Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Grained Correlation Representation for Graph-Based Point Cloud Attribute Compression

Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. A key to good compression performance of GFT is to construct the graph Laplacian matrix that accurately models signal correlation. Nevertheless, existing attribute compression methods based on GFT adopt the distance metric to define the Laplacian matrix, which does not well represent the color correlation in case of a poor relationship between geometry and color. Hence, considering point cloud color variation in space, we propose additional three kinds of fine-grained correlation representation as Laplacian matrices for patches with different texture categories. Furthermore, we utilize texture complexity features as prior to design a stage-wise decision strategy for guiding each patch to choose appropriate correlation representation in low computation complexity. Experimental results demonstrate our method achieves better compression performance compared with other platforms. Meanwhile, additional experiments also adopt Lagrangian Rate-Distortion Optimization (RDO) to choose optimal one from three correlation representations, verifying the effectiveness of our proposed stage-wise decision strategy.
Automated Classification of Cervical Image Based on Deep Neural Network

Cervical cancer is one of the most common gynecological malignancies. The colposcope can observe the cervical surface through the microscopic biopsy, which can enhance the diagnosis rate of the cervical cancer and help patients to receive timely and effective treatment. Here, this study combines the deep learning and traditional machine learning algorithm to make the classification of the cervix. Firstly, a series of preprocessing operations are carried out on cervical images, which can improve the accuracy and efficiency of the subsequent cervical classification. Secondly, this study uses the traditional machine learning algorithms and the classical neural network to verify the classification performance of cervical datasets. Thirdly, AlexNet-SVM model uses AlexNet convolutional neural network (CNN) to extract the features of cervical images at the front end, and then input the extracted feature parameters into the support vector machine (SVM) classifier at the back end. Meanwhile, the proposed AlexNet-SVM model utilizes the transfer learning method of freezing convolution layers of the model to improve the classification accuracy. The experimental results show that the accuracy of AlexNet-SVM model is 91.77%, the precision is 93.72%, the sensitivity is 86.15% and the specificity is 95.83%. The current work will be helpful to classify and detect the early cervical lesions.
Improved CT Algorithm Based on Bit Transform

The RFID system is the core technology of the future Internet of Things system and Industry 4.0, and the RFID anti-collision algorithm is the basis for realizing the communication between the tag and the reader. Aiming at the problems of high computational complexity, low recognition efficiency and complex recognition process in existing RFID anti-collision algorithms in existing international standards, based on the query tree algorithm, combined with the extended bit transformation method, a relatively simple method is proposed. We propose a relatively simple anti-collision algorithm for RFID: Improved Collision Tree based on Bit Transformation Algorithm (ICTBTA). The simulation results show that the system efficiency of the tree algorithm proposed in this paper can be stabilized at more than 80%, and the efficiency of the currently used CT algorithm can be improved by more than 50%. Therefore, ICTBTA can be applied to common RFID passive tag identification systems.
Energy Router Optimization Strategy Based on LSTM algorithm for Real-Time Congestion Prediction

As the core equipment of the energy Internet, energy routers (ERs) can optimize energy transmission and energy scheduling in time and space, thereby improving the transmission efficiency and stability of the multi-source power network. This paper establishes a graph model of the energy transmission network based on the topological structure of energy routing. Long and short-term memory (LSTM) is introduced to predict the congestion of ERs. As a result, an energy routing optimization strategy based on LSTM for real-time congestion prediction is proposed by considering the factors of congestion and transmission loss. Then Dijkstra algorithm is applied to determine the best path for energy transmission. Finally, numerical simulation verifies the feasibility and effectiveness of the energy routing optimization strategy proposed in this paper.
SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics

In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of mutated genome sequences, which can be used for supervised learning of oncology-relevant tasks by our Transformer-based Deep Neural Network, SETQUENCE. Second, we extend the model to incorporate these representations as well as multiple sources of omics data in a flexible way with SETOMIC. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are shown to be able to recapitulate the biological contribution of several features in cancer, such as individual expression loci. This validation opens the door to novel directions in multi-faceted genome-wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.
Dependency-aware Task Scheduling and Cache Placement in Vehicular Networks

