Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Routing for Beamforming-Constrained Swarm UAS Networking

With the evolution of 5G cellular communication, beamforming is mature for the implementation on a large scale. The development of cellular networking provides a great opportunity for swarm UAS. Concurrently, the advantages of swarm UAS can provide immense improvement to the advance of industrial and residential implementations. However, the nature of the antenna array constrains beamforming in a limited space which is rarely mentioned in networking routing researches. In this paper, regarding the constrained steering space, we proposed a novel algorithm, Optimized Ad-hoc On-demand Distance Vector (OAODV), which aims to improve the capacity of beamforming on swarm UAS networking. With the adjustable searching space, OAODV can achieve better latency, overhead, and link generation than the conventional algorithms of Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State Routing (OLSR). Compared with AODV and OLSR, OAODV can reduce 35.07% and 68.93% of average overhead, and decrease 47.73% and 11.55% of average latency respectively. Further, we leverage Ant Colony Optimization (ACO) to enhance OAODV, and the ACO enabled OAODV can achieve better throughput and fewer hops with reduction of overhead. The proposed algorithms show the promising capacity to improve swarm UAS networking. The OAODV is more suitable for the 5G cellular networking based swarm UAS networking. The decentralized advantages can improve the deployment of swarm UAS networking on a large scale with more efficiency, flexibility and elasticity.
Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network

Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs.
A current-integrated differential NAND-structured PUF for stable and V/T variation-tolerant low-cost IoT security

Physically unclonable functions (PUFs) have drawn attention as one of the key security primitives for IoT devices since they can provide unique, random, stable, and unclonable hardware fingerprints, ie. keys, for secure authentication. Numerous PUF designs have been evolved to provide stable keys at a lower cost, ie. with small area. Early SRAM-based PUFs [1, 2] had large area $(\gt9,000{\mathrm {F^{2}/bit}})$ and was unstable with a native BER of 8.3*. Various stabilization techniques were proposed such as temporal majority voting (TMV), burn-in, and dark bit masking, at the cost of large additional peripheral circuits and significant loss of challenge-response-pairs (CRPs). But resulting BER was still very high ($\sim$ 1*) even with 8.6x BER improvement. An inverting logic gate cell-based PUF with high reliability [3] and synthesizable PUF [4] were also proposed but the large area (5,057F2/bit,2,686F2/bit) limited cost efficiency. A 2-T amplifer-based PUF achieved 553-782F2/bit area [5, 6] but the initial version had a high BER due to the lack of a stabilization method [5]. Therefore, in the following work [6], a stabilization method was subsequently proposed and BER could be significantly improved. A leakage-based PUF with a lossless stabilization method called ‘remapping’ was proposed in [7], which allowed stabilization without sacrificing CRPs as required in [1–3, 6]. To further improve cost efficiency, a PUF with a differential NAND structure [8] was proposed with an extremely small area of $20{\mathrm {F^{2}/bit}}$. However, there was no means of stabilization applicable, other than TMV, making it unstable and susceptible to voltage or temperature (V/T) variations. Therefore, in this paper, a current-integrated differential NAND-structured PUF (CI NAND-PUF) is proposed, where stabilization methods other than TMV is made applicable. By applying trimming and remapping, up to 12.8x stability improvement is achieved with extremely cost-efficient PUF with 20F2/bit area, while also significantly reducing peripheral circuit overhead on [8].
Efficient Multi-Maneuver Platooning Framework for Autonomous Vehicles on Multi-Lane Highways

Recently, autopilot-like vehicles have become more pervasive. To maintain inter-vehicle distance stably, Cooperative Adaptive Cruise Control (CACC) is then proposed to make each autonomous vehicle exchange its dynamic state with neighboring vehicles via vehicle-to-vehicle (V2V) communication. However, longitudinal platooning via CACC systems alone is not enough to improve traffic throughput since each vehicle has its own desired speed and only considers itself to optimize its traveling. Therefore, we develop a novel framework MANA to determine the suitable platoon-merge maneuver, lane-change maneuver, and space-reserve maneuver. Extensive simulation results manifest that MANA can avoid collisions effectively, converge fast, and save fuel consumption and CO2 emissions by 24% and 20%.
HRRP Target Recognition Based On Soft-Boundary Deep SVDD With LSTM

