Miracle-dz/UniFAR-FTUs huggingface dataset:
Thread Voting DVFS for Manycore NoCs

We present a thread-voting DVFS technique for manycore networks-on-chip (NoCs). This technique has two remarkable features which differentiate from conventional NoC DVFS schemes. (1) Not only network-level but also thread-level runtime performance indicatives are used to guide DVFS decisions. (2) To resolve multiple perhaps conflicting performance indicatives from many cores, it allows each thread to “vote” for a V/F level in its own performance interest, and a region-based V/F controller makes dynamic per-region V/F decision according to the major vote. We evaluate our technique on a 64-core CMP in full-system simulation environment GEM5 with both PARSEC and SPEC OMP2012 benchmarks. Compared to a network metric (router buffer occupancy) based approach, it can improve the network energy efficacy measured in MPPJ (million packets per joule) by up to 22 percent for PARSEC and 20 percent for SPEC OMP2012, and the system energy efficacy measured in MIPJ (million instructions per joule) by up to 35 percent for PARSEC and 33 percent for SPEC OMP2012.
LLC LED Driver Small-Signal Modeling and Digital Control Design for Active Ripple Compensation

This paper presents a new approach for the small-signal modeling of the dc–dc LLC resonant converter aimed to supply high-power light-emitting diodes (LEDs). This small-signal modeling procedure is based on the extended describing function method, where for a reliable small-signal model of the LLC LED driver, the equivalent piece-wise linear circuit of the LED has to be considered under some conditions throughout the model development. Otherwise, the obtained model does not reflect the real dynamic behavior of the converter. Furthermore, a numerical analysis is presented showing the conditions where the proposed model better describes the real dynamic behavior of the converter in relation to the traditional approach where the LED is modeled by an equivalent resistance. From the obtained model, the feedback compensator is designed and implemented in order to track the average current reference and avoid output current ripple, consequently sidestepping output light modulation (flicker). Experimental results show the feasibility of the proposed small-signal model and the active ripple compensation through a dimmable digitally controlled LLC LED driver under closed-loop operation.
Design of a 3-d Printed 6-dof Joystick with Force Sensing

This research presents the design of a joystick sensor that can detect force along the six axes and also provide force intensity. The objective is to use such a manufacturing technique that it is suitable as industrial product for prototyping in future. The design was made on SolidWorks software, and divided into many sub-parts. After simulation and FEA analysis, 3-D printing was used to fabricate it. Subsequently, the parts were assembled together and the electronics components were installed. In the final step, some tests were performed to get the output of device. Incidentally, a customized GUI was programmed that displayed the output of the sensor as an arrow for force direction and a number for force intensity. The experimental results were in accordance with the theoretical results. In fact, while testing the key mechanical component called “flexible hinge”, changing the design parameters was an important step, that led to a better output. Hence, the final results are useful for determining the direction and intensity of force.
A Survey of Antenna and User Scheduling Techniques for Massive MIMO-5G Wireless System

Massive MIMO is a promising candidate for 5G future wireless communications system having ability to drastically scale-up the ergodic sum-rate and simultaneously cut off total radiated power by thousand times. Different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling algorithms that have been employed with massive MIMO systems has reported in this paper. A few number of research concept has been discussed in literature section related to performance analysis of massive MIMO regime associated with large number of antenna scheduling, user scheduling, and combined antenna and user scheduling algorithm. The prime goal of this survey paper is to provide a comprehensive overview of current and emerging research topics e.g., scheduling of antennas and users in massive MIMO. Last 5 years of research contributions has presented in this survey report with a key concentration on implementation of antenna and user scheduling algorithm in massive MIMO systems. Specifically, antenna selection and scheduling, user scheduling, and combined antenna and user scheduling algorithm have addressed. Moreover, different selection algorithms that deal with efficient utilization of power and the most scarcity thing i.e., bandwidth in massive MIMO system has reported. The discussed scheduling algorithms play a pivotal role for selecting the antennas and users in such a fashion that system sum rate has maximized. Furthermore, the list of references serves as a good roadmap for start-up and consequently motivates upcoming researchers to work in this emerging field.
Texture Feature Extraction based on Multichannel Decoded Local Binary Pattern

Automatic classification of texture features is very challenging in image analysis and scene understanding. In this project we are mainly concentrating on the images acquired in various rotation and illumination conditions. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. Here in this paper we propose a multi-channel decoded local binary pattern for the extraction of features. We introduce adder and decoder based two schemas for the combination of the LBPs from more than one channel. The multi-channel decoded local binary pattern is equipped with the kernel-based extreme learning machine for classification. The n the classification accuracy and computational complexity are used as the parameters for the performance evaluation of feature extraction algorithm.
Inverse Association of Peripheral Orexin-A with Insulin Resistance in Type 2 Diabetes Mellitus: A Randomized Clinical Trial.

