Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Control of An Electric Wheel-foot Robotic System based on Stewart Structure*

This work approaches the control problem of wheel, foot and wheel-foot combined motion and driven of an electric wheel-foot robot based on the stewart structure. The autonomous control method is designed and the functions of each link are elaborated. The cooperative control of four wheels driven independently is studied to realize flexible wheel motion. The airframe smoothing control algorithm is studied to ensure the stability of the body during moving. The experimental results show that the electric parallel wheeled robot combines the advantages of wheel and foot motion. At the same time, it fully utilizes the multi-degree-of-freedom driving capability of the wheel-foot composite robot, which reflects the comprehensive advantages of motion.
Characterisation of FBG based Magnetic Field Sensor Response Sensitivity to Excitation Orientation for Rotating Electric Machine Applications

This paper reports an experimental study aimed at characterising the response of an in-situ fibre optic fibre Bragg grating / magnetostrictive flux sensor when exposed to excitation in orientations aligned with and perpendicular to its optimal performance axes. The study is aimed at improving the understanding of the potential for non-invasive in-situ application of this sensing technology in devices with confined geometries which impose restrictions on possible magnetic excitation axis alignment with that of the sensor, such as those encountered in rotating electric machinery.
An Adaptive Sampling Technique for Massive Data Collection in Distributed Sensor Networks

Wireless sensor networks are becoming very popular nowadays. Sensors in such networks are used to gather data periodically about a given zone area and send the collected data to the sink. However, such networks face several challenges especially the limited energy source and the data management. Hence, data sampling approach is becoming one of the essential techniques that saves he sensor energies and extend the network lifetime. Adaptive algorithms are created to allow each sensor to adapt its sampling rate to the application under surveillance, which leads to reduced data collection thus, reducing energy consumption. In this paper, we propose a new adaptive sampling technique that is dedicated to periodic sensor network applications. Our technique consists of two stages: aggregation and adapting stages. The first stage is applied at sensor level and aims to reduce the amount of data collected by the sensor. The second stage is applied at an intermediate level node called cluster-head (CH). The CH receives data periodically from the sensors and computes the new sampling rate for each sensor based on the spatio-temporal correlation between the sensors. Our technique is evaluated based on real sensor data collected in the Intel lab. The obtained results show the effectiveness of our technique in terms reducing the energy consumption while ensuring a high level of data accuracy and coverage network.
Data Conformity Evaluation: A Novel Approach for IoT Security

We consider the problem of attack detection for IoT networks based only on passively collected network parameters. For the first time in the literature, we develop a blind attack detection method based on data conformity evaluation. Network parameters collected passively, are converted to their conformity values through iterative projections on refined L1-norm tensor subspaces. We demonstrate our algorithmic development in a case study for a simulated star topology network. Type of attack, affected devices, as well as, attack time frame can be easily identified.
Multiple Hypothesis Semantic Mapping for Robust Data Association

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomous systems. This is particularly evident in urban scenarios with several similar-looking surroundings. Nevertheless, it requires the handling of a non-Gaussian and discrete random variable coming from object detectors. Previous methods facilitate semantic information for global localization and data association to reduce the instance ambiguity between the landmarks. However, many of these approaches do not deal with the creation of completely globally consistent representations of the environment and typically do not scale well. We utilize multiple hypothesis trees to derive a probabilistic data association for semantic measurements by means of position, instance, and class to create a semantic representation. We propose an optimized mapping method and make use of a pose graph to derive a novel semantic SLAM solution. Furthermore, we show that semantic covisibility graphs allow for a precise place recognition in urban environments. We verify our approach using real-world outdoor dataset and demonstrate an average drift reduction of 33% w.r. t. the raw odometry source. Moreover, our approach produces 55% less hypotheses on average than a regular multiple hypothesis approach.
AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition

