Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Big Data Analysis System to Discover Associations between Genetic Variants and Diseases

Electronic Health Records (EHRs) and genomic data are fruitful resources to find associations between genetic variants and human diseases. Medical researchers have used Phenome-Wide Association Study (PheWAS) to find diseases relevant to genomic variants, and Genome-Wide Association Study (GWAS) to discover genomic variants related to specific diseases. In the era of Big data, the quantity of disease and genomic data have significantly increased, resulting in long processing and research time for Big data analysis. We designed and implemented a prototype of Medical Big Data Analysis System that finds the links between genetic variants and human disease with PheWAS and GWAS dataset of a population-based biobank in Marshfield Clinic Institute. The system consists of front-end Web-based graphic user interface using Shiny application, and back-end Big data analysis servers using Apache Spark and Mongo database. We evaluated the associations with selected variants and diseases and present the performance and CPU/memory overhead of the system while varying the number of executors in Spark cluster, which provides readers with multiple factors for deploying medical Big data analysis system.
System Revenue Maximization for Offloading Decisions in Mobile Edge Computing

Offloading decisions in mobile edge computing have been extended with multiple objectives, such as revenue maximization, energy conservation and latency reduction. Revenues of network/service operators, as the realistic and ultimate goal at intensive competitive markets, have not been thoroughly studied under a pricing scheme in combination with offloading decisions, especially with the aims of reducing and restricting energy consumption and latency. To bridge this important gap, this paper studies the revenue maximization of network operators through a pricing scheme in mobile edge computing, by explicitly formulating energy consumption and latency into the offloading strategy. A two-stage game-theory framework based on the Stackelberg game is established, through which the optimal price for both the network operator and the customer can be reached. The offloading data size can be dynamically adjusted according to the agreed price. The existence of equilibrium in the Stackelberg game is proved, and experiments are conducted to verify the effectiveness of our proposed model.
Color-based Superpixel Semantic Segmentation for Fire Data Annotation

Image-based fire detection is a safety-critical task, which requires high-quality datasets to ensure performance guarantees in real scenarios. Automatic fire detection systems are in ever-increasing demand, but the limited number and size of open datasets, and lack of annotations, hinder model development. Solving this issue requires that experts dedicate a significant time to classify and segment fire events in image datasets. Towards building large-scale curated datasets, this paper presents a data annotation method that leverages semantic segmentation based on superpixel aggregation and color features. The approach introduces interpretable linguistic models that generate pixel-wise fire segmentation and annotations, which are explainable through simple fine-tunable rules that can support subsequent annotation validation by fire domain experts. The performance of the proposed algorithm is evaluated for relevant scenarios using a publicly available dataset, namely through the assessment of the segmentation quality and the labeling of fire color categories. The outcomes of this approach pave the way for creating large-scale datasets that can empower future deployments of learning-based architectures in fire detection systems.
Social-Aware Load Balancing System for Crowds in Cellular Networks

Over time, load balancing systems in cellular networks have been key to avoiding overload problems in the network and to maintaining a correct resource allocation and performance. However, the classical approaches were not designed for the dynamism generated by user behavior. The big crowds of users at certain social venues are one of the main concerns of mobile operators due to the load imbalance generated. Additionally, the mobility of users who attend social events (e.g., sports events, concerts, etc.) greatly impacts network performance due to its high correlation with network traffic. The availability to inform about events, particularly regarding venue location (e.g., stadiums, concert halls, convention centers) is exponentially growing thanks to its proliferation in social networks through geolocation databases and other functionalities. Therefore, the present work proposes a novel load balancing system integrating a fuzzy logic controller algorithm with social-awareness, which considers the relative position between cell sites and the social event venue in order to configure the network parameters. This approach is evaluated for different configurations of load balancing methods simulated on an urban macro scenario, mitigating the impact of the number of users per cell without degrading the signal quality. In this way, results show that social event data information plus soft or aggressive transmission power changes in cells can help to maintain the balance in the number of users per cell during mass events.
Genetic Algorithm Based Parameter Tuning for Robust Control of Launch Vehicle in Atmospheric Flight

A hybrid approach to the design of the attitude control system for a launch vehicle (LV) in the atmospheric flight phase is proposed in this paper, where a structured <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> controller is tuned using a genetic algorithm (GA). The <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> synthesis relies on a classical architecture for the thrust vector control (TVC) system that features proportional-derivative loops and bending filters. Once a set of requirements on stability and robustness typical of industrial practice is specified, control design is carried out by parameterizing the <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> weighting functions, and solving a two-layer max-min global optimization problem for the tuning parameters. The design methodology is applied to the model of a medium-size LV. The novel design is analyzed in off-nominal conditions taking into consideration model parameter scattering and wind disturbances. The results show that the automated design procedure allows to devise time-scheduled controllers providing adequate stability and performance, and appears as a viable and effective solution in order to reduce the burden of recurrent activities for controller tuning and validation conducted prior to each launch.
A Fast and Precise Time-Based Characterization Method for Sensors

