Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Vehicles in Point Clouds of Underground Parking Lots Based on Graph Convolution

Three-dimensional point clouds can describe the shape and position of objects more accurately when compared with 2D images, thereby providing richer information for object recognition, detection, and reconstruction tasks. Extracting vehicles in point clouds of underground parking lots can help autonomous vehicles achieve automatic parking. Camera-based perception algorithms will fail in complicate environments, so it is necessary to study algorithms for extracting targets using point cloud data. In this paper, we designed an effective method to extract the vehicles in the underground parking lot. First, the point clouds belonging to the vehicle will be segmented using a neural network based on graph convolution, and then different vehicles will be separated based on clustering. Finally, the minimum bounding box for each car is calculated. The proposed approach achieved much better results on the point cloud dataset than other state-of-the-art methods. Our method achieves 99.6% in Overall Accuracy and 98.5% in Mean IOU (Intersection over Union).
Intelligent Video Data Security: A Survey and Open Challenges

A range of video contents and technology have provided convenience to humans, with real-time video applications—such as surveillance applications—able to contribute to increasing public safety by reducing physical crimes. The development of video technology has made it possible to achieve an improved quality of life. However, this technology can also be exploited and lead to security issues such as physical and digital crimes. Unfortunately, security breaches are increasing in complexity and frequency, making current countermeasures insufficient to prevent them. Given recent trends, we recognize the need for security technology to respond to advanced video crimes. Intelligent security is one of the methods that can be used to respond to these issues. Although research on video data security has been actively conducted, not enough studies have been published on video data security that also addresses intelligent security. Specifically, a classification system for research on video data security has not been provided, and no systematic analysis has been conducted for advanced research. Thus, the purpose of this is to fill in these gaps in existing research. This study offers a classification of research on video data security based on the collection and analysis of related works. Moreover, this study presents an analysis of research on video data security technologies combined with intelligent technologies based on SLR methodology.
On Scalable Video Codec for 4K and High Definition Video Streaming – the Hierarchical Adaptive Structure Mesh Approach “HASM-4k”

This paper presents an efficient scalable Video Codecs using hierarchical representation for video dynamics that is tailored to 4K (3840 × 2160) and ultra-large resolution. The proposed coding is based on the Hierarchical Adaptive Structure Mesh and has been applied to 4k video resolution. It is called HASM-4k and it is used for intra-frame compression. The proposed technique has a limit of computational requirement while it has a significantly better visual fidelity.
Exploring the Use of Data at Multiple Granularity Levels in Machine Learning-Based Stock Trading

In the last decade the Artificial Intelligence and Data Science communities have paid an increasing attention to the problem of forecasting stock market movements. The abundance of stock-related data, including price series, news articles, financial reports, and social content has leveraged the use of Machine Learning techniques to drive quantitative stock trading. In this field, a huge body of work has been devoted to identifying the most predictive features and to select the best performing algorithms. However, since algorithm performance is heavily affected by the granularity of the analyzed time series as well as by the amount of historical data used to train the ML models, identifying the most appropriate time granularity and ML pipeline can be challenging. This paper studies the relationship between the granularity of time series data and ML performance. It compares also the performance of established ML pipelines in order to evaluate the pros and cons of periodically retraining the ML models. Furthermore, it performs a step beyond towards the integration of ML into real trading systems by studying how to conveniently set up the most established trading system characteristics. The results provide preliminary empirical evidences on how to profitably trade U.S. NASDAQ-100 stocks and leave room for further investigations.
Topographical Feature Extraction Using Machine Learning Techniques from Sentinel-2A Imagery

The advancement in the satellite technology has made it possible to easily and frequently obtain the satellite images of most of the regions on the Earth. The satellite data contains the abundant amount of information which can be very useful for variety of societal applications. However, manual identification of the land cover in a particular area is a very challenging and time-consuming task. In this manuscript, an attempt has been made to better extract the landcover types from Sentinel-2A imagery using the popular classifiers such as random forest, SVM, Naive Bayes, Decision Tree (CART). The manuscript also validates the results obtained by the used models by computing the performance metrics. The analysis reveals that random forest classifier outperforms the rest of the classification methods in terms of better accuracy of 95.67%. This automated approach can be applied to large sets of data, reducing the need for manual labeling.
Generative Adversarial Method Considering Communication Transmission Distortion for Neural Network Codec

