Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study on Grid-Based and Non-grid-based Path Planning Algorithm

Recent years have seen a dramatic uptick in research efforts dedicated to the development of mobile robots. One of the most common research topics in this area involves the path planning of mobile robots. The existing algorithms use the samples to construct a network or a route. There are many methods available for creating samples on the map as well. However, planners need to explore a bigger search space while building a path for the mobile robot because the samples are dispersed around the map. In this study, we examine a gridbased sampling strategy that narrows the search while still allowing us to probe potential avenues of exploration. For this objective, we implement the three most well-known path planning algorithms namely, $\mathbf{A}^{*}$, Probabilistic Roadmap (PRM), and Rapidly-Exploring Random Tree (RRT). The algorithms are compared using a grid-based path planner and a non-grid-based planner. The observed findings show that the proposed sampling technique is more effective than the previous one.
Design of a Compact and Efficient 2.45GHz Rectenna for RFID Sensorised tag

The development of a small, effective rectenna is crucial for powering the growing number of low power devices. According to our research, the RFID sensorized tage must be powered by a small, highly effective receiver with 2.45 GHz energy harvesting to eliminate the need for a battery. It is suggested that a small monopole antenna be used to gather WiFi energy and to enable downsizing. Moreover, a radial stub matching network is made to achieve high conversion efficiency with an input power range that is rather large, from 5 dBm to 20 dBm. Both simulation and experimentation have been done. The findings demonstrate that the suggested antenna has good WiFi band realised gain and reflection coefficient characteristics. At 2.45 GHz with an input power of 12 dBm, the rectenna's greatest RF to DC conversion efficiency is 62%. This study shows that the rectenna that has been suggested can be used to power the sensorized tag.
Multichannel Dual Codes: The Choice of Channels Carries Information

When there are multiple channels between a pair of source and destination, the way to use the channels carries some extra information. In this paper, we study a coding scheme that encodes the message into the choices of channels. Unlike the traditional sense that the message bits are ordered in a temporal manner, the ordering information is lost after the transformation. To resolve this issue, the data to be sent via the channels is the ordering of the message blocks. We study the properties on the number of channel-uses and the limitation of the code. We also investigate the optimal message length that maximizes the efficiency of channel-uses.
Target Recognition System of Football Remote Sensing Image Based on Heapsort Algorithm

Since its birth, football has become a world-class sport with countless fans. With the continuous development of football, fans are increasingly looking forward to seeing more exciting football games. The article is based on the Heapsort algorithm-based football remote sensing image target recognition system. On the basis of the literature, the Heapsort algorithm and the relevant theories of the football remote sensing image target recognition are understood, and then the football remote sensing image target recognition based on the Heapsort algorithm the system is designed, and the accuracy of the detection system is improved by introducing the Heapsort algorithm into the target detection process, and then the designed system is detected. The detection result shows that the tracking accuracy of the system in this paper is about 90%.
SLSNet: Local Context Attention Transformer for Automatic Polyp Segmentation

Cancer is a major global health issue, and colorectal cancer is the second leading cause of cancer death. It can develop from high-risk polyps, so accurate segmentation of polyps is essential for the diagnosis and treatment of this disease. However, in clinical practice, the examination of polyps remains challenging because their texture and color are similar to the surrounding tissues with low contrast, causing a large number of missed cases. In this paper, we proposed an automatic polyp segmentation method termed as SLSNet, in which Swin-Unet was treated as the backbone network and further improved by embedding the local context attention (LCA) module, channel transformation (CT) module and Squeeze-and-Excitation (SE) block. Specifically, in order to fully utilize the local context information of the polyp in the segmentation network and incorporate hard sample mining, the LCA module was introduced into the encoder to enhance the attention for uncertain and complex regions. At the same time, the CT module was utilized to extract more features from the decoder and send it to the LCA module to enhance the fine segmentation ability of the model. The SE block was introduced into the decoder to adaptively recalibrate channel-wise feature responses. To assess the performance of the presented SLSNet, experiments were conducted on Kvasir-SEG and CVC-ClinicDB public data sets. Extensive experimental results show that the presented SLSNet could better extract polyp features, and achieved superior performance for the automatic polyp segmentation compared with the existing methods.
Evaluation of Ecological Protection Importance in Yangtze River Delta Based on Geospatial Big Data

