object detection in UAV images. To enhance the capability of acquiring the feature information and the receptive field of the network in the backbone feature extraction network, we proposed an enhanced feature extraction (EFE) module, while incorporating the advantages of different pooling methods, and introduced the receptive field block (RFB) module, which realized fusion of different features. Meanwhile, we improved the multi-scale detection module, while enhancing the detection capability of the network for small objects in UAV images. Experiments were done on the VisDrone-DET2019 dataset. The improved algorithm achieved 39.4% mean average precision (mAP), which was 5.5% better than the benchmark network. The experimental results showed that the YOLOv5 algorithm proposed in this article was feasible and effective.
Research on Adaptive Learning Model Based on Multidimensional Feature Fusion

In recent years, with the popularization of intelligent devices, the application of computer technology in the field of education has also shown a rapid development. This paper takes the construction of Adaptive learning support model as the starting point, designs the framework of Adaptive learning based on multidimensional feature fusion of Adaboost algorithm, explores the construction, fusion and application of learner model, domain knowledge model and adaptive engine, and then implements the Adaptive learning system and conducts application research on it. By constructing an adaptive model, it is verified that the model has higher accuracy and robustness, and can effectively adapt to different students and teaching environments.
ANFIS-SMC for Trajectory Tracking and Obstacle Avoidance Control of Quadcopter

In order to assess the effectiveness of the suggested ANFIS-SMC controller in trajectory tracking and obstacle avoidance control of a quadcopter, a comparative simulation analysis is presented in this research. ANFIS controller enables optimal output depending on a set of rules by establishing a variable gain rather than a fixed gain for the SMC controller. A Neuro-Fuzzy adaptive inference system is used to update the position control gain, enabling the sliding mode control to adjust to changing environmental conditions. To evaluate the effectiveness of the suggested technique, ANFIS-based SMC and SMC controllers (ANFIS-SMC) were compared and contrasted. ANFIS-SMC controller and SMC controller comparisons were made in four simulations. In the simulations, a quadcopter was moving along a square trajectory, both in the absence of obstacles and with varying distances between them. Its outcomes. The findings showed that the ANFIS-SMC controller performs better than the SMC controller in minimizing error. The mean absolute error generated by ANFIS-SMC is 0.064 m. Additionally, ANFIS-SMC outperforms the SMC controller by leading to safer obstacle avoidance, with the probability of collision being 0% while SMC is 33.33%. However, the ANFIS-SMC controller requires a longer execution time. The average execution time required by SMC is 45.2% faster than ANFIS-SMC.
A Novel 3D Image Processing Method Based on Spectral Layout

3D image processing is an important problem. Compared with 2D image processing, its difficulty is much higher due to the extra dimension. To fundamentally address this problem, we propose a novel method to embed the essential information in 3D image into 2D space via spectral layout. Specifically, we construct 3D adjacency graph to capture spatial structure of 3D voxel grid. Then we calculate the eigenvectors corresponding to the second and third smallest eigenvalues of its graph Laplacian and perform spectral layout to map voxels into pixels in 2D Cartesian coordinate plane. By using spectral method to capture the most critical structural information, the proposed method can achieve high quality 2D representations for 3D image, which enables to use 2D-based methods to process 3D images. The experimental results of visualization, clustering and classification demonstrate the effectiveness of our method.
Development of a Color Tone-Based System for Region of Origin Classification Design

Batik cloth, an integral part of Indonesian culture, has garnered recognition worldwide. A distinctive feature that sets batik cloth apart from regular fabrics is its array of unique colors. Each region boasts its own characteristic color palette for batik cloth, which can be conceptualized using a color wheel. Currently, the color mapping process is performed manually and subjectively, leading to frequent errors and considerable time consumption for mapping a single batik cloth pattern. To address these challenges, there is a pressing need for a program with the capability to automatically correct and map colors from input batik cloth images onto a color wheel. This program should also provide users with RGB coordinate information for the mapped colors, thus streamlining the color mapping process while reducing errors.Color mapping is crucial for understanding the color wheel representation of batik cloth and classifying its origin based on dominant color patterns. Rigorous testing will be conducted to evaluate the program's effectiveness by extracting grayscale and RGB values from batik fabric images and using a neural network algorithm for accurate classification. This program streamlines the color mapping and classification process, expediting and enhancing accuracy in batik cloth analysis. The neural networks algorithm achieved a model accuracy of 1.0 with a dominant classification rate of 100% on a dataset of 10 batik cloth images. This success demonstrates the program's capability to efficiently classify batik cloth based on color characteristics, making it a valuable tool for automating the color mapping and classification process in batik cloth analysis.
Drone Swarm Movement without Collisions with Fixed Obstacles Using a Hybrid Algorithm Based on Potential Functions

Facilitating obstacle avoidance for autonomous drones (UAVs) is a key problem of their safe movement, including indoor applications. The complexity of this problem increases when a large group or a swarm of drones implementing coordinated movement is concerned. In this scenario, there can be problems with the standard methods, such as the potential field method. The jamming effect that occurs when a drone cannot efficiently bypass an obstacle on its way to the destination is the most widespread problem. This paper suggests an approach that uses a hybrid algorithm combining the standard potential field and the curl-free vector field. For the sake of brevity, the authors only review the fixed obstacle scenario. The usage of the curl-free vector field component can help remove the jamming effect during swarm movement. The theoretical analysis of stability is performed using Lyapunov’s direct method. To test the effectiveness of the approach, the authors used modeling in MATLAB/Simulink.
Ensuring the Big Data Traceability in Heterogeneous Data Systems