AIMS To investigate the association between serum orexin concentrations and insulin resistance/sensitivity in a sample of patients with type 2 diabetes mellitus, and to study the effects of anti-hyperglycemic treatment on orexin concentrations over three months. METHODS This study was designed as a randomized, open-label, clinical trial. Before allocation, sixty medication-naïve, newly-diagnosed, type 2 diabetes patients underwent a 75 g oral glucose tolerance test (OGTT). Afterwards, using a randomized trial design (IRCT201102275917N1) patients were allocated to either the metformin (1000 mg daily) or pioglitazone (30 mg daily) arm, and were reexamined after three months. Serum insulin, plasma glucose, and orexin concentrations were measured at baseline, during OGTT, and after three months. RESULTS Orexin concentrations significantly decreased after OGTT (0 vs. 120 min: 0.63 ± 0.07 vs. 0.31 ± 0.03 ng/ml, p < 0.001). Insulin resistance determined by homeostasis model assessment of insulin resistance (HOMA-IR) was significantly and negatively correlated with orexin (r = -0.301, p = 0.024). Furthermore, orexin concentrations were significantly and positively correlated with the insulin sensitivity index derived from OGTT (r = 0.326, p = 0.014). Three-month treatment with metformin and pioglitazone significantly improved insulin sensitivity and increased orexin concentrations by 26% (p = 0.025) and 14% (p = 0.076), respectively. Between-group analysis showed that changes in orexin concentrations with metformin and pioglitazone were not significantly different (p = 0.742). CONCLUSIONS There was a negative association between peripheral orexin concentrations and insulin resistance in type 2 diabetes patients. Three-month anti-hyperglycemic treatment with proportionate doses of metformin or pioglitazone increased orexin concentrations via amelioration of insulin resistance and improvement of glycemic control.
Fault Detection for Linear Discrete Time-Varying Systems With Multiplicative Noise: The Finite-Horizon Case

This paper studies the problem of fault detection for linear discrete time-varying systems with multiplicative noise in finite-horizon, where our main object is to provide an optimal fault detection filter (FDF) design scheme such that stochastic sensitivity/robustness ratio for fault diagnosis is maximized in the sense of probability 1. An operator-aided optimization approach and a generalized FDF are proposed such that solutions to the filter design issues are derived in the operator forms. The relationships among the deduced solutions are explicitly revealed via the proposed operator-aided methodology. The parameter matrices of the filter are computed in an analytical way by solving some recursive matrix equations. It is shown that the addressed approaches establish an operator-based framework of optimal FDF design for some categories of linear discrete-time systems. An example is given to illustrate the efficacy of our algorithms.
The Perception and Experience of Infertile Women Who Received Acupressure in Relation to Anxiety: A Qualitative Study

Background: Women, who are in a critical situation like infertility, are more prone to depression, anxiety, and low self-esteem than other people. The aim of this study was to explore the deep experience of anxiety in infertile women who received acupressure during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). Materials and Methods: This qualitative, conventional content analysis study was performed on 14 infertile female participants in the Milad IVF Center in Mashhad, Iran, from September 2015 to August 2016. The study subjects included Iranian women, aged 20–45 (mean [standard deviation, SD] 29.07 [4.06]) years with primary infertility. Acupressure was performed on the Heart 7 and Pericardium 6 acupoints in 12 sessions. Data were gathered through semi-structured interviews, and transcribed, coded, and organized in different categories based on three primary phases of preparation, organization, and reporting. Results: The results showed the two major categories of “body perceptions of anxiety diminution” and “mind experiences. ” The body perceptions category consisted of three subcategories, namely, reduction of nervous, cardiovascular, and gastrointestinal signs and symptoms. The subcategories of the mind experiences category consisted of emotional and cognitive consequences. Conclusions: The qualitative findings showed a number of positive outcomes in the physical and mental signs and symptoms of anxiety in infertile women undergoing IVF/ICSI, so acupressure can be used for reducing the unpleasant feelings and body perceptions of these women.
Differential splicing of ANP32A in birds alters its ability to stimulate RNA synthesis by restricted influenza polymerase