Recently, induced by incorporating the ubiquitous data collected by AI application, there are more and more demand for transferring data to cloud servers due to the restriction of computing resources of devices. To transfer image data efficiently, neural network (NN) codec can be a wise choice, which can result in higher ratio of compression with similar image quality compared with conventional codec methods. However, when the NN codec is employed in the communication system, it is likely that it can be disturbed by channel distortions. In this paper, we innovatively propose a norm method to measure the NN codec’s robustness for certain tasks. And with the aid of this method, we develop a greedy algorithm using gradients to find adversarial samples of certain tasks considering communication system distortions, i.e. channel distortions during compressed image or video transferring to cloud server systems. Aided by the adversarial samples, the NN codec has been proved to have better tolerance of communication distortions that the Top-1 accuracy of the image classification can be improved 1.8%.
Analysis of S-S Resonance Compensation Circuit of Electric Vehicle Wireless Power Transfer System

Wireless power transfer (WPT) plays a crucial role in battery charging of electric vehicles (EVs). In order to compensate for leakage inductance and magnetizing inductance, the resonant compensation topology is usually added to the magnetically coupled resonant wireless power transfer system (MCR-WPTS). The S-S compensation topology is studied and analyzed. A detailed mathematical model and a simulation model are established. The expressions of output current, voltage and transmission power are derived. The relationship between the input, output current, output voltage, transmission power and load and mutual inductance of S-S compensation topology is shown by simulation. The simulation shows consistent results with the analysis. In addition, this paper analyzes the sensitivity of the capacitance parameters in the Matlab/Simulink simulation model, and obtains the changing trend of the transmission power of the system with the capacitance parameters, which provides support for the further research on the selection of capacitance parameters.
Intuitive Robot Teleoperation Through Multi-Sensor Informed Mixed Reality Visual Aids

Mobile robotic systems have evolved to include sensors capable of truthfully describing robot status and operating environment as accurately and reliably as never before. This possibility is challenged by effective sensor data exploitation, because of the cognitive load an operator is exposed to, due to the large amount of data and time-dependency constraints. This paper addresses this challenge in remote-vehicle teleoperation by proposing an intuitive way to present sensor data to users by means of using mixed reality and visual aids within the user interface. We propose a method for organizing information presentation and a set of visual aids to facilitate visual communication of data in teleoperation control panels. The resulting sensor-information presentation appears coherent and intuitive, making it easier for an operator to catch and comprehend information meaning. This increases situational awareness and speeds up decision-making. Our method is implemented on a real mobile robotic system operating outdoor equipped with on-board internal and external sensors, GPS, and a reconstructed 3D graphical model provided by an assistant drone. Experimentation verified feasibility while intuitive and comprehensive visual communication was confirmed through an assessment, which encourages further developments.
Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks

With the advances in modern communication technologies, the application scale of Internet of Things (IoT) has evolved at an unprecedented level, which on the other hand poses threats to the IoT ecosystem. As the intrusions and malicious actions are becoming more complex and unpredictable, developing an effective anomaly detection system, considering the distributed nature of IoT networks, remains a challenge. Moreover, the lack of sufficiently large amount of data samples of IoT traffic and data privacy pose further challenges in developing a behavior-based anomaly detection system. To address these issues, we present an unsupervised hierarchical approach for anomaly detection through cooperation between generative adversarial network (GAN) and auto-encoder (AE). The problems of data aggregation and privacy preservation are addressed by reconstructing a sampling pool at a centralized controller using a collection of generators from the individual IoT networks. Then, a centralized global AE is trained and passed to individual local networks for anomaly detection after a final adaptation with the local raw data from the IoT nodes. The performance is evaluated using the UNSW Bot-IoT dataset and the results demonstrate the effectiveness of our proposed approach which outperforms other approaches.
Radar-Based High-Accuracy Cardiac Activity Sensing

