Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility Study of a Wireless Power Transfer System Applied to a Leadless Pacemaker

This study deals with the battery recharge of a leadless pacemaker, which has been recently introduced in the market, by means of the wireless power transfer (WPT) technology based on the magnetic resonant coupling. The main difficulty is given by the depth of the leadless pacemaker implantation inside the human body. The electromagnetic problem mainly consists in the selection of the on-body excitation (primary coil current and frequency), which must adequately energize the leadless pacemaker without any exceedance of the basic restrictions (BRs) specified by the electromagnetic field (EMF) safety standards. Another relevant problem consists in the weight of the leadless pacemaker within the secondary coil that must be very light. In this paper, a feasibility study is carried out to define possible solutions for this kind of application.
Orchestrating Resource Management in LTE-Unlicensed Systems With Backhaul Link Constraints

Long term evolution (LTE)-unlicensed, an extension of LTE Advanced to unlicensed spectrum, can provide high performance and seamless user experience. To reap the full benefits of the LTE-unlicensed deployment, efficient resource allocation and interference management are critical to ensuring a harmonious coexistence between LTE-unlicensed and WiFi systems. In this paper, we study a resource orchestration scheme for an LTE-unlicensed network where small cells share the same unlicensed spectrum with a WiFi system. An optimization problem for channel and power allocations is formulated to maximize the overall network utility, which is an NP-hard problem. The problem is constrained on meeting the desired data rate demands of the served small-cell users, the capacity-limited backhaul links, and the maximum tolerable interference at the WiFi access point. To solve this challenging problem, a distributed solution based on Lagrangian relaxation is proposed to assist the LTE-unlicensed network in making decisions on channel allocation and transmit power. Furthermore, low-complexity solutions are devised upon applying the one-to-one matching game theory. The simulation results with practical parameter settings show that the proposed algorithms converge to the suboptimal solution after a small number of iterations in the considered examples.
PEA Space Charge Performance of HTV Silicone Rubber in the Process of Water Permeation

As the material for sheds and sheaths of composite insulators, long-term performance is a key property of high temperature vulcanized (HTV) silicone rubber. However, after subjected to the operating environment, water permeation from the air can diffuse into the silicone rubber and harm both electrical and mechanical properties of the silicone rubber. In this paper, pulsed electro-acoustic (PEA) space charge measurement is employed to study the performance degradation of HTV silicone rubber caused by water permeation. HTV silicone rubber samples are immersed into deionized water for different durations, followed by PEA space charge measurement. In the PEA tests, more space charges are injected into immersed samples than samples before water immersion, which means that after the water permeation process, traps will form because of the movement of water molecules. As the immersion time goes longer, the changes in the space charge distribution become more obvious.
Electrical Field Distribution in ±600 kV Converter Transformer Bushing Core with the Application of Epoxy Resin with Nonlinear Conductivity

The converter transformer as the conversion center for switching DC and AC is particularly crucial and is the key equipment of HVDC power system. As the weakness of the converter transformer, it is easy for the insulation failure to happen on the converter transformer bushing. Under load condition, the temperature rise on the conductor leads to an obvious temperature range and electrical field distortion along the radial direction of the bushing core. In this paper, the radial electrical field distribution of the ± 600 kV converter transformer bushing was computed using the electric-thermal-fluid coupling model. Under full load condition, the radial temperature range in the bushing core causes the distorted electrical field strength to reach 12 kV/mm at the flange. Besides, the field strength in the SF6 is tripled at the top end of bushing. Normally micron-sized SiC/ZnO were added into the insulating polymers to obtain nonlinear-conductivity insulating composites, whose conductivities had very strong electrical field dependence when the field strength reached a certain value. When the bushing core is made of nonlinear conductivity epoxy resin, the field distortion at the flange is improved, which also decreases the field strength in the SF6 and oil. It is proved to be viable to suppress the electrical field distortion and decrease the insulation failures by the nonlinear conductivity epoxy resin.
160-GHz Radar Proximity Sensor With Distributed and Flexible Antennas for Collaborative Robots

