Miracle-dz/UniFAR-FTUs huggingface dataset:
MS-SLAM: Motion State Decision of Keyframes for UAV-Based Vision Localization

To improve the robustness of UAVs in rotating and motion scenes, we propose a stereo simultaneous localization and mapping (SLAM) for UAVs with a new keyframe strategy that differs from current SLAM systems. Moreover, it has two strategies. 1) In the dominant strategy based on image quality, we filter and retain the strong feature points in each image frame by our own defined rules; this leads to longer survival time and attribute invariance in image tracking, and we save the image frames containing more strong feature points as keyframes. 2) In the secondary strategy based on motion state, we quantify the motion state during the UAV’s motion (called the compound rotation amount), and characterize the intensity of the motion. Since we want the UAV to have better robustness in the rotating scene, this strategy generates keyframes when the compound rotation amount meets the threshold. These two strategies are used to cope with gentle motion scenes and rotational motion scenes, respectively. Thus, the insertion of our keyframes is determined by the motion state; our SLAM system is proposed based on the motion state (MS-SLAM). In the back-end part of the system we construct a new weighted cost function to optimize the pose. Finally, through comparison experiments on the public dataset EuRoc, we demonstrate that our algorithm is more advantageous than some current mainstream algorithms. In difficult sequences, our algorithm compares the absolute trajectory error with ORB-SLAM2, SVO+gtsam, and VINS-Mono, the absolute trajectory error of our algorithm can be reduced by 87%.
An Intelligent Wearable Temperature Monitoring System for Epidemic Surveillance

Wearable electronics devices become more popular used for human being's physiological parameter monitor. Continuous body temperature monitor will be wide used in families and hospitals, great significance for human health state monitoring especially for body temperature sensing diseases such as corona-virus epidemic currently. In this paper, we introduce a continuous temperature monitoring system with high precision, continuous and long-time measurement, remote temperature collection, and low power consumption, which can monitor and warn the population with abnormal temperature in the epidemic in time. The high precision of the thermometer is up to 0.01 degrees Celsius, and IP multiplexing in Wi-Fi is adopted for transmission to increase the network capacity, and a high efficient working-sleeping mode achieves the low power consumption to satisfy 14 days of epidemic sensitive period. It can not only monitor the suspicious population effectively, but also reduce the workload and infection risk of the epidemic monitoring greatly. The system is easy to be integrated with the existing hospital information system, the proposed solutions can be applied to the monitoring and early warning of epidemic surveillances with abnormal temperature.
Cumulative Clustering Filter for MIMO Radar Detecting Human Hand Intrusion

MIMO radar sensors with a 3-dimension detection zone are recently used for safety-related sensors. They may possess the capability of detecting human body parts such as hands. When the reflectivity of a target is low and the sensitivity of the radar is therefore relatively high, it is necessary to eliminate cluttered noise out of detected points and increase the possibility of detecting the target at the same time. This paper presents a data processing method including a cumulative filter and clustering, for achieving the goals above. Taking the human hand intrusion scenario as an example, an experiment was conducted to investigate the improvement of the detection performance by implementing this cumulative clustering filter.
Generalized Quadrature Beamspace Modulation for Millimeter-wave Communication Systems using Lens Antenna Array

Millimeter-wave technology is one of the key 5G technology, which provides broadband data transmission (Gbps). Millimeter-wave band often suffers from high attenuation due to blockages and atmospheric absorption. In order to increase the propagation distance instead of single antenna transmitter, large antenna arrays are employed. Employing large number of antennas requires the need for optimal beamforming techniques and improved spatial diversity techniques. Large antenna array requires high implementation cost in terms of number of RF chain required. Limited number of RF chain reduces system cost but reduces the spectral efficiency and multiplexing gain. So, for improving spectral efficiency, index modulation technique is a best alternate for beamspace millimeter wave MIMO communication. A novel Generalised Quadrature Beamspace Modulation, which uses index modulation technique to improve Spectral Efficiency in beamspace channel for millimeter wave communication is proposed and compared with the existing Generalised Beamspace Modulation and Quadrature Beamspace Modulation. Spectral efficiency is found to better than the conventional methods.
An efficient Genetic Algorithm with Modified Crossover Operator for Community Detection in Social Networks

The study of community structure in social networks has become a popular topic in recent years. Many methods for detecting communities from networks have been proposed. Since the appearance of the modularity, many authors started to see community detection as an optimization problem. they often use genetic algorithm as an effective optimization technique to solve this kind of problem. But the existing algorithms based on GA have some drawbacks such as slow convergence and low accuracy. In this paper we propose a novel community detection algorithm based on genetic algorithm. To better adapt GAs to community detection problem a novel crossover operator has been designed. The proposed algorithm does not require any prior knowledge about the network. The experimental results reveal that our algorithm performs better than most existing techniques.
Optimization of the gear ratios for a vehicle manual transmission