Noncontact vital-sign telemonitoring by radar systems is applied more widely. When considering the implementation of Doppler radar for more precise extraction of cardiac physiological parameters to adapt to clinical diagnosis of heart diseases, the most open heart rate (HR) detection methods based on the periodic information are far from enough. Here, this article presents a new simple, effective, and high-accuracy settlement to maintain cardiac activity sensing comprehensively and systematically for the first time, for the estimation of beat-to-beat intervals (BBIs) and cardiac timings. We propose the decoding peak detection (DPD) method to address heartbeat peaks’ extraction problem by decoding the most likely sequence of states from the single-band frequency envelogram (FEnv) of the radar signal, thus realizing HR variability analysis. Furthermore, a hidden semi-Markov model (HSMM) is utilized to detect cardiac timings in bandpass filtered radar data, which is equivalent to positions of the R-peak and end-T-wave carried in electrocardiogram (ECG). Experimental results show that the proposed method achieves an average $F_{1}$ score of 93.19% ± 0.73% with the ECG as gold-standard, evaluated on a data set from six subjects. The accuracy of radar-based extracted BBIs is assessed by the mean relative error, which ranges from 0.51% to 1.06% by HSMM and 0.37% to 1.15% by DPD, respectively. Finally, a series of experiments are conducted to confirm the high agreement between radar signals and cardiac activities. Meanwhile, from the cardiac timings analysis by the proposed methods, we can find the advantage of radar-based detection at some specific timings compared with ECG-based, e.g., end-T-wave detection.
A Fast GPS Signal Acquisition Method Based on Fuzzy Logic

GPS, as a widely used positioning tool, covers more aspects of people's daily life and people have been interested in the study of GPS signal acquisition algorithm for a long time. In this paper, a widely used signal acquisition algorithm with superior performance, PMF-FFT algorithm, is optimized. A fast GPS signal acquisition method based on fuzzy logic is proposed to solve the problem of signal acquisition failure caused by weak signal being ignored as noise under low SNR condition. Simulation results prove that the algorithm proposed in this paper can effectively capture signals and performs well under weak signals.
Change Detection Network of Nearshore Ships for Multi-Temporal Optical Remote Sensing Images

Ship change detection is of great significance for maritime safety supervision, wharf vessel management, and vessel life cycle analysis. Nowadays, the ship change information is obtained through the difference between the multiple independent object detection results of multi-temporal images. However, it neglects the temporal correlation of the concerned features, impeding further improvement of the detection accuracy of change detection. Therefore, based on the sequential network, namely convolution LSTM, we built an end-to-end ship change detection (SCD) R-CNN network. The network extracts abstract semantic information reflecting the changed features of ships, and the time correlation between features of the different phases is established. Specifically, the changed features are utilized to guide the judgement of ship change. It is verified from the RS images that the proposed network avoids the misjudgment caused by the errors of object detection in the conventional method. In addition, a higher efficiency is revealed, maintaining the accuracy of the change detection of ship targets.
Energy and Spectrum Efficient User Association for Backhaul Load Balancing in Small Cell Networks

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipment in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipment to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency. We develop centralized and distributed user association algorithms based on sequentially minimizing the power consumption. We evaluate the performance of the proposed algorithms under two scenarios and show that they achieve higher energy efficiency compared to the existing algorithms in the literature, while maintaining high spectral efficiency and backhaul load balancing.
Adaptive Feature Aggregation Network for Object Detection in Remote Sensing Images

Object detection in remote sensing images is a challenging task because of the large scale variations across the geospatial objects. The feature pyramid network (FPN) is widely used to alleviate the scale variations problem, however, it only fuses the features from adjacent levels and lacks the information of the entire feature hierarchy. In this paper, we propose a novel and effective feature pyramid aggregation network, called Adaptive Feature Aggregation Network (AFANet). Specifically, we propose the Adaptive Feature Aggregation (AFA) module to adaptively aggregate multi-level features of FPN and introduce the Bottom-up Path to enhance the location information of the entire feature levels. In addition, we use the Receptive Field Block (RFB) module to capture different receptive field features for each level feature map. We evaluate the effectiveness of our AFANet on the DOTA dataset and achieves noticeable performance compared with the baseline.
Energy Management of Integrated Energy System Based on Stackelberg Game and Deep Reinforcement Learning

The integrated energy system (IES) makes the power system, natural gas system, and heating system coupling, which brings more flexibility and economy to the terminal energy side. In this paper, the energy management problem of the IES considering integrated demand response (IDR) is studied, and it is modeled as the typical one leader-n followers Stackelberg game. The electric heating devices are considered in the user model, which enriches the IDR. Considering that the users’ privacy parameters may be unknown, this paper uses a deep reinforcement learning (DRL) based algorithm to solve the model. Numerical results show that users can reduce energy purchasing costs through the IDR, and the integrated energy service provider (IESP) can increase profit through energy conversion devices. At the same time, the results also show that the RL based algorithm used in this paper has good convergence performance and transfer performance.
A Supervoxel-Based Approach for Leaves Segmentation of Potato Plants from Point Clouds