At this stage, China is advocating the concept of ecological space protection, and optimizing the management and control of land development and utilization has become a basic consensus in space planning work. Firstly, this paper analyzes the key ecological issues on the basis of clarifying the ecological background resources in the Yangtze River Delta region. Subsequently, an evaluation index system for the importance of ecological protection was constructed. Finally, ecological service functions and ecological sensitivity analysis results were obtained, and ecological areas with important conservation significance were identified. The research results show that the total area of the most important ecological protection areas in the Yangtze River Delta is 39170.30 km,² accounting for 18.17%, which are mainly distributed in the southeastern and central and western parts of the Yangtze River Delta. The total area of important ecological protection areas is 94,606.54 km², accounting for 43.88%, and they are mainly distributed in the southern part of the Yangtze River Delta. The research results can provide a scientific basis for optimizing the ecological protection red line in the Yangtze River Delta region, carrying out zoning and classification of ecological protection, and constructing a green ecological environmental protection barrier.
TransAPR: Absolute Camera Pose Regression With Spatial and Temporal Attention

Visual relocalization aims to estimate the absolute camera pose from an image or sequential images. Recent works tackle this problem by exploiting deep neural networks to regress camera poses. However, spatial and temporal clues from sequential images still remain underexplored, resulting in inaccurate poses and large outliers. In this letter, we introduce a novel vision Transformer based absolute pose regression model, TransAPR, to tackle this problem. Upon the traditional CNN backbone, we design Transformer based spatial and temporal fusion modules respectively to realize sufficient feature interaction among the neighboring images in the sequence. A hierarchical feature aggregation (HFA) module is further designed to aggregate multi-scale and multi-level features in the pose regressor. Benefiting from these delicate designs, our model is able to generate reliable image representations for absolute pose regression, resulting in more robust localization under challenging environments. We conduct extensive experiments on various indoor and outdoor datasets and show that our method achieves state-of-the-art performance.
Software Reliability Modeling and Prediction of Residual Defects for Embedded Systems

one of the possible approaches to assessing the reliability of real-time embedded systems software is substantiated; the features of the system functioning, criteria and metrics are specified. There are a lot of frameworks based on software reliability growth models (SRGMs) and the Non-Homogeneous Poisson Process (NHPP) for measuring the reliability of software products and predicting residual defects. Numerous SRGM extensions take into account bug correction, debugging workflow, software metrics, programmer qualifications, etc. However, few works are devoted to approaches to evaluation taking into account the features of real-time software. The applicability, goodness of fit, accuracy of the models, and prediction of residual defects are analyzed and compared depending on the initial/input data and the type of real-time tasks to be solved. The novel framework is proposed; the most suitable reliability models included in the framework are briefly considered, in accordance with the different behavior of the risk function over time, and assumptions. The developed framework allows you to choose an appropriate model or set of models to predict the remaining defects in the software. The case study draws on a data set of the results of debugging and functioning of the system for monitoring the state of electrical equipment. The software of such a system implements tasks of mixed criticality with a certain set of periodic and sporadic tasks in real-time.
An Approximation Computation Approach to Big Data Analysis with a Case Analysis of PV System

In the era of big data, it is indispensable to apply data science and technology for big data analysis to solve the big data problems. With the advancement of the big data technologies, we are also facing many problems when dealing with the big data and their studies. It is obvious that big data become "bigger" and "bigger", more complex than before, with a good number of attributes and features in various formats and styles. On the other hand, many data analysis techniques have been proposed for various application domain problems in different purposes. This worsens the situation of choosing an appropriate method for a right problem of right data. In this paper, the author intends to propose an approximation approach toward this problem, through discussing the ways of identification of patterns of the original data, be they of data features or analysis methods. The author attempts to apply the idea to a case of fault detection of a household photovoltaic system.
Stock market prediction by combining CNNs trained on multiple time frames

This paper explores a different method used for market analysis in the Forex stock market. Various econometric models, moving averages, technical indicators, and machine learning techniques have been investigated for predicting stock market trends. This study focuses on designing a new model called the multi-CNN model, which incorporates domain knowledge of Forex. The model is evaluated using EURUSD data from January 2015 to December 2020. The data is preprocessed, normalized, and divided into training, validation, and testing sets. The performance of the proposed model is compared with benchmark models such as Single-LSTM, Single-GRU, and Single-CNN. The results indicate the promising performance of the multi-CNN model in stock market forecasting. The paper provides insights into applying deep learning approaches for predicting stock market trends, highlighting the advantages of combining CNNs and utilizing multiple time frames over simple models such as simple CNN, LSTM, and other recurrent neural network-based models.
Machine learning-Based sleep stage prediction using EEG signals recorded in PSG