This study aims to optimize gear ratios for the manual transmission of a conventional vehicle to enhance fuel efficiency and reduce emissions. Using Simulink-Matlab software, Simplex method, and algorithmic functions to analyze and calculate a conventional vehicle's fuel consumption in the standardized driving cycles, solving the optimization problem, checking and comparing with other initial parameters and termination criterion.
Flyback Converter for a Multi-Chemistry Battery Balancer

This paper presents the development and testing of a Flyback converter for multi-chemistry battery balancer. The development was done considering that this circuit must ensure efficiency, reliability and safety for battery balancers that equip Li-Ion and Pb-acid batteries. The design was done using LtSpice software, and the testing was done in Matlab/Simulink environment using real data for several batteries.
A Compact Model for Relaxation Effect in Analog RRAM for Computation-in-Memory System Design and Benchmark

Analog RRAM is considered as a promising emerging device for the future computation-in-memory system. However, the relaxation effect shows significant impact on system performance. It causes high accuracy loss for inference application. In this work, we have statistically studied the relaxation effect of analog RRAM. Based on the statistical measurement results, a compact model for relaxation is established. The simulation results can match experiment results well for different conductance levels at different relaxation time. Furthermore, a device-level to system-level simulation framework is proposed. The simulation framework supports both inference and on-chip training, and the compact relaxation model is embedded to the framework to benchmark the CIM system. Finally, a suppression method is further provided for relaxation effect from system level.
Evaluation of Biofidelity and a Proposal for Simplification of a Human-inspired Safety Dummy

To allow robots to safely perform collaborative work with people, we need to ensure the safety of individuals who might come into contact with robots. Part of this process is to develop a method by which pain and slight injuries can be precisely evaluated. For the first time in the world, we have developed a dummy by which we can evaluate human pain. This dummy is based on the human body structure and is designed to model the forces that are created in a human body at the moment of its coming into contact with a robot. We have confirmed so far that the dummy's force-displacement characteristics are similar to that of the human body if a quasi-static contact with a robot should occur. Having acquired such confirmation, this paper then discusses the biofidelity of the dummy during its dynamic contact with a robot. To ease the more common adoption of our dummy technology, we will also propose a method by which our human-inspired safety dummy can be simplified while maintaining its high degree of biofidelity.
Dynamics Learning With Object-Centric Interaction Networks for Robot Manipulation

Understanding the physical interactions of objects with environments is critical for multi-object robotic manipulation tasks. A predictive dynamics model can predict the future states of manipulated objects, which is used to plan plausible actions that enable the objects to achieve desired goal states. However, most current approaches on dynamics learning from high-dimensional visual observations have limitations. These methods either rely on a large amount of real-world data or build a model with a fixed number of objects, which makes them difficult to generalize to unseen objects. This paper proposes a Deep Object-centric Interaction Network (DOIN) which encodes object-centric representations for multiple objects from raw RGB images and reasons about the future trajectory for each object in latent space. The proposed model is trained only on large amounts of random interaction data collected in simulation. The learned model combined with a model predictive control framework enables a robot to search action sequences that manipulate objects to the desired configurations. The proposed method is evaluated both in simulation and real-world experiments on multi-object pushing tasks. Extensive simulation experiments show that DOIN can achieve high prediction accuracy in different scenes with different numbers of objects and outperform state-of-the-art baselines in the manipulation tasks. Real-world experiments demonstrate that the model trained on simulated data can be transferred to the real robot and can successfully perform multi-object pushing tasks for previously-unseen objects with significant variations in shape and size.
A Novel Feature Selection Method on Mutual Information and Improved Gravitational Search Algorithm for High Dimensional Biomedical Data

In the past few decades, the field of bioinformatics has accumulated a large amount of gene expression data which provided important support for the diagnosis of disease. However, high dimensionality, small sample sizes, and redundant features often adversely affect the accuracy and the speed of prediction. Existing feature selection models cannot obtain the information of these datasets accurately. Filter and wrapper are two commonly used feature selection methods. Combining the advantages of the fast calculation speed of the filter and the high accuracy of the wrapper, a new hybrid algorithm called MIIBGSA, is proposed, which hybridizes mutual information and improved Gravitational Search Algorithm (GSA). First, mutual information is used to rank and select important features, these features are further chosen into the population of the wrapper method. Then, due to the effectiveness of the GSA algorithm, GSA is adopted to further seek an optimal feature subset. However, GSA also has the disadvantages of slow search speed and premature convergence, which limit its optimization ability. In our work, a scale function is added to the speed update to enhance its searchability, and an adaptive ${k}_{best}$ particle update formula is proposed to improve its convergence accuracy and propose a fitness sharing strategy to enhance the randomness of particle populations and searchability through the niche algorithm of fitness sharing. We used 10-fold-CV method with the KNN classifier to evaluate the classification accuracy. Experimental results on five publicly available high-dimensional biomedical data sets show that the proposed MI-IBGSA has superior performance than other algorithms.
A Comparative Analysis of Fault Tolerance Methods in 3D-NoC