Radar high-resolution range profile (HRRP) target recognition is an active part of radar target recognition (ATR). The current radar HRRP target data has the characteristics of less data volume. At the same time, support vector data description has limited ability to extract the deep features of the signal. To address this issue, we propose a soft-boundary Deep SVDD with LSTM (long short-term memory). The framework consists of an autoencoder, an LSTM neural network layer, and an SVDD hyper-sphere. The autoencoder generates the deep signal features, and the LSTM layer can extract the time-related features. The distance from the feature point to the center of the hyper-sphere is the classification judgment condition. The neural network parameters and the hyper-sphere are trained to be the optimal value. We carry out experiments on a dataset with a small volume. The result and the Received operation characteristic (ROC) curve show that the classifier has good performance. The area under ROC (AUC) value is close to 87%–94%.
A Time-Efficient and Attention-Aware Deployment Strategy for UAV Networks Driven by Deep Reinforcement Learning

Collaborative unmanned aerial vehicle (UAV) networking has the characteristics of flexibility, efficiency, ubiquity, etc., which can enhance wireless network coverage and improve the quality of service to ground users. However, collaborative UAV networking has main challenges such as location deployment and energy optimization. For collaborative networking scenarios, it is necessary to focus on solving key issues such as algorithm convergence and time complexity. In response to the above problems, we propose to leverage hybrid deep reinforcement learning (DRL) with spatial attention mechanism to reduce time complexity according to actual application requirements of location deployment. Firstly, the convolutional neural network is used to improve the ability of discriminating state features. Then, an improved spatial attention mechanism is introduced to apply different weights to state features of different spatial locations, and focuses on the state features that are favorable for UAV deployment in spatial locations. Finally, the offline training state attention model is added to the state input of hybrid deep reinforcement learning for adaptive deployment training. The simulation results show that the training time of the algorithm can be greatly reduced while both mean opinion score (MOS) and energy consumption performance are reduced by about 10%.
31.4: A 3.4Gbps/lane Low Overhead Clock Embedded Intra‐panel Interface for High Resolution and Large‐Sized TFT‐LCDs

This paper proposes a 3.4Gbps/lane intra‐panel interface with 11.1% of the protocol overhead for the raw data to be transmitted. The proposed intra‐panel interface uses a point‐to‐point interface architecture with embedded clock. To reduce the EMI radiation, the scrambling scheme was adopted. The protocol of the proposed intra‐panel interface provides a PLL based clock and data recovery (CDR) scheme for the receiver. Timing controller (TCON) and source driver (SD) are implemented using 45nm/1.1V and 0.18um/1.8V CMOS processes, respectively. The proposed interface is verified on a 55‐inch Full‐HD TFT‐LCD panel with 8bit RGB and 120‐Hz frame rates. The maximum data rate per lane was measured as up to 3.4Gbps/lane.
A Secure and Privacy Preserving Incentive Mechainism for Vehicular Crowdsensing with Data Quality Assurance

With the development of communication and Internet of Vehicles (IoV) technology, a large number of high precision sensors and computing units are widely used and deployed on vehicles. With the vehicles work as users, Mobile Crowdsensing (MCS) system has a broad application prospect in traffic planning, environmental monitoring and so on. The realization of these applications needs a large amount of data. However, in the process of crowdsensing, users are often faced with the consumption of computing, communication and energy and the risk of privacy leakage, so they are reluctant to actively participate. Therefore, we need to design a safe and reasonable incentive mechanism. In this paper, we focus on privacy protection and user incentive, and propose a framework of the vehicular crowdsensing with blockchain, as well as the smart contacts deployed on the blockchain. The characteristics of the blockchain are used to solve the security problems in the crowdsensing process. In addition, we propose a reverse auction-based incentive mechanism. A group of users with the highest reputation value are selected to complete the sensing tasks, and the payoffs are assigned according to the quality of the sensing data uploaded by the selected users. Finally, Matlab-based simulation verifies the effectiveness of the incentive mechanism proposed in this paper.
Optimization of a magnetic structure for wireless power transfer in charge while driving

The electromagnetic design of a wireless power transfer system focuses on the development of coupled inductors. Moreover, the system performance can be improved by a auxiliary magnetic structure. This paper deals with the design of a novel magnetic structure for a light commercial vehicle in charge while driving. The design is carried out through an optimization procedure which aims to solve both EMC and exposure problems
ZCS-ZVS bi-directional phase-shifted DC-DC converter with extended load range

