Miracle-dz/UniFAR-FTUs huggingface dataset:
Dielectric patch resonator and antenna

This paper presents an overview of dielectric patch (DP) antennas developed in recent years. The employed DP resonator composed of a DP and a bottom substrate is analyzed comprehensively here, enabling the easy realization of a quasi-planar DP antenna. It combines the dual advantages of the conventional microstrip patch (MP) antenna and dielectric resonator (DR) antenna in terms of profile, gain, bandwidth, radiation efficiency, and design freedom. Furthermore, the DP antenna inherits the multi-mode characteristic of the DR antenna, thus it has a large number of high-order modes, including TMmn mode and TEmn mode. The high-order modes are widely applied, for example, by combining with the dominantTMio mode to expand the bandwidth, or selecting multiple higher-order modes to implement a high-gain antenna. Additionally, the non-radiation high-order modes are also utilized to produce natural radiation null in filtering antenna design. In this paper, the design theories and techniques of DP antenna are introduced and investigated, including calculation and control methods of the resonant mode frequencies, analysis of the radiation mechanism, and applications of the multi-mode characteristic. This overview could provide guidance for the subsequent antenna design, thus effectively avoid time-consuming optimization.
Deep Kernel Method for Dynamic MRI Reconstruction

We introduce a deep kernel model for the recovery of real-time dynamic MRI from highly undersampled measurements. The proposed scheme uses the cascade of two deep convolutional neural networks (CNN) for the kernel representation of images. Unlike the supervised CNN approaches for image reconstructions that require extensive fully-sampled training data for learning the network, the parameters of the two CNNs in the proposed method are learned from the undersampled measurements directly in this work. The main benefits of the proposed scheme are (a) the elimination of the empirical choice of the feature map and kernel function in the kernel method, and (b) the unsupervised nature of the proposed framework.
Content prefetching method to utilize reinforcement learning-based routing for in-network caching

In-network caching is a technology that allows routers in a network to cache contents, enabling users to download the contents from the routers in addition to content servers. As one of routing methods enhancing the performance of in-network caching environments, a multi-armed bandit-based routing (MAB-routing) has been proposed in the past. To further enhance the performance of MAB routing, in this paper, we propose a content prefetching method. In the proposed method, each router periodically retrieves contents according to their popularity, and then notifies its adjacent routers of caching information to efficiently utilize MAB-routing. Through simulation experiments, we show the effectiveness of the proposed method.
Towards Trajectory Prediction-Based UAV Deployment in Smart Transportation Systems

$A$ smart transportation system (i.e., intelligent transportation system) refers to a transportation critical infrastructure system that integrates advanced technologies (e.g., networking, distributed computing, big data analytics, etc.) to improve the efficiency, safety, and sustainability of the transportation system. However, the rapid increase in the number of vehicles on roads and significant fluctuations in the flow of traffic can cause the coverage holes of Road Side Units (RSUs) and local traffic overload in smart transportation systems, which can negatively affect the performance of systems and causes accidents. To address these issues, deploying Unmanned Aerial Vehicles (UAVs) as mobile RSUs is a viable approach. Nonetheless, how to deploy UAVs to the optimal position in the smart transportation system remains an unsolved issue. This paper proposes a Vehicle Trajectory-based Dynamic UAV Deployment Algorithm (VTUDA). The VTUDA utilizes vehicle trajectory prediction information to improve the efficiency of UAV deployment. First, we deploy a distributed Seq2Seq-GRU model to the UAVs and train the model. We leverage the well-trained model to predict vehicle trajectory. VTUDA then uses the predicted information to make informed decisions on the optimal location to position the UAVs. Further-more, VTUDA considers both the condition of communication channels and energy consumption during the deployment process to ensure that UAVs are deployed to optimal positions. Our experimental results confirm that the proposed VTUDA can effectively improve the deployment of UAVs. The experimental results also demonstrate that VTUDA can significantly enhance vehicle access and communication quality between vehicles and UAVs.
E-App: Adaptive mmWave Access Point Planning with Environmental Awareness in Wireless LANs

To enable ultra-high throughputs while addressing the potential blockage problem, maintaining an adaptive access point (AP) planning is critical to mmWave networking. By investigating the hidden interaction between the environment map and the placement of mmWave APs, we develop an adaptive AP planning (E-app) approach that can accurately sense the environment dynamics, reconstruct the obstacle map, and then predict the placements of mmWave APs adaptively. Specifically, our solution leverages mmWave radio itself to sniff the unacceptable performance degradation through sensing only a small fraction of observation points that are identified by a sparsity-aware analytical model, thereby accurately triggering a prediction module for AP positioning when necessary. Extensive evaluations show a very high prediction accuracy for our solution, which can provide around 25% improvement on user throughput performance in mmWave WLANs. This intelligent AP-planning framework well handles the environment dynamics that affect the average-case network performance, which is of utmost interest for network deployers because of its usage convenience and adaptivity.
Fault Tolerance of Stateful Microservices for Industrial Edge Scenarios