Identification of the defective patterns of the wafer maps can provide insights for the quality control in the semiconductor wafer fabrication systems (SWFSs). In real SWFSs, the collected wafer maps are usually imbalanced from the defective types, which will result in misidentification. In this paper, a novel deep learning model called adaptive balancing generative adversarial network (AdaBalGAN) is proposed for the defective pattern recognition (DPR) of wafer maps with imbalanced data. In addition, a categorical generative adversarial network is improved to generate simulated wafer maps in high fidelity and classify the patterns with high accuracy for all defective categories. Taking consideration of the various learning abilities of the DPR model for different patterns into account, an adaptive generative controller is designed to balance the number of samples of each defective type according to the classification accuracy. The experiment results indicated that the proposed AdaBalGAN model outperforms conventional models with higher accuracy and stability for the DPR of wafer maps. Further results of comparative experiments revealed that the proposed adaptive generative mechanism can enhance and balance the recognition accuracy for all categories in the DPR of wafer maps.
Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Using Morphological Features

Lumen segmentation in intravascular optical coherence tomography (IVOCT) images is a fundamental work for more advanced plaque analysis, stent recognition, fractional flow reserve (FFR) assessment, and so on. However, the catheter, guide-wire, inadequate blood clearance, and other factors will impact on the accuracy of lumen segmentation. We present a simple and effective method for automatic lumen segmentation method in IVOCT based on morphological features. We use image enhancement, median filtering, image binarization, and morphological closing operation to reduce speckle noise, minimize the effect of blood artifacts and fill in small holes inside vascular walls. We extract the orientation and area-size of connected regions as morphological features in images and remove the catheter and guide-wire completely by morphological corrosion operation, small area-size region removal, and orientation morphological feature comparison, and then the contour of the lumen can be discriminated. The evaluation metrics of this method, the Dice index, Hausdorff distance, Jaccard index, and accuracy of 99.32%, 0.06 mm, 99.4%, and 99.66%, respectively, are obtained from comparing with expert annotations on 268 IVOCT images. Compared with the other morphology-based lumen segmentation methods, the presented method can remove the catheter and guide-wire completely, even if the catheter and guide-wire cling to the lumen or the shape of the catheter is irregular. Since only morphological operations are used to complete all processes, the calculation burden is reduced greatly.
Phasor-Based Adaptive Control of a Test-Feeder Distribution Network: Application of Retrospective Cost Adaptive Control to the IEEE 13-Node Test Feeder

Control in the power distribution system is needed to match the generation of electricity (that is, supply) with the demand for power (that is, load) at all times while maintaining a high level of power quality [1] (see "Summary"). The complexity of controlling the electric distribution system increases when distributed intermittent energy sources, such as solar photovoltaics (PVs), displace large-scale dispatchable generation [2]-[10]. If the supply from distributed resources exceeds the time-varying demand across a feeder, then the feeder-level voltage will increase. In extreme cases, large voltage increases will trigger the disconnection of distributed generation to protect electrical equipment and ensure customer safety. Consequently, coordinating renewable energy sources that are distributed throughout the distribution system-to match real-time demand across an entire power network-brings the additional challenge of maintaining a high level of supply reliability.
CASK: Conditional Authentication and Session Key Establishment In Fog-assisted Social IoT Network

The Fog-assisted Social Internet of Things (FSIoT) is an emergent paradigm that brought new opportunities in terms of service provisioning in the IoT-Fog network. Therefore, in such environment, the establishment of secure and trust communication scheme is a crucial need between peer IoT devices having similar social profiles. In this context, we propose a Conditional Authentication and Session Key establishment (CASK)-Algorithm for a fog-assisted Social IoT environment. The algorithm allows users profile matching before establishing the authenticated session key. CASK is performed using ECC and one_way hash function. Comparing to the existing works, our solution presents many advantages in terms of security aspects, efficiency, social-awareness and reduced computation overheads.
Efficient EEG Mobile Edge Computing and Optimal Resource Allocation for Smart Health Applications

