Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Coordinated Control of Low-Voltage Multi-terminal DC Distribution System Based on Model Predictive Control

Multi-terminal flexible structure can interconnect multiple AC systems through DC network, which can fundamentally change the original structure of traditional power grid. However, with the increasing penetration of distributed generation (DG), the quality of DC voltage has been widely concerned. In this paper, the equivalent model of DC load group is analyzed, and the complete model of multi-terminal DC (MTDC) system is constructed, and as a result the discrete state equation is deduced. On this basis, considering the effect of voltage control and the capacity of voltage source converter (VSC), a centralized coordinated control method of variable droop gains based on model predictive control (MPC) is designed. The problem of voltage fluctuation is solved by minimizing the deviation between the predicted voltage and the rated voltage of each VSC. According to the different power margin of the converter station, the droop gains is adjusted in real time without changing the operation mode of the converter station, so that the converter station can participate in the power regulation flexibly according to its power distribution ability to achieve the optimal power distribution.
A Miniaturized Unequal Power Divider Design for 5G Telecommunication systems

For 5G RF (Radio Frequency) devices, various instrumentation and sensors, there is an increasing demand for the miniaturization of microwave devices. The passive power divider has the advantages of stable operation and simple structure in various microwave systems, instrumentation, and a large number of applications in 5G, so the performance of the power divider will affect the performance of the entire telecommunication system to a large extent. The following shows a lumped $\pi$ model output power ratio of 2:1 unequal Wilkinson power divider. Compared with the traditional structure, under the premise of ensuring that the performance meets the requirements, the size of the device can be greatly reduced, and the practical application value is obtained.
Dielectric Constant Measurement and Error Analysis of 3D-Printing Materials For Microwave Applications

To facilitate the using of 3D-printing process in the design and implementation of microwave components, this paper presents the measurement of the complex dielectric constant of 3D-printing non-magnetic materials. A rectangular waveguide based transmission line method has been used, through which the dielectric constant can be retrieved from the measured $S$-parameters of the transmission line filled with the 3D-printing material. The calibration of referenced phase plane has been discussed, and two empirical formulas have been introduced to improve the measurement accuracy. The proposed method has been experimentally verified, and a much smoother result has been achieved when comparing with the counterpart result obtained by a commercial software.
Enhanced Explicit Congestion Notification (EECN) in TCP with P4 Programming

In current TCP/IP networks, TCP mainly relies on packet drops as an indication of congestion. With the emergence of Explicit Congestion Notification (ECN), TCP can detect the congestion through packets with marked bits instead of dropped packets. ECN thus reduces the unnecessary delay due to packet drops, especially for bandwidth-delay sensitive connections. However, ECN still relies on the receiver for congestion indication, which incurs a round trip time (RTT) before the sender can react to the congestion. In network environments with a high bandwidth-delay product (BDP), this could be problematic. In this paper, we present an enhanced ECN mechanism for the early detection of congestion using P4 programming. In our proposed scheme, the sender does not have to wait for the receiver to indicate congestion because switches in the network can indicate congestion. We evaluate our scheme comparing our proposed solution with standard TCP and TCP with the conventional ECN. Experimental results show that our scheme outperforms ECN as the RTT grows.
Effect of Image Noise on Registration in PET Brain Imaging

Rigid image registration techniques can be used in PET imaging to align frames acquired during a brain scan where motion may or may not have been present during the scan. The subsequent estimated motion parameters can be used to correct for the motion. Shorter frames provide better temporal resolution but suffer from increased noise, which may degrade the quality of the image registration. The relationship between the image noise (by proxy of the frame count level) and the accuracy of the image registration is investigated. For FDG brain studies, a true coincidence level of 200×103 (usually approximately 1 sec frame duration) was found to provide adequate image quality for accurate registration.
Intelligent Distributed Customer Anticipation Approach for Taxi Routing Optimization

The advent of ubiquitous mobile computing technology has opened doors for innovative applications that effectively utilize information from IoT devices in a real-time and seamless manner, disrupting many traditional business procedures. A prevalent example of such applications includes on-demand taxi calling services such as Grab and Uber, which have shown to help taxi drivers to reduce the roaming time to search for customers. Regardless of such technology, taxi drivers still have to idly wait until the customers use the application to request taxi services. The ability to accurately predict the customer spawning at a given time could prove crucial in productive taxi roaming. In this paper, we propose a distributed taxi routing optimization approach that aims to suggest each taxi driver to productively roam to areas where the customers are expected to spawn. Specifically, we propose a machine learning model to forecast taxi customer spawning at a given place and time. Then, such a model is used to compute the probability of customer spawning for each road cluster. Finally, a weighted randomization algorithm is used to suggest a taxi driver to travel to a specific region to optimize his search time and to avoid scramble for customers. Our proposed method is validated in the simulation program (COMSET) and compared with the random walk and random destination baselines. The experiment results suggest that our proposed algorithm is effective and yields promising results that overcome both of the baselines.
Double-Ridge Waveguide Mode Analysis based on FDTD-SVD Method