Due to the ubiquitous increase of Industrial Internet of Things(IIoT) devices, there is a tendency to move some of the microservices-based applications from Cloud to Edge. However, edge devices are prone to node failures because of weak reliability, resulting in the loss of stateful microservices computing state, which may involve fault tolerance of stateful microservices. Moreover, the method of traditional mechanisms for microservices fault tolerance could not meet the real-time requirement. Within this context, based on stateful microservices characteristics, we propose a novel fault tolerant mechanism for IIoT Edge, which mainly consists of causal logging and distributed checkpoint algorithm. This fault recovery mechanism utilizes causal logging to record the nondeterministic events of microservices, and completes the state recovery of microservices by loading checkpoint and replaying log records, which achieves exactly-once guarantees for distributed microservices. In addition, a set of experiments was performed to evaluate the proposed mechanism by integration with Kubernetes. The results show that the proposed mechanism has less impact on service performance compared with other methods.
SRAM-based efficiency memory model for quantized convolutional neural networks

Consumer electronics pursue the balance of power consumption and performance, especially for today's AI consumer electronic devices. Reducing power consumption while maintaining system reliability is a significant challenge. At the same time, artificial intelligence edge computing devices, such as Artificial Intelligence of Things (AIOT) devices, are expected lower memory occupancy and consumption for Convolutional Neural Networks (CNN) computing devices. In this paper, we design a model with certain reliability under low voltage composed of two parts: protected bits and unprotected bits. The system's reliability with SRAM as memory is improved, and we analyzed the relationship between the Bit Error Rate (BER) and the recognition rate. We present a circuit model that can reduce power consumption while maintaining a high recognition rate.
Wavelet Scattering of RhoB-Expressed Deep-Learning Features for Rectal Cancer Prognosis

Association between RhoB protein expression and rectal cancer in radiotherapy (RT) resistance has been hypothesized. However, there is no strong clinical evidence to confirm the prognostic power of the protein in this disease. Here, we combine advanced artificial intelligence and signal processing methods to examine RhoB expression captured by immunohistochemical imaging of tumor tissue in a cohort of rectal cancer patients with preoperative RT. Prediction results obtained from the proposed approach with 10-fold cross-validation accuracy rates between 85% and 94% not only discover the potential role of RhoB for rectal cancer prognosis, but also significantly outperform individual pretrained deep-learning models with accuracy between 58% and 67%.
Pushing the Limits of an FCN and A CRF Towards Near-Ideal Vertebrae Labelling

In this work, we propose a simple pipeline for labelling vertebrae in a spine CT image composed of a fully convolutional neural network (FCN) and a conditional random field (CRF). Firstly, we adapt the high-resolution network to work on three-dimensional spine CT images and train them with recent advances in deep learning to regress spatial likelihood maps of the vertebral locations. This sets a strong baseline performance for fully automated identification, resulting in a performance comparable to prior state-of-art. Secondly, we employ a prior-informed CRF conditioned on the predicted likelihood maps of the HRNet, thus refining the location predictions. Our custom FCN-CRF solution produces state-of-the-art results in automated labelling tasks for three benchmark datasets achieving identification rates higher than 97%. Finally, we design an interaction module to perform drag-and-drop correction on the CRF output graph. This semi-automated solution achieves near-100% identification with minimal interaction (measured in actions per scan). Code for this work is published at https://github.com/JannikIrmai/interactive-fcn-crf.
TOD-Net: Transformer-Based Neural Network for Tiny Object Detection in Sperm Microscopic Videos

The total number of families who have lost their only child in China is about 1 million, and the death toll in this category is about 76,000 yearly. Therefore, people desperately need the help of in vitro fertilization (IVF) technology, and the selection of excellent sperms is the key application of IVF technology. However, there exists some difficulties to detect tiny objects such as sperms in microscopic videos, especially in large-scale high-throughput experiments. One of the primary reasons is, sperms in microscopic videos are tiny, fuzzy, and with quite random characteristics and dynamics, which are difficult to detect using the current image analysis methods. Here, an advanced transformer-based neural network is proposed for tiny object detection (TOD-Net), and the model is evaluated on a unique high-quality labeled big dataset of sperm microscopic videos (consisting of >151,000 annotated objects). The results show that TOD-Net outperforms the state-of-the-art methods in the sperm detection task (83.61% AP50), works in real-time (35.7 frames per second), and is in an excellent agreement with that reported by medical expert.
Design of a Collaborative Energy-Aware Routing Protocol for UAV-VANETs