In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission of big real-time health data is a challenge since the current dynamic networks are limited by different aspects such as the bandwidth, end-to-end delay, and transmission energy. Due to this, a data reduction technique should be applied to the data before being transmitted based on the resources of the network. In this paper, we integrate efficient data reduction with wireless networking transmission to enable an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. Convolutional Auto-encoder (CAE) approach was used to implement an adaptive compression/reconstruction technique with the minimum distortion. Then, a resource allocation framework was implemented to minimize the transmission energy along with the distortion of the reconstructed signal while considering different network and applications constraints. A comparison between the results of the resource allocation framework considering both CAE and Discrete wavelet transforms (DWT) was also captured.
Application of Magnetic Levitation Induced Weightlessness to Detect Cell Lineage

Identification and classification of bone marrow cells is an important step for molecular biology and therapeutic studies related to bone marrow disorders such as osteoporosis or obesity. In this study, we applied magnetic levitation technology to induce a weightlessness environment to detect adipocytes and osteoblasts based on their single cell density. This biotechnological method can be used for separation of heterogeneous populations such as bone marrow once adapted to a continuous microfluidic platform.
Automatic Detection of Everyday Social Behaviours and Environments from Verbatim Transcripts of Daily Conversations

Coding in social sciences is a process that involves the categorisation of qualitative or quantitative data in order to facilitate further analysis. Coding is usually a manual process that involves a lot of effort and time to produce codes with high validity and interrater reliability. Although automated methods for quantitative data analysis are largely used in social sciences, there are only a few attempts at automatically or semi-automatically coding the data collected in qualitative studies. To address this problem, in this work we propose an approach for automated coding of social behaviours and environments based on verbatim transcriptions of everyday conversations. To evaluate the approach, we analysed the transcripts from three datasets containing recordings of everyday conversations from: (1) young healthy adults (German transcriptions), (2) elderly healthy adults (German transcriptions), and (3) young healthy adults (English transcriptions). The results show that it is possible to automatically code the social behaviours and environments based on verbatim transcripts of the recorded conversations. This could reduce the time and effort researchers need to assign accurate codes to transcribed conversations.
Randomized Two-Timescale Hybrid Precoding for Downlink Multicell Massive MIMO Systems

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. Several precoding schemes have been proposed for massive MIMO but there still lacks a systematic solution to address all of the aforementioned issues especially for the multicell case. In this paper, we propose a randomized two-timescale hybrid precoding (RTHP) scheme for the downlink transmission of multicell massive MIMO systems. RTHP allows time sharing among multiple sets of analog precoders to achieve better tradeoff between sum throughput and fairness. Moreover, the time-sharing factors and analog precoders are adapted to the long-term channel statistics to mitigate inter-cell interference with reduced CSI overhead and RF chains. The digital precoder is used to support multiuser MIMO at each base station based on local low-dimensional effective CSI only. As such, RTHP resolves all of the aforementioned issues. We formulate the optimization of RTHP as a general network utility maximization problem (GNUMP), which is a two-timescale stochastic non-convex problem, and consequently challenging to solve. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop an online algorithm, which is shown to converge to stationary solutions of the approximate GNUMP. Finally, simulation results verify the advantages of the proposed scheme over baseline schemes.
Source-level instrumentation for in-system debug of high-level synthesis designs for FPGA

High-Level Synthesis (HLS) has emerged as a promising technology to reduce the time and complexity that is associated with the design of digital logic circuits. HLS tools are capable of allocating resources and scheduling operations from a software-like behavioral specification. In order to maintain the productivity promised by HLS, it is important that the designer can debug the system in the context of the high-level code. Currently, software simulations offer a quick and familiar method to target logic and syntax bugs, while software/hardware co-simulations are useful for synthesis verification. However, to analyze the behaviour of the circuit as it is running, the user is forced to understand waveforms from the synthesized design. Debugging a system as it is running requires inserting instrumentation circuitry that gathers data regarding the operation of the circuit, and a database that maps the record entries to the original high-level variables. Previous work has proposed adding this instrumentation at the Register Transfer Level (RTL) or in the high-level source code. Source-level instrumentation provides advantages in portability, transparency, and customization. However, previous work using source-level transformations has focused on the ability to expose signals for observation rather than the construction of the instrumentation itself, thereby limiting these advantages by requiring
Autonomous Patrol and Invader Detection by Coverage Controlled Quadcopters