In this paper, hybrid finite-difference time-domain method(FDTD) and singular value decomposition(SVD), denoted by FDTD-SVD, is applied to analyze the double-ridge waveguide dominant mode. Double-ridge waveguide (WRD) without fillets and WRD with small fillets are analyzed. The simulation results show WRD without fillets can achieve a single-mode operation of $TE_{10}$ from 35GHz to 112 GHz. For WRD with small fillets, its single-mode operation frequency band is 35–116GHz. These results are almost consistent with the detailed parametric study result(38–120GHz)based on Ansys HFSS, which proves that the FDTD-SVD method is simple and more effective. In addition, the transmission performance of WRD can be improved by adding fillets and enlarging the radius of fillets.
Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction

Respiratory motion has to be corrected in PET/CT imaging for precise tumor detection and quantification. The optimal motion correction methods for regular breathers and irregular breathers could be different. In this study, we developed deep learning based methods to automatically classify patient breathing patterns and investigated the impact of breathing pattern variability on gating performance. We implemented a hybrid neural network consisting of convolutional (Conv) layers, recurrent layers (LSTM, long short-term memory) and a linear classifier to differentiate breathing patterns. 1295 respiratory traces collected using RPM (Real-time Position Management) system were used for training and testing, as well as additional traces acquired using the Anzai system. We optimized the deep neural network with respect to data preprocessing, augmentation, weighted loss function and generalization capability. The results showed that the proposed deep learning model has reached a high prediction accuracy, with a sensitivity of 92.0% and a specificity of 91.8%. Using phase gating approach, for regular breathers, end-expiration phase gating can effectively reduce the respiratory motion. In contrast, for irregular breathers, larger amount of intra-gate motion was present in the gated PET/CT images and more sophisticated motion correction methods are required.
High Speed-Low Power GNRFET based Digital to Analog Converters for ULSI applications

In this papr, A 2-bit, 3-bit, and 4-bit DACs using newly emerged transistor technology known as Graphene Nano Ribbon Field Effect Transistor (GNRFET) technology were developed. A channel length of 10nm for the GNRFET device with supply voltage of 0.7V was incorporated in the design and simulated via ADS (Advanced Digital System) platform. Biasing with current mirror topology was used for highly efficient small size implementation. The power consumption was analyzed for all three devices. The design showed a full range linear input region within the 0.7 V supply. The signal to noise distortion ratio (SNDR) was 25.8 for the 4-bit DAC. The findings of this design conclude that the proposed DAC is more suitable for high speed nano electromechanical systems (NEMs), computer architecture and memory cells, among other applications.
Autonomic Resource Provisioning Framework for Service-based Cloud Applications: A Queuing-Model Based Approach

The user’s request changes dynamically in service-based cloud applications, which requires optimal amount of computing resources to meet service-level agreements (SLAs). The existing server-side resource allocation mechanisms have limitations in provisioning the required resources to handle the incoming load on the basis of user’s requests. To overcome the aforementioned situation, cloud computing provides ample amount of computing resources to meet the SLAs. There are possibilities that cloud resources might not be properly utilized and might suffer over and under utilization. In this study, the authors have proposed an autonomic resource allocation framework, that automatically provisions (allocate and deallocate) the required computing resources as per the load. In this study, the proposed model leverages the queuing model to optimize the resource allocation process. The primary goal of this study is to improve the virtual resource utilization and response time with respect to the existing methods. Finally, the results have shown that the response time and resource utilization have been improved.
A Wideband Vivaldi Antenna with Fractal Dielectric Lens for Imaging Applications

In this communication a novel fractal inspired antipodal Vivaldi antenna is proposed for wideband applications. A step by step procedure has been employed to design and optimize the performance of the proposed antenna. First, a conventional antipodal Vivaldi antenna (CAVA) is designed as a reference antenna and then a Koch fractal shaped dielectric lens has been added an extension of the antenna substrate. The presence of fractal dielectric lens produces stronger radiation in the end fire direction and also enhances the gain and directivity of the antenna. The optimized antenna is fabricated and tested showing S11 below −10 dB from 4.2 GHz to 42 GHz band. The dimension of the proposed antenna is 129×66×1.524 mm3. Results show that S11 characteristics and other parameters are in good agreement with the simulation counterpart.
Can Deep Learning Detect Esophageal Lesions In PET-CT Scans?