Mobile Edge Computing (MEC) enables vehicles to flexibly obtain computing, content storage and other services by deploying at the network edge, which is of great research significance. The application tasks in vehicular networks can be distributed to MEC or task vehicle for collaborative processing. Aiming at the situation that the task dependency graph with multiple corresponding relationships is represented by Directed Acyclic Graph (DAG), this paper considers caching the program data of the corresponding node, and explores the problem of task scheduling and resource allocation in the cache enhancement scenario. Firstly, the task scheduling and cache placement decision are modeled as the optimization problem of minimizing the completion time. Then, through the analysis of the optimization problem, the problem is divided into two sub problems: task scheduling and cache decision. For the two sub problems, the task scheduling algorithm based on the latest start time and the cache decision algorithm based on dynamic programming are designed respectively. Simulation results show that the proposed algorithms and scheme can significantly reduce the completion time and task failure rate compared with the benchmark scheme.
A Robot Relocalization Method Based on Laser and Visual Features

Relocalization is a well-known problem to regain the robot's pose in an incorrect pose. The robot using Adaptive Monte Carlo Localization(AMCL) algorithm can achieve localization based on laser information, but AMCL is prone to localization drift and challenging to achieve relocalization, especially in environments with simple and repetitive geometric features. Image can obtain more texture information and color information than laser, which determines that the camera is more accessible to relocalization than the lidar. This paper proposes a fast and reliable relocalization method to solve the problem of laser localization drift and kidnapped robot by visual information. The robot can build and save the point cloud map and the grid map and calculate the correspondence. Relocalization is achieved through a relocalization trigger mechanism and Oriented FAST and Rotated BRIEF(ORB) feature points. Finally, Several experiments in the simulation and indoor environment were performed to verify the effectiveness of the proposed approach. The experimental results show that the relocalization recovery time of the proposed method is within two seconds. Compared with the relocalization achieved by lidar alone, the proposed method is three times faster.
Enhancing Localization Accuracy of Indoor Occupancy Tracking Using Optical Camera Communication and Human Pose Estimation

Precise location of personnel indoors is one of the crucial prerequisite of data analytics of workforce productivity as well as workplace safety and health. In this paper, we propose an enhanced indoor occupancy tracking system using optical camera communication (OCC) on top of conventional surveillance cam-eras. The proposed system is able to track workers who carry unique infrared LED beacons indoor. OCC with infrared beacons is adopted for identity check, while the localization accuracy is enhanced using deep-learning-based human pose estimation to infer the footfall location. Particularly, a comprehensive footfall estimation algorithm empowered by deep-learning-based human pose estimation is presented, which provides precise footfall estimation considering real-time actions of person and occlusion patterns. In order to validate the accuracy improvement, The experiment is conducted in an open lab with area of about 30 m2 using single surveillance camera sensors. According to the experimental results, the localization accuracy is improved by 9.36% on average and 44.19% at far end, in comparison with conventional methods.
Passive offset adjusting wheel mechanism

Small mobile robots have been studied for various purposes such as exploration and research. For small mobile robots, there is a trade-off between miniaturization and performance in overcoming steps, and it is desirable to improve the performance without increasing the number of actuators. Various methods such as crawlers, wheels, and leg transfers have been developed to achieve a high step-clearing performance without increasing the size of the mobile robot. Among these methods, we focused on the use of eccentric wheels. By eccentrically rotating the center of the wheel, the robot can traverse higher steps than that is capable by a normal wheel. Although this is a simple mechanism, the eccentric wheel has a disadvantage in that its traveling efficiency on flat roads is inferior to that of a normal wheel. In this study, we developed Passive offset adjusting wheels that automatically deforms into an eccentric wheel only when stepping over a bump and remains in the normal wheel state at other times. Experiments were conducted using a prototype rover with a wheel diameter of 120 mm, and it was confirmed that the eccentricity of the wheel was 50% higher than that of a normal wheel.
Adaptive Beam Alignment Based on Deep Reinforcement Learning for High Speed Railways

The fast moving characteristics of high-speed trains pose a challenge to the beam alignment of high-speed railway millimeter wave communication systems. With powerful learning capabilities, machine learning-based methods can help improve the beam alignment performance, such as greatly reducing the delay. In this paper, a non-convex optimization problem is formulated aiming at maximizing the received power of downlink transmission, and deep reinforcement learning is used to assist beam alignment. Particularly, an adaptive beam alignment algorithm based on prioritized experience replay double deep Q-Learning is proposed to adjust beam direction dynamically. The algorithm observes the position of the train and the beam direction of the train roof mobile relay to guide the beam adjustment. To reduce the adjustment frequency and the requirements for train position accuracy, the service range of remote radio head is divided into multiple location bins. The beam direction adjustment is only executed when the train enters the next location bin. Simulation results verify that compared with other baseline schemes, the proposed algorithm can effectively improve the received power and reduce the beam alignment delay.
Improving Image Paragraph Captioning with Dual Relations