This paper describes a fast and precise time-based characterization method for sensors without the need for stabilization of environmental parameters by using the time logging information of the acquired measurement data. A piezoresistive MEMS pressure sensor is presented to demonstrate the effectiveness of the time-based characterization method. Experimental results indicate that the novel characterization method reduces the characterization time by 66 times and increases the number of data samples by 5 times compared to a conventional method. The mismatch between the time-based and the conventional methods is very small, 0.028% full scale (FS) and 0.019% FS in terms of sensitivity and offset of the pressure sensor, respectively, after correction for errors on trigger time.
A Novel Sensor-Based Human Activity Recognition Method Based on Hybrid Feature Selection and Combinational Optimization

In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies. Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system. However, most of the previous research focused on one of them ignoring their interactions. Very few studies focused on these two aspects simultaneously. Considering the two factors separately may lead to inferior HAR performance. This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance. A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed. GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods. It consists of two phases, namely pre-selection phase and re-selection phase. Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method. The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier. The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.
A Scalable BP Method for Joint Localization and Synchronization in Dense Wireless Sensor Networks

In this paper, we develop a joint cooperative localization and synchronization scheme for dense mobile wireless sensor networks (WSNs) using a scalable belief propagation (BP) based method. We consider a distributed time-varying WSN with mobile devices, where message packets are propagated among all devices starting from temporal and spatial anchors. To account for the nonlinear system models and to compute the belief at each device while maintaining low communication and computation complexity, we propose an efficient scalable BP scheme, where a temporary posterior belief is calculated and updated sequentially so that the dimension of measurement covariance matrices is fixed instead of the unlimited dimension augmentation and batch computation in sigma point belief propagation (SPBP). Simulation results demonstrate a significant enhancement on the robustness of the algorithm and reduction of the computational complexity compared to the baseline scheme.
Impact of Chuck Boundary Conditions on Wideband On-Wafer Measurements

While a lot of investigations have been presented recently explaining the parasitic effects in on-wafer measurements caused by probes, neighborhood and environmental effects, this paper addresses the impact of chuck boundary conditions. Starting from a coplanar waveguide (CPW) measurement example, this paper demonstrates how the most relevant chuck parameters, i.e. thickness and relative permittivity, deteriorate the S-parameters of CPWs.
Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks

In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs). In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength). Second, quality of service (QoS) which is used to measure the network’s performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-Q-Networks (DQN). Additionally, we compare the performance of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions.We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs.
Design and Analysis of mmWave Full-Duplex Integrated Access and Backhaul Networks

The full-duplex (FD) integrated access and backhaul (IAB) technique is considered in the 3rd Generation Partnership Project (3GPP) release 16 to address the demand for high capacity, large coverage and low latency millimeter wave (mmWave) beyond 5G networks. However, the self-interference (SI) appears on the FD-IAB-node, becoming the major bottleneck in the development of these novel networks. With the assumption of successful antenna isolation, this paper proposes an SI cancellation (SIC) technique for the wideband mmWave-FD-IAB multiuser networks with the cost-efficient subarray hybrid precoding structure. The fiber Bragg grating-based analog canceler is established between the RF chain pairs of the FD-IAB-node, and the minimum mean square error baseband combiner is used at the FD-IAB-node receiver to cancel the residual SI digitally. Further, hardware impairment (HWI) is considered to capture the transceiver distortion. Simulation results show that the proposed FD-IAB network capacity is almost doubled compared to the half-duplex-IAB system when the effect of HWI is low.
Adaptive Controller for Induction Machine Direct Torque Control

In this article is proposed a direct torque control of induction motor with additional control signal from adaptive controller. It‘s signal is summed with the those from the conventional PI regulator and the result is the reference torque signal. Using the created mathematical model, the modes of operation of an induction electric drive with adaptive control under various disturbing influences are investigated.
Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

Power outages can disrupt daily domestic activities as well as the economy as operations are hampered when they occur. They can decrease work productivity by delaying operations that require electricity. The key solution to this problem is to ensure that there are fewer or no power interruptions. This can be achieved by ensuring secure and continuous network operations through regular maintenance and inspection. However, the traditional inspection technique of foot patrol is risky, laborious, and time-consuming. A preferable contemporary technique uses an unmanned aerial vehicle (UAV) for inspecting distribution lines. Detecting power lines are crucial for real-time motion planning and navigation of UAVs. Previous techniques that depend on conventional filters and gradients may fail to detect power lines because of noisy backgrounds. Thus, this study proposes a novel technique by adopting the Transfer Learning approach. The process involves re-training the Point Instance Network (a road lane detection model) with images for power line detection. The proposed method extends the PINet model by adding a comparator for rotation block before it and a postprocessing block after it. This study generates four versions of the model, each of which was trained on one of the following datasets (i) self-gathered images captured by a handheld camera, (ii) a drone, (iii) publicly accessible images from the Power Line Dataset of Mountain Scene (PLDM), and (iv) Power Line Dataset of Urban Scene (PLDU). Experimental results on each dataset confirm the feasibility of the proposed approach.
Joint Freshness and Channel Aware Scheduling for Multi-User Wireless Communications