Networks-on-Chips (NoCs) have found wide acceptance in many-core systems for on-chip communication due to their ease in addressing communication problems in integrated systems. Due to the incompetence of the 2D NoC structures, academia and industry have moved towards the 3D-NoC. The 3D-NoC can be manufactured with the help of Through Silicon Vias (TSV) as an interconnecting mechanism between the horizontal layers. These TSV links exhibit high vulnerability to the faults in the system. Significant research has been done to reduce this vulnerability by employing a few algorithms and schemes. This paper provides a survey of the different methods applied and their further advancements.
Coherence and Adaptivity in Frameless Rendering-A Practical and Information Theoretic Analysis

Virtual and Augmented Reality applications demand low-latency rendering. Adaptive Frameless Rendering (AFR) techniques offer a potential alternative to currently dominating non-adaptive double-buffered real-time 3D rendering techniques. However, due to their adaptive non-contiguous importance sampling, AFR does not benefit fully from the performance improvements offered by caching the local scene information, resulting in a lower sampling rate. This article presents an experimental, followed by an information-theoretic analysis, of this coherence vs. adaptivity trade-off in AFR. It introduces a novel way to utilize entropy to evaluate the efficiency of a rendering technique. It also presents various spatial coherence exploiting techniques in 3D path tracing and their application to AFR, paving the way to future research in the field.
SeNonDiv: Securing Non-Volatile Memory using Hybrid Memory and Critical Data Diversion

The emerging Non-volatile memories are projected as alternatives of traditional DRAM-based main memories. However, their non-volatility feature leads to serious security vulnerabilities. The sensitive data stored in these memories can be easily taken away due to prolonged data retention. A wide variety of encryption-based techniques protect these data at the cost of harmful side effects of encryption algorithms like high encryption/decryption latency and increased encryption induced write activities. It launches a tug-of-war between security provisioning and system performance degradation as well as shortened lifetime of NVMs. In this paper, we propose a data-diversion based technique that protects the security-sensitive data of the applications by allocating the security critical pages in the volatile DRAM part of a DRAM-PCM hybrid main memory system on page faults. Experimental evaluation shows significant improvements in performance and lifetime compared to a partial encryption and a full encryption based technique.
User Interest Prediction based on Social Network Profile with Machine Learning

The explosion of social media data which holds important, massive, rapidly evolving unstructured information has provided an opportunity to research public opinion and know people's feelings. Capturing the views on social events, corporate strategies, political movements, marketing campaigns and product preferences et cetera brings growing interest from the scientific and business communities to improve their performances. This has resulted in an emerging field of social network mining or sentiment analysis. This paper describes a system that was built to analyze data from social media platforms such as Twitter, Instagram, Facebook and Reddit. The data is gathered from various services and programs which involves using the official API's and scraping tools. Machine Learning & Image Processing algorithms are applied on the gathered data to gain insights about the individual users' interests, usage patterns, media reachability, et cetera. These models are then relayed to a web application where various visualizations of the insights can be obtained.
A Review on Workload Characteristics for Multi Core Embedded Architectures using Machine Learning and Deep Learning Techniques

Optimization of energy consumption in system on-chip (SoC) become a challenging task in real-time. Detection of best core for each workload is an additional critical task. To overcome these challenges, we analyzed the different machine learning and deep learning algorithms for mapping each workload on the Quad-core platforms. In this paper, we adopted support vector machines (SVM), naïve baize, random forest and KNN for prediction of the best core for each workload. Initially, we observe the workload characterization in terms of memory, instruction cycles, branch data's and developed as a database. In second phase, we deployed the ML and DL algorithms with trained database to predict accurately the best core for each workload. In the third phase, prediction accuracy, energy consumption metrics are observed and compared with the traditional algorithms. The proposed model is executed on Rasp -pi Quad-Core hardware platform and ML algorithms are simulated on the python IDE. Simulation results illustrates the prediction accuracy is achieved up to 99% on LSTM prediction for IomT benchmark and 8.6% on the energy consumption metric when compared to other ML techniques.
MulCode: A Multi-task Learning Approach for Source Code Understanding