Adaptation of viruses to their host can result in specialization and a restricted host range. Species-specific polymorphisms in the influenza virus polymerase restrict its host range during transmission from birds to mammals. ANP32A was recently been identified as a cellular co-factor impacting polymerase adaption and activity. Avian influenza polymerases require ANP32A containing an insertion resulting from an exon duplication uniquely encoded in birds. Here we find that natural splice variants surrounding this exon create avian ANP32A proteins with distinct effects on polymerase activity. We demonstrate species-independent direct interactions between all ANP32A variants and the PB2 polymerase subunit. This interaction is enhanced in the presence of viral genomic RNA. In contrast, only avian ANP32A restored ribonucleoprotein complex assembly for a restricted polymerase by enhancing RNA synthesis. Our data suggest that ANP32A splicing variation amongst birds differentially impacts viral replication, polymerase adaption, and the potential of avian hosts to be reservoirs.
A Two-Phase Method of QoS Prediction for Situated Service Recommendation

With the rapid growth of Web services, recommending suitable services to users has become a big challenge. The existing service recommendation works by Quality of Service (QoS) prediction fail to fully consider the influence of situation information, such as time, location, and user relations thoroughly. Two issues must be resolved to consider situation information: issue one, rating scarcity, is that there are less data to learn when importing more situations; issue two is that an effective approach is needed to adapt many situational factors. Our solution is a two-phase method: first, to overcome rating scarcity, data is augmented with estimations of unknown QoS values by learning from observable factors. The augmented data is then used to learn the important latent factors associated with the situational factors for QoS prediction. Experiments on data of real service invocations in different situations show improvement of our method in terms of QoS prediction accuracy over several existing methods, especially in the severe rating scarcity condition. In addition, analysis on parameter selection of proposed method can further assist in obtaining better QoS prediction in practical use.
Compact Omnidirectional Circularly Polarized Antenna Loaded With Complementary V-Shaped Slits

A novel omnidirectional circularly polarized (CP) antenna loaded with complementary V-shaped slits is presented. It is etched on a single-layer substrate with a patch and ground plane on two sides. By arranging the complementary V-shaped slits in the patch and ground plane, horizontally polarized electric field is generated, such that the |<italic>E</italic><sub>ϕ</sub>| = |<italic>E</italic><sub>θ</sub>| and <italic>δ</italic><sub>θ</sub>-<italic>δ</italic><sub>ϕ</sub> = 90° can be realized. Meanwhile, the performance of CP wave is further improved by etching the orthogonal arc slots at the middle of the patch and ground plane. Avoiding additional stub used to generate the horizontally polarized wave, the presented antenna achieves a compact dimension of 0. 52λ<sub>0</sub> × 0. 52λ<sub>0</sub> × 0. 026λ<sub>0</sub> (λ<sub>0</sub> is the wavelength in free space). The realized antenna operates at 5.8 GHz with a bandwidth of 3.5% (5.65–5.85 GHz). Also, it has a peak gain of 1.26 dBic at 5.75 GHz. A good agreement is obtained between the simulated and measured results.
Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines

Time-to-failure (TTF) prognostic plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In the degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert–Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches.
Video Face Swap Based on Autoencoder Generation Network

Video facial swap usually has strong entertainment applications, and it is also applicable for the post-production of films and has great application value. At present, the popular face swap is done manually by the PS software, and the synthetic effect of the automatic face changing technology is not good. In order to make up for the lack of these features, this paper proposes a method of video face swap based on autoencoder generation network. The network learns the mapping relationship between distorted face and original face: the encoder can distinguish and extract facial information, and the decoder can restore face separately. First, the local information of tow face is sent to the network to get the initial model; then, the global information is put into the network for fine-tuning; finally, the face exchange between A and B is completed with face alignment and alpha fusion. The experimental results show that the quality of the method is improved significantly.
A do it Yourself Mobile Communications Signal Based Passive Radar

Passive radar systems have received a renewed interest in the last decade or so due to its numerous advantages and the technological advancements that rendered the receiver design a rather easy task. This paper aims to give the recipe for a repeatable low cost UMTS based passive radar system composed of all commercially of the shelf (COTS) equipment and demonstrate its use for detecting ground targets on the field. We realize that this is not the first attempt to develop a passive radar system based on mobile signals, nor it is the first one to employ COTS equipment, however, unlike previous reports this aspires to give a step-by-step description of the hardware and algorithm design of the radar system that would enable other researchers to build their own which yields it of great practical value.
Optical Phase Lock Loop as High-Quality Tuneable Filter for Optical Frequency Comb Line Selection