There is a growing demand for collaborative robots in order to improve the efficiency of industrial processes and to push Industry 4.0 forward. The challenge for these robots is the compliance of the safety regulations to protect human workers from injuries. Therefore, a large number of sensors for the close range (several centimeters up to some meters) around the robot are needed. For a protective cover around the robotic arms all sensors must cooperate and process their information together. This paper presents a close range proximity sensor based on a 160-GHz radar MMIC with a transition to two flexible dielectric waveguides. On the low-loss waveguides, leaky-wave antennas with a large field of view are distributed. This reduces the number of sensors and achieves a coverage area for one sensor of more than $\mathrm {2200~ \text {c} \text {m} ^{2}}$ for a distance of 30 cm. For distances below 30 cm the sensor has blind areas. However, the expected extended targets like body parts can still be detected and the proximity sensor will stop the robot in the danger area. Additionally, the flexible waveguide offers the possibility to place the antennas almost arbitrarily around the robotic arm. For a single target scenario it is even possible to determine the target position by multilateration with only one receive channel.
Dynamically Tunable Scattering Manipulation of Dielectric and Conducting Cylinders Using Nanostructured Graphene Metasurfaces

Nanostructured graphene metasurface is considered for the dynamically tunable scattering manipulation of dielectric and conducting cylinders. It is analytically shown that changing scattering width of dielectric and conducting cylinders into one of the cylinders with desired radii is possible. To achieve this purpose, the required surface impedance of the covering metasurface is derived. By properly tuning the chemical potential of the graphene, the given cylinder can controllably be seen as one with larger or smaller radii without changing the geometry of the metasurface. The simulation results of the scattering width and the 3-D far-field radar cross-section of the cylinders verify the analytical approach.
Breast Cancer Detection and Classification Using Artificial Neural Networks

Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. Breast Cancer detection of medical images is one of the most important elements of this field. Because of low contrast and ambiguous the structure of the tumor cells in breast images, it is still a challenging task to automatically segment the breast tumors. Our method presents an innovative approach to the diagnosis of breast tumor incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using balance contrast enhancement techniques (BCET). The results of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method and Thresholding method to segment the out boundaries of the breast and to locate the Breast Tumor boundaries (shape, area, spatial sizes, etc.) in the images. The third stage feature extraction using Discrete Wavelet Transform (DWT). Finally the artificial neural network will be used to classify the stage of Breast Tumor that is benign, malignant or normal. The early detection of Breast tumor will improves the chances of survival for the patient. Probabilistic Neural Network (PNN) with radial basis function will be employed to implement an automated breast tumor classification. The simulated results shown that classifier and segmentation algorithm provides better accuracy than previous method. Proper segmentation is mandatory for efficient feature extraction and classification.
Distributed Adaptive Attitude Control for Networked Underactuated Flexible Spacecraft

This paper studies the distributed adaptive control of a team of underactuated flexible spacecraft under a leader–follower architecture with the measurements of the rigid bodies only. By treating the flexible spacecraft as an underactuated Lagrange system, an adaptive control strategy is proposed with the feedback of the generalized coordinates, velocities, and accelerations of the rigid bodies. Then, an extended state observer is introduced to design a distributed adaptive controller without the measurement of the generalized accelerations.
Wireless Passive Radiation Dosimeter Using Flash Memory

We propose and demonstrate technique to utilize commercial off-the-shelf Flash memory for radiation dosimetry application. We demonstrate that the state-of-the-art Flash chips is not only sensitive to ionizing radiation (data corruption starts at the dose of 100 rad (SiO2)) but also selective enough to distinguish X-ray vs Gamma-ray exposure. Sensitivity of the dosimeter is measured by correlating data corruption rate, while selectivity is achieved by utilizing dose enhancement effect and relative response from fresh vs worn-out memory blocks. Finally, an algorithm is introduced for smart phone application that can potentially be used for real-time wireless radiation dosimetry
Research on Model for Verifying the Integrity of Software Based on API Hook

Software have been deployed in various measurement and control applications for information and network communication technology. A serious threat to system is malicious software attack that results in fake data delivery or private data disclosure. In order to increase security of the monitoring system, which software code will not be replaced and the tampered. The authors propose a software integrity measurement method based on trust transfer and Hook function. Verify pro- gram integrity during process creation, use program monitoring tools to monitor processes in the isolation environment, and update the white list in real time based on the monitoring results. Finally, experimental results demonstrating the effectiveness of prevent malicious code from destroying the integrity of the monitoring system by window platform experiment.
Hybrid Theorem Proving as a Lightweight Method for Verifying Numerical Software