Recent years have witnessed the significant rise of Deep Learning (DL) techniques applied to source code. Researchers exploit DL for a multitude of tasks and achieve impressive results. However, most tasks are explored separately, resulting in a lack of generalization of the solutions. In this work, we propose MulCode, a multi-task learning approach for source code understanding that learns unified representation space for tasks, with the pre-trained BERT model for the token sequence and the Tree-LSTM model for abstract syntax trees. Furthermore, we integrate two source code views into a hybrid representation via the attention mechanism and set learnable uncertainty parameters to adjust the tasks’ relationship.We train and evaluate MulCode in three downstream tasks: comment classification, author attribution, and duplicate function detection. In all tasks, MulCode outperforms the state-of-the-art techniques. Moreover, experiments on three unseen tasks demonstrate the generalization ability of MulCode compared with state-of-the-art embedding methods.
Interoperable Blockchain Solution For Digital Identity Management

Identity management is the set of policies, processes, and technologies used for managing access to information systems, safeguarding digital data, and tracking system activities. The existing Identity management system is not that secure, reliable, distributed and trustworthy. All these systems containing digital information are vulnerable to getting hacked or sometimes act as a malicious entity if the data is altered on central servers. Data breaches, including confidential data, are happening regularly. Blockchain on the other hand is one of the most emerging technologies that has gained a lot of attention in the past 5–6 years. Blockchain can address most of the issues with the existing identity management system. Over few years, various dApps have been created using Blockchain Framework (leveraging PKI system) to solve the problem of Digital Identity Management, but at the same time, most of the work done is compromising the feature of decentralization (systems based on Centralization as Verifiable Credentials / Public-Private Keys are stored in the central server for verification). In this system, the authors have proposed an interoperable blockchain solution for Identity Management. The purpose of this research work is to study the feasibility of whether or not we can use interoperable blockchain solutions for Identity and Access Management. The authors were able to create a PoC for illustrating the same. This system was able to complete the authentication and verification process within 1 second, and all the necessary functions created to test the feasibility was able to execute within 1 second. Also in this system authors have achieved full decentralization.
Employee Turnover Prediction System: With Special Reference to Apparel Industry in Sri Lanka

Apparel Industry in Sri Lanka faces the issue of high employee turnover with regard to operational level workers. Unlike other industries, garment industry is highly labor intensive, thus, turnover of employees can directly affect the productivity and sustainability of the organization. In a highly competitive environment; both local and global, it is important to focus on minimizing the turnover of these personnel as operational level workers are the main contributors of achieving the organizational production targets. Therefore, finding strategic solutions for employee turnover has become a key concern in this sector. This research focuses on analyzing reasons for labor turnover and design and develop a software solution that gives a score for each employee's turnover intention and the potential causes, along with a high-level suggestion to mitigate it, in order to improve employee retention, with a proactive approach.
Computer Vision Based Estimation of Shrimp Population Density and Size

Shrimp is one of the most important aquaculture products in Vietnam and it is desired to continuously estimate shrimp population density and size. This paper presents our initial steps in supervising aqua farming based on computer vision techniques. The computer vision system includes U-net segmentation, marker-controlled watershed segmentation, thresholding, contour tracing, etc. Experiment results show that the proposed method can obtain good results in counting shrimps (mean absolute error of 0.093) and estimating shrimp lengths (RMS error of 0.293 cm) when they are separately located.
Influence of FGM layer on the distribution of the electric field in DC cable joints insulations

This paper presents the results of a study regarding the influence of a Field Grading Material (FGM) on the electric field and temperature distributions in the insulations of a power cable joint model. The computation was performed at steady state for a cable joint insulation consisting of Ethylene Propylene Rubber (EPR) installed for a cable with Cross-linked Polyethylene (XLPE) insulation. The presence of the FGM layer leads to important modifications of the electric field values, especially near the corners of the cable joint inner semiconductive layer. The results also show that the maximum electric field and temperature values, as well as the surface charge density values present at the interface between the insulations are lowered by using a FGM layer. This leads to reduced electric stresses and charge injection. The reduction of the surface charge density values also reduces the values of the residual electric field and leads to a slowdown of insulation ageing.
A Neural-network-based Nonlinear Controller for Robot Manipulators with Gain-learning Ability and Output Constraints

This paper presents an adaptive robust controller for tracking control problems of robotic manipulators with output constraints. To realize the constrained objective, the controller is designed using a modified backstepping scheme. A neural-network model is employed to deal with uncertain nonlinearities and disturbances inside the system dynamics. To accomplish an asymptotically tracking performance under predefined constraints, both the control gains and the neural-network approximator are self-learnt with nonlinear laws. Effectiveness of the proposed controller is carefully verified by theoretical proofs and comparative simulation results.