PET-CT scans using 18F-FDG with a co-registered CT scan are increasingly used to detect cancer. This paper compares deep learning-based lesion detection tools trained on PET, CT and combined modality data. 486 pre-contoured scans were used from a retrospective cohort study into esophageal cancer. Scans were partitioned into training, validation and test sets with an 80:10:10 ratio. 1000 image segments were generated from each scan, with tumor present segments located on the contoured lesion and tumor absent segments distributed randomly within the patient but excluding the tumor. PET and CT image segments were used to train a separate dedicated 5-layer convolutional neural networks (CNN). Testing on segments from unseen scans resulted in an accuracy of greater than 95% for the PET data, and greater than 90% for CT data.
A Novel Weighted Relaying Algorithm for Opportunistic Mobile Sensing System Applications

Novel mobile information systems are being developed with the use of smartphones. The traditional applications of wireless sensor networks such as environmental monitoring, health care, and transportation are being realized by many mobile social network applications. Smartphones forward various onboard sensor data using a store-carry-forward way to the central repositories over the internet. In this paper, inter contact time distribution of each smartphone user with other users is utilized for efficient sensor data routing. A novel threshold-based approach is used to estimate future interconnection times. Human mobility based Weighted Relaying Algorithm (Hm-WRA) is proposed for routing in opportunistic mobile sensing systems. The proposed protocol performs almost 15% to 20% better compared to an intercontact time based shortest path routing algorithm named CHARON in terms of the data delivery ratio with proper selection of threshold value at each node.
An Automatic Laser Scanning System for Objects with Unknown Model

Laser scanning has been widely used in the industrial manufacturing, especially in reverse engineering, quality control and surface defect detection. Achieving automatic scanning can succeed in reducing production costs and increasing production efficiency. This paper proposes an automatic intelligent scanning system based on ROS, including robot arm, RGBD camera and line laser scanner. The system is designed to scan objects with unknown model and we use high precision calibration methods to improve the accuracy of the system. A novel scanning trajectory planning strategy is developed, including three steps. Firstly we plan preliminary trajectory based on the RGBD camera data. Then the robot arm pose is adjusted with PID controller in real time to optimize the scanning results. Finally, according to the distribution of point cloud density, the portion whose density is lower than the threshold will be re-scanned. The experiment we illustrate shows the feasibility and high accuracy of our system.
Clinical Implementation of MITS Reconstruction for Linear Tomosynthesis

Tomosynthesis is an X-ray based tomographic modality, which calculates slice images of the examined volume from a set of low dose projection images acquired over a limited scan angle. Due to the limited scan angle, the reconstruction is an extremely ill-posed inverse problem. Therefore, many artefacts degrade the calculated slice images in the case of any reconstruction method. If linear, shift invariant system based algorithm reconstructs these slices, they can be interpreted by utilizing linear systems theory. MITS [1] is one of the most adequate of these methods; although its clinical implementation is not trivial. This paper examines the main difficulties of its clinical implementation from both theoretical and practical viewpoints, proposes modifications to treat these and compensate the possible artifacts (i.e. the intensity oscillation artifact, the partial volume artifact and the effects caused by the low condition of the inverse problem). The proposed modifications preserve the linear, shift invariant nature of the reconstruction, therefore, the reconstruction after applying these modifications, can also be interpreted by analyzing its Transfer Function. The paper also describes the method of this interpretation. A clinical implementation of a linear tomosynthesis system is used for validating the proposed, modified MITS algorithm. The paper concludes that, based on this validation, the proposed modifications significantly decrease the artifacts of the MITS algorithm in practical utilization and improve the quality of the reconstructed slice images, while the whole reconstruction remains interpretable (which mean, that we exactly know how any voxel of a reconstruction depend on the internal structure of the examined volume).
Extensible Environment for Monitoring and Detecting Symptoms of Depression

Depression is a silent disease that is increasing alarmingly due to the pace of society lifestyle. The symptoms of depression themselves cause patients to face psychological barriers that hinder the search for treatment. Currently, mobile devices are being used to monitor the behavior of people and, thus, identify if they have a healthy lifestyle or, even, if they have specific diseases. These devices are also being used to identify symptoms of depression. Nevertheless, most of the applications developed consume a lot of mobile resources and require the active participation of the user answering some test, which entails a barrier for the adoption of these applications. This paper presents an environment that passively monitors the user's context for automatically detecting symptoms of depression, reducing the possible barriers for the identification of this disease. This environment has been developed so that the monitored data can be reused by other systems, reducing the resource consumption. In addition, the presented system has been compared with popular mobile apps for assessing the mental health analyzing the resource consumption and the required interaction to users.
Pulse Shape Discrimination and Energy Measurement in Phoswich Detectors Using Gated-Integrator Circuit