The work described is based on a bidirectional phase-shifted DC-DC converter that conventionally suffers from the restricted load range and nonlinear control of the output voltage. The concepts developed are used to extend the load range of the converter by applying both zero current switching (ZCS) and zero voltage switching (ZVS). In addition, the control to output voltage characteristics of the converter is improved to be more linear over the large load range.
DW-FBCSP: EEG emotion recognition algorithm based on scale distance weighted optimization

Emotion calibration is measured by the valence and arousal scales and the ideal center is used to directly divide valence arousal into high scores and low scores. This division method has a big classification and labeling defect, and the influence of emotion stimulation material on the subjects cannot be accurately measured. To address this problem, this paper proposes an EEG emotion recognition algorithm (DW-FBCSP: Distance Weighted Filter Bank Common Spatial Pattern) based on scale distance weighted optimization to optimize the classification according to the distance of the scores from ideal center. This method is a natural extension of CSP that optimize the user’s EEG signal projection matrix. Then, the LDA classifier is used to recognize emotions using the features set which fused the selected features and the features extracted by the projection matrix. The results show that the mean correct rate of the valence and arousal achieves 81.14% and 84.45% using the DEAP dataset. The results demonstrate that our proposed method outperforms better than some other results published in recent years.
Attacking the damage identification problem

A review of the evolution of the field of damage identification is presented. Trends of research and the present state of the field are discussed and directions of future research are postulated. Ideally, an automated damage identification method incorporated into the smart structure scheme would be able to detect damage as it is incurred by the structure, determine the location and extent of the damage, predict when and if catastrophic failure of the structure will occur, and alert the operator as to how the performance of the structure is affected in order for appropriate steps to be made to remedy the situation. Obviously, this is no easy task but it is essential that it is clearly defined how the research fits into the ultimate goal of developing an automated, noninvasive damage identification method. In attempting to quantify changes in response characteristics due to damage on a structure, it is very important to be aware of the inherent variabilities one might encounter in acquiring these response characteristics. These variabilities may come from computational algorithms, sensor error, or environmental effects. A method which assumes a priori ignorance to the manifestations of damage in the response characteristics of the structure is presented. This method uses inductive learning to statistically isolate changes in response characteristics due to damage from those due to the inherent variabilities. In order to validate the method, an example is presented which identifies the existence and location of a small test mass on an aluminum plate via the measurement of the structural impedance-response of the plate.
Topology Inference for Consensus-based Cooperation under Time-invariant Latent Input

Topology inference is a critical problem in multiagent systems. Knowledge of the topology helps to better understand the system behavior, e.g., predicting states of a multivehicle system. Different from most prior works, this paper is dedicated to inferring the directed network topology from the time-series observations of the system, which is driven by the unmeasurable latent input. Independent of the system state, the latent input is designed by the system user or injected by external excitations. To decouple its influence from the observations, an input filtering-based topology inference algorithm (IF-TIA) is proposed. Specifically, we demonstrate that the influence of the time-invariant input on the system state evolution is separable, and transform the goal into solving an optimization problem based on the separability. The novel algorithm significantly improves the inference performance by reformulating the empirical risk, where the decreasing weight setting is adopted for the cost terms in the objective function. Compared with the benchmark methods, extensive simulations illustrate the outperformance of the IF-TIA method in terms of both the topology structure and the edge weight.
Takt Time Grouping: implementing kanban-flow manufacturing in an unbalanced, high variation cycle-time process with moving constraints

One-piece flow and kanban/pull methods have been used to reduce work-in-process (WIP) and flowtime in manufacturing flow processes; however, these methods have limitations. For example, one-piece flow does not work well when there are relatively large set-up times required between different components. One-piece flow also requires operations to be well balanced with a minimum of variability in processing times at each operation. Unfortunately, these conditions often do not exist. The theory of constraints drum-buffer-rope (DBR) method is designed for unbalanced processes, and it has been shown to be effective for products with large operation time variation. However, DBR does not generally optimise flowtime and cannot handle a process with moving constraints (bottlenecks). We have developed a method called Takt Time Grouping (TTG) for implementing kanban-flow manufacturing, when one-piece flow or DBR do not provide good results. TTG combines one-piece flow manufacturing, transfer-batch sizing and DBR concepts through the use of a constraints-based transfer-batch sizing formula. Using a discrete event simulation model, it is shown that TTG increases throughput rate as compared to one-piece flow, CONWIP and DBR approaches, with much lower WIP inventory and faster flowtime than CONWIP and DBR.
Some Capabilities of Android OS for Distributed Computing