This paper presents dynamic coverage control using multiple quadcopters for a surveillance of buildings or facilities. The surveillance system requires to move in the task area, detect and follow an invader. For observing an unknown invader, a fixed flight route cannot be chosen. In this paper, an autonomous surveillance method is proposed to detect an unknown invader considering the feature of the mounted sensor on a quadcopter. At the same time, a quadcopter follows an invader while patrolling in the task area. In order to verify the validity of the proposed method, the simulation is performed, and then the result of the simulation shows the effectiveness.
Energy Efficient for Scalable Video Caching Service over Device-to-Device Communication

Due to its advantages in service flexibility, scalable video coding (SVC) has been widely used in Device-to-Device (D2D) network communication, which is an effective network technology in the fifth generation (5G) communication network. However, duo to the limited capacity of mobile device, the uninterrupted transmission communication is difficult to be maintained, which needs to be solved urgently. Therefore, in this paper, we introduce the maximum offloading traffic and energy cost ratio to estimate service performance and propose a heuristic algorithm based on the greedy strategy to maximize the offloading traffic because of the NP-hardness of the cache placement problem for SVC. Then, we improve the energy efficiency over D2D link by optimizing the transmission power. Finally, a series of detailed simulation experiments are conducted to analyze the relationship between offloading traffic and energy cost, which demonstrates that a tradeoff exists between the significant amount of traffic and approving energy efficiency.
Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning

Constructing strong AI systems for video games is difficult due to enormous state and action spaces and the lack of good state evaluation functions and high-level action abstractions. In spite of recent research progress in popular video game genres such as Atari 2600 console games and multiplayer online battle arena (MOBA) games, to this day strong human players can still defeat the best AI systems in adversarial video games. In this paper, we propose to use a deep Convolutional Neural Network (CNN) to select among a limited set of abstract action choices in Real-Time Strategy (RTS) games, and to utilize the remaining computation time for game tree search to improve low-level tactics. The CNN is trained by supervised learning on game states labeled by Puppet Search, a strategic search algorithm that uses action abstractions. Replacing Puppet Search by a CNN frees up time that can be used for improving units? tactical behavior while executing the strategic plan. Experiments in the μRTS game show that the combined algorithm results in higher win-rates than either of its two independent components and other state-of-the-art μRTS agents. We then present a case-study that investigates how deep Reinforcement Learning (RL) can be used in modern video games, such as Total War: Warhammer, to improve tactical multi-agent AI modules. We use popular RL algorithms such as Deep-Q Networks (DQN) and Asynchronous Advantage-Actor Critic (A3C), basic network architectures and minimal hyper-parameter tuning to learn complex cooperative behaviors that defeat the highest difficulty built-in AI in mediumscale scenarios.
SURE-H: A Secure IoT Enabled Smart Home System

With the growing technology, the demand for smart things is drastically increased in daily-life. The IoT (Internet of Things) is one of the major components that provides facility to interact with IoT enabled devices. In this work, we propose a secure and efficient smart home system that enable to protect homes from theft or unusual activities and parallelly saves power. Our system is developed by exploiting the features of IoT that facilitates us to monitor an IoT enabled home from anywhere anytime over the Internet when data are stored in the cloud. This system uses a motion detector to detect a moving object from the environment where the system is deployed. The proposed system is evaluated using real-time deployment at KU campus considering 30 rooms for 60 days and found really useful in terms of safeness from any theft and saving power in comparison to existing systems.
Gait Analysis Using Foot-Mounted Inertial Sensors and Permanent Magnet

Two inertial measurement units and a permanent magnet are attached on two feet to estimate foot motion for gait analysis. Gait events are detected using a simple peak detection method with accelerometer data. To correct foot position error, the zero-velocity intervals, z-axis position, and magnetic measurement updates are used. The proposed solution gives accurate trajectories and also the relative position of two feet in straight walking experiments, which is simple, low-cost and promising for gait analysis applications.