Large-scale numerical software requires substantial computer resources that complicate testing and debugging. A single run of a climate model may require many millions of core-hours and terabytes of disk space, making trial-and-error experiments burdensome and time consuming. In this study, we apply hybrid theorem proving from the field of cyber-physical systems to problems in scientific computation, and show how to verify the correctness of discrete updates that appear in the simulation of continuous physical systems. By viewing numerical software as a hybrid system that combines discrete and continuous behavior, test coverage and confidence in findings can be increased. We describe abstraction approaches for modeling numerical software and demonstrate the applicability of the approach in a case study that reproduces undesirable behavior encountered in a parameterization scheme, called the K-profile parameterization, widely used in ocean components of large-scale climate models. We then identify and model a fix in the configuration of the scheme, and verify that the undesired behavior is eliminated for all possible execution sequences. We conclude that hybrid theorem proving is an effective and efficient approach that can be used to verify and reason about properties of large-scale numerical software.
UAV-Assisted Emergency Networks in Disasters

Reliable and flexible emergency communication is a key challenge for search and rescue in the event of disasters, especially for the case when base stations are no longer functioning. Unmanned aerial vehicle (UAV)-assisted networking is emerging as a promising method to establish emergency networks. In this article, a unified framework for a UAV-assisted emergency network is established in disasters. First, the trajectory and scheduling of UAVs are jointly optimized to provide wireless service to ground devices with surviving BSs. Then the transceiver design of UAV and establishment of multihop ground device-to-device communication are studied to extend the wireless coverage of UAV. In addition, multihop UAV relaying is added to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs. Simulation results are presented to show the effectiveness of these three schemes. Finally, open research issues and challenges are discussed.
Face Identification for an in-vehicle Surveillance System Using Near Infrared Camera

Face identification is an essential topic in surveillance system research. Surveillance systems have many unconstrained conditions, e.g., brightness, occlusion, and user state variations. In this paper, we propose a multi-SVM based face recognition method using a near-infrared camera. Our method has a face identification scenario optimized for an in-vehicle surveillance system, which comprises two steps: (i) registering a driver and (ii) recognizing whether the driver is a registered. We perform feature extraction and recognition for each facial landmark. In the case of extreme exposure to light, we convert normal face images into simulated light overexposed images for learning. Thus, face classifiers for normal and extreme illumination conditions are simultaneously generated. We also create a new face dataset and evaluate our method with both our new and PolyU NIR datasets. Experimental results show that we achieve significantly higher recognition accuracy than existing methods.
Improving Real-Time Pedestrian Detectors with RGB+Depth Fusion

In this paper we investigate the benefit of using depth information on top of normal RGB for camera-based pedestrian detection. Indeed, depth sensing is easily acquired using depth cameras such as a Kinect or stereo setups. We investigate the best way to perform this sensor fusion with a special focus on lightweight single-pass CNN architectures, enabling real-time processing on limited hardware. We implement different network architectures, each fusing depth at different layers of our network. Our experiments show that midway fusion performs the best, outperforming a regular RGB detector substantially in accuracy. Moreover, we prove that our fusion network is better at detecting individuals in a crowd, by demonstrating that it has both a better localization of pedestrians and is better at handling occluded persons. The resulting network is computationally efficient and achieves real-time performance on both desktop and embedded GPUs.
Critical Care Aspects of Gallstone Disease

Abstract Approximately twenty per cent of adults have gallstones making it one of the most prevalent gastrointestinal diseases in Western countries. About twenty per cent of gallstone patients requires medical, endoscopic, or surgical therapies such as cholecystectomy due to the onset of gallstone-related symptoms or gallstone-related complications. Thus, patients with symptomatic, uncomplicated or complicated gallstones, regardless of the type of stones, represent one of the largest patient categories admitted to European hospitals. This review deals with the important critical care aspects associated with a gallstone-related disease.
Classroom Instruction That Works

a. Each group will present its research-based instructional strategy in an engaging manner (2030 minutes). Each group will prepare a complete lesson plan, based on—and using--the first six (six) steps of the clinical/target teaching (Direct Instruction) model described in class and in Slavin’s text (Chapter 7). Your lesson will provide research and theory on the strategies in your chapter, its guiding principles. It will highlight important components of each strategy you present (not all of them need to be highlighted), and instructional ideas for when best to use each strategy.
Joint Optimal Beamforming and Sensing Interval design for Energy Efficient Cognitive Small Cell