Due to the mobility of drones and their limited power supply, the network topology frequently changes, making routing more complex. To address this issue, an energy-aware routing protocol is proposed to extend the lifespan of the unmanned aerial vehicle (UAV). Because vehicles have less power constraints, the proposed algorithm prioritizes vehicles for packet transmission when multiple paths exist, and both UAV networks and vehicular communication networks can aid in packet forwarding to improve the lifespan of UAV networks.
Design of Improved Path Planning Algorithm for Realistic Roads

The road navigation system which can be often seen everywhere plays an important role in the modern life. The appearance of this system replaces the traditional paper map, thus it not only can efficiently reduce the time to find the way for the drivers and substantially absolve the trouble of getting lost and risk, but also can increase the driving safe. However, the existing system generally considers the information of planned path length, road speed limit, or road type in the map when performing the path planning, rather than considers other road conditions that will also affect the applicability of path planning, such as the effects of traffic lights and traffic jams, and so on. This paper mainly proposes an improved path planning algorithm for realistic roads by adding the function of further considering the waiting time of traffic lights to the existing one, to meet the requirements of applications in the future intelligent transportation system.
Undersampled MRI Reconstruction Using Switchable Interdependent Self-Cooperative Learning Without Paired Training Data

In recent studies, deep learning has proved to be an imperative tool for accelerated MRI reconstruction. Despite its superior performance and fast computational time, the performance of existing deep-learning methods is highly dependent on the amount of training data. However, it is challenging to generate a large amount of fully sampled high-resolution k-space data because MRI typically requires a longer scanning time. To address this problem, we propose a novel extension of interdependent self-cooperative learning (ISCL) for highresolution accelerated MRI reconstruction using only small sets of unpaired k-space data. Adaptive instance normalization layers (AdaIN) and affine transforms are used to reduce training complexity, facilitating stable training with a small dataset without paired (full and undersampled) MRI data. Our sampling consistency loss improves the data consistency in the MRI image domain by providing a constraint in the k-space domain. We demonstrate that the proposed method can reconstruct high-resolution MRI images from undersampled k-space data without paired training data.
Stator Intermittent Ground Fault Detection in High-Impedance Grounded Generators

The paper shows that stator intermittent ground fault (IGF) events in high-impedance grounded (HIG) generators cannot always be dependably detected using the conventional and some new proposed IEEE protection schemes. Hence, a subharmonic current differential scheme is presented for reliable detection of IGF events. The key advantage of the new scheme is that it uses already available neutral grounding current signal in the existing subharmonic injection protection circuit of HIG generators and as a result it does not require any additional current transformers or hardware. The performance of the proposed scheme is evaluated using simulations with field captured data from a 618 MVA generator.
Exploring CNN-Based Self-Supervised Illumination Inhomogeneity Compensation for Serial Optical Coherence Tomography

Serial blockface histology is a 3D imaging modality that combines a vibratome with a microscope. Whole samples are acquired by sequentially removing small tissue layers with the vibrating blade and by generating a mosaic of several images of the revealed tissue which can be assembled to obtain a 3D representation of the sample at a high resolution. Due to many factors, the acquired mosaic tiles can be affected by complex illumination inhomogeneity that negatively affects the data reconstruction and analysis. Here, we propose a convolutional neural network approach to estimate and compensate the illumination inhomogeneity. The model is trained with simulated vignettes without using illumination ground truth, which is many times harder or even impossible to obtain. Using a small multiresolution dataset consisting in serial OCT images from whole mouse brains, we show that our proposed approach has many advantages compared to an unsupervised a posteriori illumination compensation method.
Method of Inverse Dynamics Problems with Incomplete Observations

A dynamic object controlling problem is considered when the control algorithm is based on the concept of inverse problems of dynamics in conditions of incomplete observability of the state vector. It is shown that with the direct synthesis of control, the optimal value of the quality functional is achieved by measuring the full state vector. Otherwise, firstly, the closed control system will not meet the required restrictions, and, secondly, the conditions of optimality of the specified quality functional are not met. An algorithm for restoring the full state vector is proposed, the estimation of the state vector is determined as a function of the discrepancy, defined as the difference between the measurement vector and its forecast. This algorithm differs from the known ones in that the control signal is formed by the measured components of the state vector of the system, and the missing components are replaced by the coordinates of an ideal closed-loop system.
Patient-Specific Dynamic Model for Optimization of Cardiac Radioablation

Cardiac radiotherapy ablation, or radioablation, is a promising treatment for refractory ventricular tachycardia. However, its precision may be hampered by cardiac and respiratory motions. The purpose of this work was, based on cardiac and respiratory dynamic CT scans, to generate a patient-specific dynamic model of the structures of interest. Deep leaning-based segmentation was used to extract the geometry of the cardiac structures, whose deformations and displacements were assessed using deformable and rigid image registrations. The combination of the model with dose maps enabled to evaluate the dose locally accumulated during the treatment. Each step was evaluated considering expert references. The exploitation of the model was illustrated on the data of two patients, demonstrating that the impact cardiorespiratory dynamics is not negligible and is highly patient-specific.
Optimization and Coordination of Crowdsourcing Supply Chain in Fast Fashion Industry