Image paragraph captioning aims to generate multiple de-scriptive sentences for an image. However, most previous methods ignore the explicit relations among objects resulting in unsatisfactory performance. In this paper, we propose a novel model (i.e., DualRel) to capture spatial and seman-tic relations among objects. Specifically, the spatial relation embedding is obtained solely from images using a predefined geometry pattern. With the help of captions, the semantic relation embedding is learned in a weakly supervised man-ner. These two relation embeddings are then interacted with regional features of objects through a relation-aware attention interaction. It first obtains a visual context vector using regional features. Then with the visual context vector, we obtain the corresponding spatial and semantic relation-aware vectors using attentions. These three vectors are fused with two gates for language decoding to further generate a para-graph. Experimental results on Stanford benchmark dataset show that DualRel achieves remarkable improvements11Code released at https://github.com/fuyunll07/DualRel.
A Self-training Multi-task Attention Method for NILM

The key task of non-invasive load monitoring (NILM) is to know the power consumption of all household appliances, from which the power consumption of individual household appliance can be disaggregated. The power consumption and on/off state of household appliances are expected to be obtained, the multi-task learning model is used. One task is used to train the power comsumpution of household appliances, the other task is used to train the on/off state of household appliances, and then these two results are combined as the final result. In this paper, a self-training multi-task learning model is proposed. In the model, a parallel structure is used to deal with two different tasks, and the outputs of two branches are directly combined as the final output. The model only needs one loss function and is only trained once. In addition, we also introduce attention mechanism into the proposed model. Finally, two public data sets are simulated to verify the effectiveness and superiority of the proposed method.
Design Of Ultrawide-Band MIMO Antenna With Polarisation Diversity

An ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna having double band-notched attributes is created on a FR4 substrate (1.6 mm). The antenna is composed of a better ground plane and dual microstrip feeding lines with a minimized dimension of $40^{\ast}40\ \text{mm}^{2}$. By carving the T-molded feed over ground stubs, the port isolation between individual components of antenna is enormously expanded. To decrease the obstruction from the adjacent band, a band-notch of the antenna is required. Measurement and simulation are utilized to concentrate on the antenna execution as far as isolation between the two input ports, envelope correlation coefficient, impedance matching, efficiency, radiation pattern and peak gain. The exploratory outcomes show that the bandwidth of the planned antenna is from 3.3 to 12 GHz, alongside double notched bands covering 0.38 GHz and 0.53 GHz, and the mutual coupling is smaller than 15 dB within the entire acting transmission capacity. The axial ratio bandwidth for the same design is obtained in the range from 4.27 GHz to 5.47 GHz. This antenna exhibits great radiation qualities, a depleted envelope correlation coefficient ($\text{ECC} < 0.007$) and stable gain, which is appropriate for UWB MIMO framework implementations.
Quantized Iterative Learning Control for Nonlinear Switched Discrete- Time Systems with Actuator Saturation

A quantized iterative learning control (ILC) scheme is investigated for nonlinear switched discrete-time systems with actuator saturation in repetitive operations. The logarithmic quantizers are utilized to quantize the tracking error signals, and the sector bound method is applied for analysis and mitigation of quantization influence. By using mathematical induction method, it is strictly proved that the tracking error of each subsystem will converge to zero with the increase of iteration. A numerical example is given to illustrate the validity of the quantized ILC algorithm.
Performance Analysis of a DFIG Based Wind Turbine with BESS System for Voltage and Frequency Stability during Grid Fault

The ease of Doubly-Fed Induction Generator (DFIG) based wind turbines is largely deployed due to their variable speed feature and hence influencing system dynamics. However, owing to grid faults, the output power fluctuation in the DFIG wind turbine system brings a major concern to power system stability. In this paper, the grid voltage and frequency stability of the wind power system investigates different cases such as DFIG and the approach of a Battery Energy Storage System (BESS). Designing of a wind turbine model including Rotor Side Controller (RSC) and Grid Side Controller (GSC) and connected to the grid. An equivalent BESS is introduced in the power system model and connected to the grid through a three-phase inverter. The BESS system is designed to stabilize the frequency at a constant value with controlled active power also; voltage is controlled by reactive power. To design the wind turbine only active power is considered in this specific work. Therefore, the system performance has improved after including BESS. The performance analysis is observed by simulation work through “PSCAD/EMTDC” professional software, which is the most realistic and well-organized software, especially for power system analysis.
Fault-Tolerant Consensus of Single-Integrator Multi-Agent Systems under Synchronous Self-Sensing Function Failure