We consider a cognitive small cell to improve both energy efficiency and spectral efficiency. The goal of this paper is to jointly optimize the spectrum sensing time, spectrum sensing interval and the beamforming matrix to maximize the energy efficiency of the small cell. A non-convex optimization problem is formulated under the primary user interference constraint, the secrecy rate constraint and the energy harvesting constraint. The non-convex problem is transformed into a concave problem and a Dinkelbach’s method based iterative algorithm is used to obtain the optimal solution. Simulation results show that the proposed algorithm leads to a significant improvement of the energy efficiency than the traditional spectrum sensing scheme.
User Preference Learning-Based Edge Caching for Fog Radio Access Network

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.
Discrete Event Modeling and Simulation Aspects to Improve Machine Learning Systems

Discrete Event Modeling and Simulation (M&S) and Machine Learning (ML) are two frameworks suited for system modeling which when combined can give powerful tools for system optimization for example. This paper details how discrete event M&S could be integrated into ML concepts and tools in order to improve the design and use of ML frameworks. An overview of different improvements are given and three concerning Reinforcement Learning (RL) are implemented in the framework of the DEVS formalism.
Camera Pose Estimation: What is the computationally efficient approach?

In this paper we present a system that is suitable for real time camera pose estimation over a video stream, Our approach proves to be computationally efficient for real time applications to calculate the new extrinsic parameters for a moving camera. The system depends only on the camera without the aid of any external sensors or fixed points. Our analysis for the problem shows that the most computationally intensive task is in the feature detectors/descriptors used, for that we will be comparing between 2 feature detectors/descriptors; AKAZE and ORB, that are well known for their good performance in terms of computational cost. Although AKAZE has managed to achieve better results, it comes with higher computational cost yet with substantial gain in terms of quality, this will provide more robust results for our system if compared to ORB. On the other hand, ORB still manages to give enough quality to satisfy our needs and with real-time performance.
Microwave Oven Field Detector Probe

This article proposes a probe to monitor the electromagnetic field inside microwave ovens. Although magnetrons are now small and highly efficient, and solid state ovens can control the power delivered to the food, neither can ‘see’ the electromagnetic field inside the microwave oven. Therefore, this article proposes a field detector probe, based on a rectenna design, which consists of an antenna and a rectifier, converting the RF signal, inside the cavity, to a DC voltage. The rectifier is based on a LDMOS power transistor (BLC2425M8LS300P) provided by Ampleon. The electromagnetic field inside the microwave oven can be detected and measured by reading the output DC voltage generated by the field detector probe.
Reversible Swelling and Shrinking of Paramagnetic Nanoparticle Swarms in Biofluids With High Ionic Strength

A microrobotic swarm has drawn extensive attention recently, due to its potential advantages in real-time tracking and targeted delivery in vivo. Herein, we use paramagnetic nanoparticles with a diameter of 500 $\mu \text{m}$ as building blocks to investigate the reversible pattern transformation of a swarm, i.e., swelling and shrinking, in biofluids by simulation and experiments. Three kinds of biofluids, i.e., phosphate buffered saline with Tween-20, simulated body fluid (SBF), and fetal bovine serum, are used, which have much higher ionic strength (up to 0.212 mol/L) and different viscosity compared with deionized water. Our results indicate that, except for the magnetic dipole interactions and hydrodynamic effects, the electrostatic interaction also plays an important role for swarm generation and reconfiguration in biofluids. The equivalent electrostatic torques exerted between nanoparticles, induced by ions, are quantified by an analytical model and are successfully compensated by tuning applied magnetic fields. The input magnetic field has a maximum field strength of 10 mT and a maximum frequency of 40 Hz. The experimental results validate the simulation, and meanwhile, the magnetic particle swarm is capable of performing significant swelling in the biofluids. In addition, by applying rotating magnetic fields, the swelled patterns can be reversibly shrunk into more concentrated regions.