Sleep problems are very common nowadays. Many conventional methods are there for analysing this types of problems. But these methods are often time consuming, expensive and also human interventions are needed. So the need automatic diagnostic tool is very much important. Different artificial intelligence technologies like deep learning ensure the full utilization of data with very less information loss. In this paper a diagnostic tool is proposed by using the methods in machine learning. Signals were pre-processed in the first module, and the feature extraction is done by power spectral density technique (PSD). In the final section, features that had been extracted were put into an ensemble classifier, also known as a rotational support vector machine (RotSVM). The accuracy & sensitivity for the sleep stages classification is also calculated. According to classification performance results, 1D channel EEG can be used to create a sleep monitoring system that is useful for the hospitals and home care monitoring systems.
Nonlinear Control Methods for Complex Plants under Uncertain Conditions

The paper deals with the problems of control system synthesis for nonlinear underdetermined plant, reduced to a lower triangular form and parameterized with respect to the vector of unknown parameters. Nonlinear adaptive control systems are developed and studied, synthesized on the basis of adaptive integrator bypass methods with redundant parametrization and using tuning functions. At the same time, in each of the methods, the expressions of derivatives of virtual controls calculated at each step, are replaced by their filtered counterparts. A comparative study of the developed adaptive systems based on a mathematical model of a generalized parametrized third-order nonlinear plant is carried out using the Matlab Simulink program packages.
High-Precision Detection and Classification of Covid-19 Cases Based on Optimized ResNet

This paper attempts to use convolutional neural networks to classify images of patients with COVID-19. The classification model is constructed based on enormous actual X-ray images of patients with COVID-19 patients and normal people. In this paper, the structure is optimized based on the Residual Neural Network model, and the accuracy is improved from 0.91 to 0.99. In addition, NasNet, DenseNet and Vgg-16, were compared horizontally in terms of accuracy, precision, recall and F1 score. ResNet reached 99.9% in the training set of accuracy, and 96.7% in the test set of accuracy, which performed well.
Nonlinear Model Predictive Control for Repetitive Area Reconnaissance with a Multirotor Drone

This paper considers the problem of a reconnaissance mission in which a single multirotor drone must survey a given map by repetitively visiting different checkpoints. Several points of interest (POIs) are used to discretise the map, and each of them is associated with a time-varying heat value according to the specific application. In that way, each POI has a different visiting priority each time. The proposed solution considers a nonlinear model predictive control (NMPC) approach that minimises the map’s overall heat and considers several constraints related to the system dynamics and the environment (e.g., the presence of unknown obstacles). Possible applications are related to the research of gas leaks, area surveillance, patrolling, etc. The methodology is tested in a realistic simulation environment and through experiments.
Survey and Comparison of Pipeline of Some RISC and CISC System Architectures

Instruction set is a set of instructions used by CPU to calculate and control computer system, and is the interface between hardware and software. There are two common instruction sets: CISC and RISC. Pipeline technology is widely used in instruction set processor design to improve the efficiency of executing instructions. This paper introduces the difference between CISC and RISC in pipeline implementation, introduces the basic pipelining and two advanced pipelining - superscalar and superpipelining in detail, and introduces several pipelining using CISC and RISC architecture processors, including ARM, RISC-V, Longarch, and X86.
Improving Retweet Prediction via Tweet Features

Twitter is a popular social media platform used to share information. Retweeting another user's tweet is considered a powerful mechanism for disseminating information. Predicting whether a a tweet will be retweet has gained increasing interest in recent years. In this study, we examined the prediction of retweeting and the influence of tweet features on retweeting, using tweets with the hashtag #BlackHistoryMonth, which was a top trend in the US Twitter agenda in February 2023, as our experimental space. The tweet features tested included both directly obtainable features and five features obtained using these features. We used the Random Forest algorithm to classify retweeting based on all of these tweet features. We identified the impact of categorized tweet features on the importance of retweeting on Twitter. By adding our newly tested features, we improved the tweet features category by some 6% in terms of F-1 score. This study can contribute to a better understanding of social media usage and user behavior, as well as the development of marketing and advertising strategies.
Crop yield forecasting with climate data using PCA and Machine Learning