This paper describes an optical phase lock loop (OPLL) implemented as an ultraselective optical frequency comb line filter. The OPLL is based on a photonic integrated circuit (PIC) fabricated for the first time through a generic foundry approach. The PIC contains a distributed Bragg reflector (DBR) laser whose frequency and phase are stabilized by reference to an optical frequency comb generator. The OPLL output is a single-mode DBR laser line; other comb lines and noise at the output of the OPLL filter are attenuated by >58 dB below the peak power of the OPLL-filter output line. The OPLL bandwidth is up to 200 MHz, giving a filter quality factor greater than 1,000,000. The DBR laser can be tuned over 1 THz (8 nm), enabling different comb lines to be selected. Locking to a comb line with a frequency offset precisely selectable between 4 and 12 GHz is also possible. The coherence between the DBR laser and the comb lines is demonstrated by measurements of the heterodyne signal residual phase noise level, which is below −100 dBc/Hz at 5 kHz offset from the carrier. The OPLL-filter output can be up to 6 dB higher than the peak power of the comb line to be isolated by the filter. This optical gain is a unique characteristic which can significantly improve the SNR of communication or spectroscopy systems. This OPLL is envisaged to be used for high purity, tuneable microwave, millimetre-wave, and THz generation.
A Modified Objective Quality Index for Image Fusion

Quality assessment of fused image is a vital procedure for image fusion in the Internet of Multimedia Things. A reasonable quality index is not only able to give an accurate assessment for fused results, but also could provide a performance improvement advice for fusion algorithms. In this paper, we will mainly discuss the image fusion metric based on gradient field and improve the accuracy of objective quality assessment algorithm. Our work is towards making the paving for further research of more HVS conformed metric and fusion algorithm based on quality driving.
A Compact, Lightweight and Low-Cost 8-Bit Datapath AES Circuit for IoT Applications in 28nm CMOS

Lightweight AES encryption circuits are significant to IoT applications to protect data, where very strict area and power constraints are required. In this paper, we propose a novel 8-bit datapath AES architecture aiming for a small area and a high energy efficiency. Firstly, our proposed design reduces the number of temporary data storage (IntermediateReg) to 40-bit from the originally 128-bit, which saves 68.75% intermediate registers. Secondly, only one S-Box is implemented in the design that is reused throughout the whole AES operations to achieve a low area. Thirdly, the architecture is further optimized to reduce the number of clock cycles by reusing S-Box in the key expansion and the data encryption module, leading to only 213 total cycles of latency in a complete encryption process. Simulation results under TSMC 28nm technology show that our proposed AES circuit consumes an area of 0.0028mm2, with an energy efficiency of 667Gbps/W (equivalent to 1.50 pJ/bit) and throughput rate of 30.05Mbps at 0.5V. This design achieves a high energy efficiency per unit area of 0.278 Gbps/(W•um2). This small area, high energy efficiency and relatively high throughput AES circuit is suitable for IoT applications.
An Approach for Extracting Road Pavement Disease from HD Camera Videos by Deep Convolutional Networks

In the area of road construction and maintenance, automatically extracting road damages plays an import role for traffic security. This paper aims to extract road surface diseases from camera videos in complex background. In order to detect various damage types with high accuracy, the paper applies deep convolution networks to perform the object detection and segmentation approaches. The road damages can be located and assigned into different classes. Following the road maintenances specifications that enacted by transportation organizations, a weakly-supervised labelling method is introduced to generate representative dataset for massive training and testing. After extracting the damages, the road maintenance staffs need to prioritize the diseases and design optimal repairing schedule. Hence the paper also presents an evaluation approach to calculate the pavement disease's length, width, and influential area, which can be used for quantification purposes. Experimental results show that, the proposed approach successfully identifies different kinds of road damages, providing an efficient way for practical application.
Image Inpainting Algorithm Based on KSVD and Improved CDD

In order to solve the problem that the traditional overcomplete dictionary can't repair the image well when the image data is sparsely coded, this paper uses a learning-type overcomplete dictionary called K-Singular Value Decomposition (KSVD) to enhance the adaptive of algorithm. At the same time, for the problem that the visual coherence of the repaired area is poor and the fuzzy area still exists, the curvature-driven diffusion (CDD) technology is designed to further deal with the damaged area. Finally, using the proposed CDD-KSVD algorithm to repair the image in YUV color space. Experimental results show that compared with the image restoration algorithm using KSVD and the image restoration algorithm based on CDD, the CDD-KSVD algorithm proposed in this paper recovers the details of the damaged area better, and the repaired image is more in line with human visual perception, at the same time, we get higher values through objective metrics PSNR and SSIM.