Most of the algorithms in Computer Science are linear algorithms. This means that each subsequent instruction directly depends on the calculations of the previous instructions. In this type of linear algorithm, the sequence of calculations does not allow instructions to be calculated simultaneously on different processors. A much smaller subset of algorithms in Computer Science allows individual pieces of data to be processed in parallel. In turn, a subset of parallel algorithms allows implementation in the form of distributed computing. A very bright example of this is the population heuristics for global optimization and machine learning algorithms. Modern mobile devices, and especially Android OS based, are giving unlimited capabilities for distributed computing. In this study, some of these capabilities are discussed.
Joint Transmit Beamforming and Antenna Selection Design for MISO-NOMA Systems Based on Improper Gaussian Signaling

In this paper, we consider the joint antenna selection (AS) and beamforming (BF) vector design problem for a downlink two-user power-domain non-orthogonal multiple access (PD-NOMA) system considering improper Gaussian signaling (IGS). We formulate an optimization problem concerned about the max-min rate between users with hardware constraints such as the total power constraint (TPC), per-antenna power constraint (PAPC), and the number of activated antennas along with the conditions for successful successive interference cancellation (SIC) that are proved in existing works. To deal with the log-determinant objective function and binary optimization, we propose a solution that combines successive concave quadratic programming (SCQP) and the exact penalty approach to obtain suboptimal results by iterations. As shown in simulation results, IGS can prevail over its proper Gaussian signaling (PGS) counterpart in terms of achievable sum rate (ASR) particularly in high transmit signal-to-noise ratio (SNR) scenarios.
Predicting Synthetic Lethality in Human Cancers via Multi-Graph Ensemble Neural Network

Synthetic lethality (SL) is currently one of the most effective methods to identify new drugs for cancer treatment. It means that simultaneous inactivation target of two non-lethal genes will cause cell death, but loss of either will not. However, detecting SL pair is challenging due to the experimental costs. Artificial intelligence (AI) is a low-cost way to predict the potential SL relation between two genes. In this paper, a new Multi-Graph Ensemble (MGE) network structure combining graph neural network and existing knowledge about genes is proposed to predict SL pairs, which integrates the embedding of each feature with different neural networks to predict if a pair of genes have SL relation. It has a higher prediction performance compared with existing SL prediction methods. Also, with the integration of other biological knowledge, it has the potential of interpretability.
Collective Perception Messages: New Low Complexity Fusion and V2X Connectivity Analysis

Connected vehicles are equipped with sensors that can detect surrounding objects. However, vehicles perception is limited by sensors range or by the presence of other obstacles. Collective perception can improve vehicles perception by allowing them to exchange information about detected obstacles. In this context, vehicles rely on on-board sensors in order to generate Collective Perception Messages (CPM) that are exchanged by means of LTE-V2X connectivity. In order to reveal hidden obstacles and obtain a coherent visualization about the environment, CPM fusion is then crucial. In this work, we propose a novel low complexity fusion algorithm for CPM. Moreover, we evaluate the impact of LTE-V2X connectivity performance, specially in terms of packets loss, on the fusion. Simulation results in a smart junction demonstrate the relevance and efficiency of our algorithm in terms of obstacles detection capabilities.
Recommending Research Articles: A Multi-Level Chronological Learning-Based Approach using Unsupervised Keyphrase Extraction and Lexical Similarity Calculation

A research article recommendation approach aims to recommend appropriate research articles to analogous researchers to help them better grasp a new topic in a particular research area. Due to the accessibility of research articles on the web, it is tedious to recommend a relevant article to a researcher who strives to understand a particular article. Most of the existing approaches for recommending research articles are metadata-based, citation-based, bibliographic coupling-based, content-based, and collaborative filtering-based. They require a large amount of data and do not recommend reference articles to the researcher who wants to understand a particular article going through the reference articles of that particular article. Therefore, an approach that can recommend reference articles for a given article is needed. In this paper, a new multi-level chronological learning-based approach is proposed for recommending research articles to understand the topics/concepts of an article in detail. The proposed method utilizes the TeKET keyphrase extraction technique, among other unsupervised techniques, which performs better in extracting keyphrases from the articles. Cosine and Jaccard similarity measures are employed to calculate the similarity between the parent article and its reference articles using the extracted keyphrases. The cosine similarity measure outperforms the Jaccard similarity measure for finding and recommending relevant articles to understand a particular article. The performance of the recommendation approach seems satisfactory, with an NDCG value of 0.87. The proposed approach can play an essential role alongside other existing approaches to recommend research articles.