Due to inadequate designers in fast fashion industry and the development of the Internet, small-and-medium-sized garment makers have gradually turned to external talents to enhance their new product design efficiency via crowdsourcing initiative. This paper presents a new framework of crowdsourcing supply chain for fast fashion industry. First, a basic multiperiod order model is established in a crowdsourcing supply chain system, where a garment maker chooses the best one among available solutions submitted by online designers (i.e., crowdsources) in each period, then transforms it into finished product, and sells to consumers through a retailer. Second, we extend this model by integrating the factors of capital turnover, the retailer’s risk-aversion, and the garment maker’s minimum production quantity. Moreover, utilizing wholesale price, buyback, and profit-sharing scheme designs a mixed contract for coordinating crowdsourcees, retailer, and garment maker of crowdsourcing supply chain for achieving Pareto optimality. The models help the garment maker determining the optimal production quantities of crowdsourcing designed products and enable the retailer placing the optimum orders and setting the reasonable risk level. In addition, we also find that the incentive policy for crowdsourcing designers can be fulfilled by using a profit-sharing scheme with a piecewise function of order quantity instead of a linear function.
Virtual Machine Placement Method with Compressed Sensing-Based Traffic Volume Estimation

In a cloud computing environment, a virtual machine placement (VMP) problem is considered for traffic load balancing, where each VM is placed on a physical host according to traffic loads in the physical hosts. In particular, VM is referred to as a node and a pair of nodes is considered a flow. To achieve load balancing, identifying flows that transfer heavy traffic volumes is necessary. We propose a VMP method with compressed sensing (CS)-based traffic volume estimation. In the proposed method, all flow traffic volumes are estimated by CS from the traffic volumes that nodes transmit and receive. VMs are placed according to the estimated flow traffic volumes by solving a combinatorial optimization problem. We validate the proposed method via simulation experiments.
Underactuated Control for Two-Wheeled Mobile Robot with an Arm Using Torque Constraint Conditions and Disturbance Observer

This paper presents an underactuated control system for a two-wheeled mobile robot (TWMR). TWMR is expected to use as an autonomous mobile robot (AMR) that works in human-collaborative spaces. TWMR has a smaller footprint and higher center of gravity (CoG), than typical AMRs. Furthermore, in this study, TWMR has an arm. Therefore, TWMR is superior in handling loads in human-presence spaces. However, TWMR is the underactuated system in the body’s translational position and tilting angle. In addition, TWMR is affected by disturbances caused by loads. As a robust underactuated control system, the cascade PI/PD and synthesized pitch angle disturbance observer (SPADO) are considered. The cascade PI/PD results in significant translational position error and oscillatory responses. To cope with this problem, a proposed method minimizes both errors of the translational position and tilting angle within torque constraints. The proposed method utilizes the predictive functional control (PFC) for the underactuated control considering constraints. SPADO improves the accuracy of an internal model of PFC. The validity of the proposed method was confirmed compared with the cascade PI/PD through the simulation.
Latency Analysis and Trial for 5G Ultra Reliable Low Latency Communication

The characteristics of URLLC technology in high reliability and low latency make uRLLC technology an important breakthrough for traditional communication to cut into the Internet of Things (IoT) industry. However, the current uRLLC industry is not mature, and both industry and operators lack reliable test results to determine the application scheme, such as low latency. Although theoretical analysis can get the latency gain from different characteristics, factors such as terminal processing latency are not negligible part in practical applications, and latency-sensitive IoT applications need real latency test results to better apply uRLLC technology. In this paper, we analyze in detail the latency features of uRLLC, and simulate real networks with network emulators to analyze the latency gain of uRLLC from multiple dimensions such as duplex type, frame structure, UE processing capability, and technical characteristics, and conclude that shorter slot lengths, better UE processing capability and smaller scheduling units all result in reduced latency, with gains of up to 50% in some configurations. Finally, we make some suggestions for the commercialization of uRLLC.
Impact of Constellation and Index Mapper Selection in OFDM-IM Systems

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) offers significant flexibility to address the diverse communication scenarios of 5G and beyond. However, the selection of an appropriate index mapper remains an unresolved issue that could pose challenges to the standardization of OFDM-IM. Through the evaluation of a specific example in this paper, it is observed that without optimizing the index mapper, a performance loss of approximately 0.7 dB (SNR gain) may occur. Additionally, we discovered that the optimized index mapper can be universally applied to various constellations, despite OFDM-IM exhibiting sensitivity to subcarrier power variations.