This paper investigates fault-tolerant consensus of single-integrator multi-agent systems (MASs) with partial agents subject to synchronous self-sensing function failure (SSFF). First, a strategy of recovering the connectivity of network topology among normal agents is proposed via multi-hop communication together with agents subject to SSFF as routing nodes. Second, a fault-tolerant consensus protocol with time-varying gains is designed. Then, consensus convergence is analyzed by separately investigating the dynamics of normal agents and agents subject to SSFF. It is proved that under proper time-varying gains, single-integrator MASs using the proposed strategy of recovering the connectivity of network topology and fault-tolerant consensus protocol can achieve consensus. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
UAV Autonomous Indoor Exploration and Mapping for SAR Missions: Reflections from the ICUAS 2022 Competition

The technological advancement in Unmanned Aerial Vehicles (UAVs) or drones and their deployment in real-life Search and Rescue (SAR) missions is imminent. We, therefore, present a perception-aware autonomous exploration framework aimed at performing vision-based target detection and collision avoidance with an Unmanned Aerial Vehicle (UAV). The UAV utilizes a depth camera for maneuvering and finding the target. The underlying indoor exploration approach considers autonomous collision-free navigation, as well as target detection with a ballistic ball payload delivery without a prior map. Moreover, the proposed method allows safe navigation in enclosed unknown areas congested with randomly positioned obstacles and target locations. Our underlined end-to-end system architecture integrates the proposed exploration strategy. Extensive simulation experiments, using several Key Performance Indicators (KPIs), showcase the effectiveness of the proposed Robot Operating System (ROS) framework in a simulated Gazebo environment under various parameter settings.
Feature Pyramid Boosting Network for Rendering Natural Bokeh

Natural bokeh is a typical characteristic of digital single-lens reflex (DSLR) cameras and high-quality lenses, which is commonly used to emphasize a subject from a distracting background. However, it is still a big challenge for mobile platforms to produce similar effects due to the small apertures of their lenses. Unlike many previous methods formulated as a two-stage task composed of depth/defocus estimation and defocus magnification, we propose a feature pyramid boosting network with novel hierarchical attention modules to render bokeh in one step. In addition, existing learning-based methods suffer from the pixel misalignment of the datasets. We present a well-aligned bokeh dataset captured by a DSLR to address this problem. Experiments show that our method can render comparable bokeh with the state-of-the-art method but requires fewer parameters.
Micro Accelerometer Built-In Self-Test and Calibration Using Genetic Algorithm and Interpolation Method

A Built-in Self-Test methodology has been developed for fault diagnostic in MEMS using the interpolation. In this work, a lookup table together with AKIMA interpolation has been used to calibrate the MEMS capacitive accelerometer to identify deficiencies after manufacturing by reevaluate the self test voltage value for the test electrodes. Several faults are carried out to the spring or finger of the sensing with applying GA to calculate the appropriate electrode voltages for the fault compensation. These voltages (after calibration) then stored in the lookup table to be used in the feedback for the proof mass displacement correction. The sensor has a nonlinear behavior of 0.16 % and a bandwidth (BW) of 400 Hz, with just an input range of ±50 g. This result of a sensing element was compared to various stored fault-free outputs computed by GA to determine the updated self-test bias voltage again for purpose of proof MEMS calibration, which served to verify this process.
Autonomous Power Management for Hybrid AC/DC Nano Grid Using ESS and DG

The increasing power demand with energy management is turning out to be an important aspect for electrical power system. Integration of Renewable Energy Source (RES) has indeed minimized the carbon emission and succeeded in delivering clean green energy. Power electronic converters are the major building block of power transmission and conversion at economical cost and also in reducing the design and installation of bulky electrical devices in remote zones, rural, congested place and consumers with few kilo watts of power. Designing a Nano Grid (NG) with less than 50kW of capacity will be benefit for the electrical distribution board. The proposed design discuss the design of Photo Voltaic (PV) with MPPT, DC/DC converter, single phase bidirectional Voltage Source Converter(VSC), Battery Energy Storage System (BESS) and three phase converter. The control technique for autonomous bidirectional power transfer to hybrid AC/DC NG with a simple PI controller. NG structure is simulated in Matlab/Simulink. This NG is equally adoptable for Pico Grids (PG), domestic and commercial consumers. It also features a load management strategy. The proposed work is reliable in upgrading to Micro Grid (MG) or integrating with MG with decentralized control scheme. This NG is plug and play type also suitable for Internet of Things (IoT) for various electrical farms and other applications.