The work corresponds to the security of Big Data, the key element of the modern digital world. The distributed ledger technology is widely applied to achieve data integrity and confidentiality. However, all relevant solutions are grounded in specific application environments and platforms, what determines the goal to create a universal approach in which Big Data processing components can vary and interchange. The paper proposes a method for ensuring the traceability of Big Data processing in heterogeneous data systems. Traceability is the ability to ensure that data is completely traceable across the entire system. This allows data to be easily followed all the way back to the original sources, audited and controlled. Big Data processing models have been presented. HashGraph and Blockchain distributed ledger mechanisms have been adapted to monitor Big Data processing chains. For tracing Big Data, the appropriate components of the distributed ledger have been modified and implemented. The experiments have been arranged on the test data system processing the big network traffic input, and it has been confirmed that the proposed solution is universal for the ledger technology, does not depend on the Big Data processing platform, and provides Big Data security preserving the computing resources of the protected system.
Adaptive Control Process Automation of Acoustic-Magnetic Device Operating Parameters on the Active Experiment Basis in CoDeSys Environment

This paper proposes an adaptive control model for an active experiment of reagent-free treatment of geothermal water by an acoustic-magnetic device (AMD) in the real time. The CoDeSys environment was used to control the active physical experiment. This paper considers the use of the CoDeSys environment for the simulation program development of geothermal water reagent-free treatment. Simulation process, software code and algorithm for adaptive control of the system are presented. The process flow diagram for this process is visualized with a graphical representation of the equipment and sub-processes. The program was developed with CoDeSys for the reagent-free geothermal water treatment process and can be adjusted directly during operation to reflect process changes. An adaptive control system with tabular tuning was used to adjust the PI-controller coefficients. The results of the simulation and experiment on the plant are presented in the paper. The proposed methodology of the experimental plant can be applied to other technological processes, which will reduce the cost of performing experiments.
Recognizing Train Brake Shoes Based on the Improved OCRNet

The brake shoe is a key component used for braking in train operation; once the brake shoe is severely worn or falls off, it may cause a major traffic accident. Therefore, the accurate inspection of locomotive brake shoes is of great importance. An improved semantic segmentation algorithm based on an object contextual representations network (OCRNet) is proposed to recognize brake shoes. Firstly, for the problem of brake shoe contour detail segmentation, a coordinate attention mechanism module (CA Block) is introduced into the backbone network of OCRNet to capture the internal correlation of position information and channel information, which can overcome not only the information loss caused by the downsampling process but also improve the semantic segmentation of brake shoe accuracy. In addition, semantic segmentation only focuses on pixel-level classification but lacks the optimization of object-level classification. So, we use a mixed loss function composed of cross-entropy loss and Lovász-softmax loss to supervise the training process at the pixel and object levels, respectively. Finally, the classification network is introduced to improve the detection accuracy and reduce the false positive rate. The proposed method is verified on the test set, and the experimental results show that the improved improved OCRNet can achieve higher accuracy with a slight increase in parameters and calculation amount.
Image Caption Method with Verb-Specific Scene Graph Decomposition

Inaccurate linguistic descriptions are generated when target features collected from images with unclear semantics and complex visual relationships are utilized to generate descriptions from the images. In order to obtain deep visual comprehension and generate high-quality descriptions, an image caption method based on verb-specific scene graph decomposition is proposed in this study. The method described in this paper extracts grid features from images to build a verb-specific scene graph that integrates richer information than conventional ones. The scene graph is then divided into sub-graphs according to the relationships between entity objects and verbs to address the event compatibility of detected target. Finally, a language decoder is employed to generate the various text descriptions for the images. The results of the experiment on the public dataset MSCOCO demonstrate that the proposed image caption method can capture the key semantics of the image content more effectively, with a CIDEr of 133.4%, achieving higher accuracy and improving the quality of the image description text.
An Integrated Stochastic Configuration Network Based on Fuzzy Transformation

In this paper, fuzzy transformation is considered to modify stochastic configuration network for data regression. Firstly, the mathematical representation for stochastic configuration network based on fuzzy transformation, named FTSCN, is deduced. Then, the parameter learning algorithm for FTSCN is obtained. Further, the ensemble scheme is applied to improve the performance of FTSCN. Some simulation experiments in the benchmark datasets show the effectiveness of the designed model.
Multi-robot Cooperative Navigation Method based on Multi-agent Reinforcement Learning in Sparse Reward Tasks

Multi-robot systems can collaborate to accomplish more complex tasks than a single robot. Cooperative navigation is the basis for multi-robot to complete rescue, reconnaissance, and other tasks in high-risk areas instead of human beings. Multi-agent reinforcement learning (MARL) is the most effective method to control multi-robot cooperation, but the sparsity of rewards limits its application in real scenarios. In this paper, a curiosity-inspired MARL approach which is called CIMADDPG is proposed to promote robot exploration. The global curiosity allocation mechanism is designed to determine each agent’s contribution to the global reward. In addition, to ensure that the collaboration of agents is not lost during exploration, the dual critic network is designed to guide the update of the policy network jointly. Finally, the performance of the proposed method is verified in a multi-agent particle environment (MPE) and multi-robot (Turtlebot3) cooperative navigation simulation environment. The experimental results show that CIMADDPG improves the performance of SOTA by 23.53% ~ 48.84% and achieves a high success rate in multi-robot cooperative navigation.