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information in Industrial Internet of Things (IIoT). To improve the information freshness over a fading channel shared by multiple users, efficient scheduling methods are highly desired for IIoT. In this paper, we are interested in a cross-layer scheduling policy, in which power and rate-adaptive transmissions are enabled in the physical layer. More specifically, we adopt a probabilistic scheduling method to minimize AoI while satisfying an average power constraint. The optimization of the probabilistic scheduling policy is formulated as a Constrained Markov Decision Process (CMDP), the dimension of which grows exponentially with the number of users and the buffer size. To overcome the prohibitive computational cost, we reveal the threshold-based structure of the AoI-optimal scheduling. Then we conceive a low complexity iterative algorithm that gives us the optimal freshness-aware scheduling policy.
Unsupervised Semantic Mapping for Healthcare Data Storage Schema

Data, information, and knowledge processing systems, in the domain of healthcare, are currently plagued by heterogeneity at various levels. Current solutions have focused on developing a standard-based, manual intervention mechanism, which requires a large number of human resources and necessitates the realignment of existing systems. State-of-the-art methodologies in the field of natural language processing and machine learning can help to partially automate this process, reducing the resource requirements and providing a relatively good multi-class-based classification algorithm. We present a novel methodology for bridging the gap between various healthcare data management solutions by leveraging the strength of transformer-based machine learning models, to create mappings between the data elements. Additionally, the annotated data, collected against five medical schemas and labeled by four annotators is made available for helping future researchers. Our results indicate, that for biased, dependent multi-class text classification, transformer-based models provide better results than linguistic and other classical models. In particular, the Robustly Optimized BERT Pretraining Approach (RoBERTa) provides the best schema matching performance by achieving a Cohen’s kappa score of 0.47 and Matthews Correlation Coefficient (MCC) score of 0.48, with human-annotated data.
FlyBeam: Echo State Learning for Joint Flight and Beamforming Control in Wireless UAV Networks

This paper aims at designing high-data-rate swarm UAV networks with distributed beamforming capabilities. The primary challenge is that the beamforming gain in swarm UAV networks is highly affected by the UAVs’ flight altitude, their movements and the resulting intermittent link blockages, as well as the availability of channel state information (CSI) at individual UAVs. To address this challenge, we propose FlyBeam, a learning- based framework for joint flight and beamforming control in swarm UAV networks. We first present a mathematical formulation of the control problem with the objective of maximizing the throughput of swarm UAV networks by jointly controlling the flight and distributed beamforming of UAVs. Then, a distributed solution algorithm is designed based on a combination of Echo State Network learning and online reinforcement learning. The former is adopted to approximate the utility function for individual UAVs based on online measurements, by jointly considering the unknown blockage dynamics and other factors that affect the beamforming gain. The latter is used to guide the exploitation and exploration in FlyBeam. The effectiveness of FlyBeam is evaluated through an extensive simulation campaign. Results indicate that significant (up to 450%) beamforming gain can be achieved by FlyBeam. We also investigate the effects of blockages and UAV flight altitude on the beamforming gain. It is found that, which is somewhat surprising, higher (rather than lower) beamforming gain can be achieved by FlyBeam with denser blockages in swarm UAV networks.
IRS-Assisted High-Speed Train Communications: Outage Probability Minimization with Statistical CSI

This paper considers an intelligent reconfigurable surface (IRS) assisted high-speed train communication system to address blockage issues, thus enhancing the spectrum efficiency and reducing the outage probability. However, instantaneous channel state information (CSI) at the base station is hard to be acquired in high-speed railway communication scenarios. To deal with the channel uncertainty, the outage performance is investigated by exploiting the statistical CSI in the downlink multiple-input multiple-output system, and we derive a closed-form expression of the outage probability. Moreover, an outage probability minimization problem is formulated to facilitate the IRS phase shift design and transceiver beam-forming design at the BS and the mobile relay on the train, subject to the transmit power constraint. Simulation results validate the efficacy of the proposed algorithm in terms of outage probability and effective rate, which is in close agreement with the theoretical analysis. Besides, the impacts of critical system parameters including the transmit power and the signal-to-noise ratio (SNR) threshold on the system performance are presented.
A Comprehensive Analysis of the Impact of Selecting the Training Set Elements on the Correctness of Classification for Highly Variable Ecological Data

Classification of objects in empirical data, especially in biological sciences, is a very complex process and has been a big challenge for researchers who do not specialize in data analysis. Therefore, in this study, we present a comprehensive summary of selected classifiers operating on both exact and fuzzy numbers. The results of performance of specific classifiers are compared on the example of a unique set of empirical data on changes in the behavior of animals in response to environmental factors. This is one of the key challenges in ecological research and it is strictly related to ecosystem changes caused by climate change. Nowadays, changes in behavior are a very popular topic of research because as a result of the COVID-19 pandemic and lower activity of people (lockdown effect). Therefore, various unusual reactions of wild animals were found around the world. A detailed compilation of research results, shortcomings, and strengths of various classification methods may be a compendium of knowledge for biologists and other practitioners as well as researchers working with empirical data.