This paper proposes a method for automatic 3D point cloud segmentation of leaves on individual potato (Solanum tubero-sum) plants from DAV-based images. Conventional methods based on visual inspection require significant field work and human effort. The proposed approach uses remote sensing techniques as well as the Voxel Cloud Connectivity Segmentation (VCCS) technique to identify geometrical features and calculate structural attributes. The main component of the method is the SuperVoxel-based leaves segmentation from the tridimensional photogrametric point cloud. The approach is tested using DAV data from an experimental plot. Super-voxel segments are compared with a manual segmentation conducted by experts. The results show that the proposed method allows an accurate leaves segmentation, i.e. it is as good as conventional methods, but much more economic and timely.
Protecting Intelligent Route Control Against Spurious Timeouts

Intelligent Route Controllers (IRC) are being increasingly used by multi-homed stub networks to improve performance and reliability of Internet accesses. However, little is known about the mechanisms used by IRCs to react to path failures and losses of probing packets. Since their reaction time to these events depends on the Request Timeout (RTO) used to declare that a probe is lost, this paper propose two automatic IRC’ monitoring mechanisms inspired in the Jacobson’s algorithm of TCP and in the Box-plot, that enable IRCs to adapt the RTO timers to network conditions, and above all, to protect IRCs against spurious RTO timeouts, while keeping short reaction times. Furthermore, simulation results show the effectiveness of these algorithms to cope with spurious RTOs.
Assessing Land Suitability for Managing Urban Growth: An Application Of GIS and RS

World is marching towards sustainable urban settlement and resource utilization has to be augmented. Land as a limited resource, needs to be utilized judiciously. Built density and urban fabric poses challenge to urban planner and decision maker, to plan the city in such a way that it will use land resource optimally. So this study aims to identify suitable land which has high potential to satisfy future urban needs of Surat regions a city in western part of India. Multispectral Image (LISS IV) is used to extract the Land cover map of city. From DEM, slope and elevation maps are generated. Land suitability analysis is carried out using weighted overlay tool of Geographic Information System (GIS) and weights are obtained by Analytical Hierarchy process(AHP) considering socio-economic, utilities, environment and physical as main criteria. Weightage has been given by experts in planning area and GIS environment. The result of this study shows that 82.93 sq.km (8.56%) area is very suitable, 128.84 sq.km (13.30%) is suitable, 749.82 sq.km (77.37%) is moderately suitable, 6.43 sq.km (0.66%) is less suitable and 1.07 sq.km (0.11%) is unsuitable for the future development. This study of land suitability can help the urban planner to regularize the proper zoning and to prepare the development plan as well as local plan. Also policy maker can use this map to form appropriate policy to cop up with blighted situation in urban area and to carry out sustainable planned development to achieve Sustainable Development Goals(SDGs) announced by ministry of Urban Development.
Reliable Identity Management System Using Raspberry Pi

Biometric authentication, the process of verification using unique biological characteristics, is considered one of the most accurate technologies for authentication. It compares two or more biometric data to identify the differences among them properly. Based on the comparison between individual’s biometric features and stored authorized features, it makes the decision of matched or mismatched. If the given sample of the biometric data matches with the stored one, authorization is confirmed. Nowadays, many real-time applications use biometric authentication to meet security purposes fully. However, it is difficult to implement fingerprint recognition system specially on Linux based computer. A well-known, less costly embedded computer called Raspberry Pi is used to build the entire system. Fingerprint enrollment, fingerprint recognition, photo capture, QR code generation as well as QR code reading were entirely done using python and python associated libraries. For storage management, we have used MySQL as it is convenient and open-source. This paper discusses about a quality authentication model which is highly capable and cost- effective. The authentication process consists of extracting the biometric features with ORB algorithm, matching those features using FLANN matcher, generating and validating QR code based on encrypted personal information accurately. Here, AES algorithm is used for encrypting personal data which is then converted and stored in form of QR code. Final authentication process involves both fingerprint and QR code validation. Performing two factor authentication affixes more reliability to the system.