A simple readout circuit has been developed to discriminate the pulse shape from different scintillation crystals in a phoswich-based PET detector as well as measure event energy. As opposed to other methods, this approach only requires a single channel, single ADC with power efficient components. To test this concept, a phoswich PET detector comprising two 3×3×10 mm3 fast and slow LSO crystals (τd = 34.39 ns and 43.07 ns respectively), side-coupled to a 1×2 array of 6×6 mm2 SensL J-series SiPMs was used. Experimental results verify that the small difference in decay times of the crystals leads to a difference in their integration times to the maximum integrated values. This validates a pulse shape discrimination with 98% of accuracy and a M = 2.33 FOM.
Monte Carlo Modeling of Electron Multiplication in Amorphous Silicon Based Microchannel Plates

Amorphous silicon based microchannel plates are being developed to overcome performance limits of conventional microchannel plates. They offer a new flexibility and ease of fabrication. A comprehensive AMCP model is being constructed to analyze the performances of AMCPs. It includes Monte Carlo simulation of secondary electron emission distribution as a function of energy and angles and finite element analysis multiphysics software to compute electron trajectories. The paper presents the results of Monte Carlo simulations of secondary emission functions in silicon and the high secondary emissive material Al2O3. We discuss the gain and potential performance as a function of geometry of such devices. The validity of the Eberhardt model for the analysis of AMCPs is also addressed.
GATE Optical Simulation of a Dual-Ended Readout DOI PET Detector with Trapezoid Geometry

GATE Monte Carlo optical simulation has been used widely as an excellent tool for the design and optimization of positron emission tomography (PET) detectors. Recently, our group has started to develop a small animal depth-of-interaction (DOI) PET scanner with a trapezoid geometry crystal for highresolution and high-sensitivity small animal imaging. However, modeling of a DOI PET detector with a trapezoid geometry crystal has not been studied yet because of the complex geometry. The aim of this study was to model this trapezoid geometry detector with a mixed SiPM pitch using GATE Monte Carlo optical simulation. The trapezoid geometry dual-ended readout PET detector consisted of a 13 × 11 LYSO crystal array, a 7×6 SiPM array (pixel pitch = 2.4 mm) and an 8×4 SiPM array (pixel pitch = 3.2 mm). The 20 mm long LYSO crystal had front and back pitches of 1.18 mm and 1.87 mm, respectively. The effect of the crystal surface roughness on the DOI resolution was investigated. The DOI resolution was calculated by using the signal amplitude ratio of the two SiPMs. We analyzed the GATE optical simulation output file by using a custom-written MATLAB code. In conclusion, we could identify all crystals in the 13×11 LYSO crystal array in the 2D flood map. The DOI resolution of 1.1 mm could be obtained by using a saw-cut LYSO crystal. In the future, we plan to validate the GATE optical simulation results by experimental results.
Dynamic PET Image Reconstruction Using the Wavelet Kernel Method

Dynamic PET imaging is used to monitor the spatio-temporal distribution of a tracer in a tissue region. Dynamic PET can suffer from high noise; to address this problem, the kernel method has been developed for efficient dynamic PET image reconstruction. Previous kernel approaches used a Gaussian kernel to exploit nonlocal spatial correlations from image priors. The Gaussian kernel, has an undesired effect of smoothing high frequencies. In this work, we propose using a wavelet kernel with good energy compaction to further enhance kernel-based dynamic PET image reconstruction. The oscillation in the wavelet kernel can result in better representation of details in the final reconstructed images. We evaluated the wavelet kernel approach using patient data acquired from dynamic C-11 hydroxyephedrine (HED) PET imaging. Reconstruction results demonstrate that this wavelet kernel approach achieves better image quality than standard reconstruction and the Gaussian kernel approaches.
Access Control for Physical Objects in Internet of Things

The rapid development of the Internet of things (IoT) promotes the close combination of cyberspace and physical space, thus humans can dynamically manage their own life in social space to achieve a smart life. However, as more and more physical objects (eg, people, devices) accessing to the IoT, a lot of sensitive information related to our privacy integrates with the IoT. Therefore, the security and privacy have become a key issue for the IoT. In order to avoid the data leakage and illegal access in the IoT, people have proposed the access control scheme for physical objects. The secure access control of physical objects in the IoT is the cornerstone of IoT security. This paper mainly analyzed the requirements of access control for physical objects in the IoT. For different needs, we further summarized the corresponding schemes of access control, then we discussed the challenges and future development of physical objects in the IoT.