Accurately forecasting annual crop production is crucial for countries as it enables them to formulate import and export policies and estimate the economic benefits of their agricultural planning. The crop growth is significantly influenced by weather conditions throughout the year, and climate conditions during different stages of plant development can greatly affect crop yield. The availability of historical climate data has greatly benefited the agricultural sciences and food sector, particularly with the application of Artificial Intelligence methods in big data analysis, enabling the extraction of practical information and actions. The objective of this research is to develop a predictive Machine Learning (ML) model that utilizes climate data from a specific time frame to forecast the wheat yield in the Pelagonia valley, a crucial region for wheat cultivation in North Macedonia. Principal Component Analysis (PCA) was employed as a dimensionality-reduction method to reduce the input data set's dimensionality. A least-squares boosting regression model was implemented as the ML method to estimate wheat yield from climate data. The results indicate a high accuracy of wheat yield prediction, even with limited dataset, on both the training and testing datasets. The study demonstrates the feasibility of utilizing ML methods as complementary to existing models for accurate wheat yield forecasting, offering significant advantages due to the ease of calibrating the ML model parameters.
DeepEgo: Deep Instantaneous Ego-Motion Estimation Using Automotive Radar

The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from input point clouds and applies weighted least squares (WLS) for motion estimation is utilized in DeepEgo. Additionally, a novel loss function, Doppler loss, is proposed to locate “inlier points” originating from detected stationary objects without human annotation. Finally, a challenging real-world automotive radar dataset is selected for extensive performance evaluation. Compared to other methods selected from the literature, significant improvements in estimation accuracy, long-term stability, and runtime performance of DeepEgo in comparison to other methods are demonstrated.
Research of Rabbit and Human Eyes Structure in Electrophysical Diagnostic Using 3D Modeling

This article explores the concepts of electroretinography and addresses the issues of its importance in modern medicine and science in general. Here we consider such topics as the diagnostic capabilities of an electroretinograph, a graphical representation of an electroretinographic study - an electroretinogram - and its features. However, this article is not only about the diagnosis of the human eye, but also the eyes of rabbits, too, because these animals are indispensable if we are talking about new experiments and research in this area. The purpose of this article, among other things, is to confirm this using 3D modeling methods. Here we investigate the structures of human and rabbit eyes, give their parameters and resistivity of tissues, build simplified diagrams of these organs, consider their similarities and differences, and ultimately present an algorithm for modeling human and rabbit eyes in the Autodesk Inventor environment as the most important product of this article.
Malware Detection in AdHoc E-Government Network Using Machine Learning

Malware developers have been kept tremendously busy in recent years due to the increasing use of the Android operating system in e-government operations. Significant numbers of virus developers have as their primary purpose the transformation of mobile devices into bots. As a result, it is now feasible for hackers to take control of the device and maybe other devices that are connected to it, so forming botnets. Botnets are utilized to aid the execution of numerous harmful operations, including distributed denial of service (DDoS), spamming, and data theft, among others. The aim of this research was to determine whether an unidentified application is a “attack” or a “normal” application by gleaning static and dynamic information from it. Some examples include the k-nearest neighbors (KNN) [17], the support vector machine (SVM) [18], the convolutional neural networks (CNN) [19], the dense neural networks (DNN) [20], the gated recurrent units (GRU), the long short-term memory (LSTM) [21], and the hybrid deep learning convolutional neural networks long/short-term memory (CNN-LSTM) and convolutional neural networks [22], [23]. It was assessed how effective various machine learning and deep learning algorithms are at detecting mobile malware attacks, and a number of these techniques were evaluated.
VLSI Architecture of a High Speed Polar Code Decoder using Finite Length Scaling LDPC Codes

In this concise a polar decoder form propagation based on belief is formulated which employ finite length LDPC systems. Here the belief sum-product Propagation (BP) is designed for LDPC system beyond affecting the binary communication erasure channels. Belief decoding is parallel and iterative in nature, as it own iteratively nature the required idleness and energy dissemination increments straightly. The prompt report stated that unstable node (VNs) is reduced during individual iteration than as BP. Declination of erased VNs reduces decoding process cause a forceful decrease in complexity, compared among polar decoder that is designed based on CSFG. CSFG is implemented with Quarter-way scheduling algorithm, a sub-factor graph reduces valuations of taken by belief decoder but due to different variable nodes used in the process it has large complication during design. To overcome this BPD with LDPC codes is designed. Simulation and synthesis results in the progressive art reveal that LDPC system drawn better in performance in contrast with belief based propagation.