Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling, Analysis and Evaluation of Modified Model Predictive Control Method for Parallel Three-Level Simplified Neutral Point Clamped Inverters

Parallel three-level simplified neutral point clamped inverters (3L-SNPCIs) are applied in this paper because of fewer switching devices, low harmonic, larger power capacity and more flexibility. However, the challenge of zero-sequence circulating current (ZSCC) is inevitable, which causes the output current distortion and system instability. The conventional control and modulation methods introduced by far cannot solve them properly due to the limitations of fewer voltage vectors selection. To overcome these limitations, a modified model predictive control (MMPC) strategy is proposed based on the discrete mathematical model of parallel 3L-SNPCIs. The MMPC strategy realizes circulating current suppression and neutral-point voltage balancing by selecting P-type or N-type small voltage vector. In addition, the control objectives are optimized and the implementation is simplified, which can reduce the burden of calculation and reserve the advantages of the conventional model predictive control (MPC) algorithm. The proposed control strategy can be flexibly extended to parallel multilevel inverters. The efficiency of the proposed method is proved by simulation and experimental results under various conditions.
A Proposal for an Improved Distributed Architecture for OpenPGP's Web of Trust

Security and authenticity issues were never considered when e-mail was originally designed and implemented. Along the time, most of electronic mails have been sent between users in a way that any user, intended or not, could read the message or impersonate a sender. To resolve all these issues and maintain the privacy of e-mails was the reason for the creation of PGP and its complements. However, over the years, PGP revealed unexpected security issues when faced to mistrusted environments where we do not know if someone is who claims to be. To overcome this problem, we propose a trust solution for OpenPGP that improves trust relationships between users and the public key distribution network. Our proposal could be implemented in a system using cryptographic technology efficiently in OpenPGP over an all-new trust approach.
Contrast Limited Adaptive Histogram Equalization for an Advanced Stereo Visual SLAM System

Visual SLAM(Simultaneous Location and Mapping) has gained popularity in the field of autonomous driving, virtual reality and augmented reality recently. However, the popular feature-based visual SLAM systems are usually sensitive to brightness and translation. To make our system more robust, we present a real-time robust feature-based visual SLAM system with low drift and high precision. We contribute an image pre-processing module together with the CLAHE(Contrast Limited Adaptive Histogram Equalization) algorithm to locally enhance image contrast and get more feature details. Experimental evaluation shows that our system is significantly better than ORB SLAM2.
Deep Fusion Net for Coral Classification in Fluorescence and Reflectance Images

Coral reefs are vital for marine ecosystem and fishing industry. Automatic classification of corals is essential for the preservation and study of coral reefs. However, significant intra-class variations and inter-class similarity among coral genera, as well as the challenges of underwater illumination present a great hindrance for the automatic classification. We propose an end-to-end trainable Deep Fusion Net for the classification of corals from two types of images. The network takes two simultaneous inputs of reflectance and fluorescence images. It is composed of three branches: Reflectance, Fluorescence and Integration. The branches are first trained individually and then fused together. Finally, the Deep Fusion Net is trained end-to-end for the classification of different coral genera and other non-coral classes. Experiments on the challenging Eliat Fluorescence Coral dataset show that the Deep Fusion net achieves superior classification accuracy compared to other methods.
Design of On-Chip Millimeter-Wave Bandpass Filters Using Multilayer Patterned-Ground Element in 0.13- $\mu$ m (Bi)-CMOS Technology

A novel design methodology for transmission-zero (TZ) generation in on-chip millimeter-wave (mm-wave) bandpass filters (BPFs) based on the original concept of multilayer patterned-ground (MPG) element is presented in this article. Unlike most of prior-art techniques available in the technical literature, this method has two distinct features. First, it is inherently suitable for miniaturized BPF design since the MPG element can be implemented through the layers below the top-metal layer and, thus, without occupying any additional die/chip area. Second, it provides a simple but effective way to produce a TZ at the upper stopband without adversely affecting other BPF performance metrics. To fully understand the operational insight of the engineered approach, a simplified LC-equivalent behavioral circuit model for the MPG element is developed. Using this model, three second-order BPFs based on different circuit configurations are codesigned to further demonstrate the experimental feasibility of the technique. All the filter prototypes are fabricated in a standard 0.13- $\mu \text{m}$ bipolar complementary metal–oxide–semiconductor [(Bi)-CMOS] technology. The obtained on-wafer measurements show that all fabricated BPF chips have the capability to suppress the second-order harmonic by more than 30 dB, which indicates the effectiveness of the proposed integrated BPF design approach with the MPG element.
Cognitive Training to Improve Problem Solving with EEG Headset

The recent research found that cognitive capacities can be continually improved by conducting proper practices. In our previous paper, we developed a series of training programs in game formats to improve cognitive skills. The real time EEG data read from EPOC headset was used for adjusting the program difficult levels. We conducted testing in some college students. Based on the survey and feedback from the testing group, we implemented a multi-user training program for improving problem solving skill. A preliminary test with a small group of students was conducted and the results were promising.
Analysis of Vulnerabilities of Web Browser Extensions

Web browser extensions are popularly used to add extra functionality to web-browsers such as Safari, Chrome, Firefox, and Edge. These functionalities may include activity, entertainment, and security. However, many of these web exten- sions are not coded by security experts and thus they are targets for hackers when conducting cyber-attacks. This paper is based on a security review of 50 chrome extensions, 25 popular and 25 random. A study is done on their vulnerabilities through privilege granting and escalation, and suggestions are given on how to ameliorate the development and use of extensions, while reducing the risk-factors.
Miniaturization Design of Normal-Mode Helical Antenna Based on Capacitive Loading

This paper discusses the design of an electrically small normal-mode helical antenna (NMHA) with a capacitive loading and an innovative coupling feed structure. With this structure, the height of the helical antenna can be effectively reduced (0.064λ totally) while ensuring the mode of the antenna and high realized gain (2.02dBi). The electromagnetic analysis of a compact electrically small normal-mode helical antenna are presented. To understand the working mechanism, an equivalent circuit model of the helical antenna is proposed and analyzed. This innovative structure makes the antenna compact enough to be widely used in short-range communication devices. The simulated results of the antenna show that the proposed structure has a pretty high radiation efficiency, good impedance matching and high gain.
Modeling Smartphone Energy Consumption Based on User Behavior Data

Energy consumption and power management are key issues problem when using smartphone handsets or any other connected devices. This is linked to two opposite users’ expectations: having a better autonomy on one side and having devices with more computer power and bigger screens on the other side. In literature, many previous works focused on modeling the smartphone power drain at the hardware or software levels. As opposed to these approaches, we present, in this paper the construction of a handset energy model at user level based on coarse grain collected data. To that way, we adopt a crowd-sourcing approach, based on data collected by the Device Analyzer project. This data is covering all the activities/events of the user relatively to his/her smartphone. However, the data is collected within a fixed time period which involved imprecisions in the time stamp associated to each event. So as to model the energy drain and to improve events time location, we propose a top-down method based on reactive multi-agent systems paradigm. In this approach, each event is represented by an agent in a virtual world which can move along the time axis according to the behavior of its mates and which influences the battery discharging curve. As soon as the multi-agent system is stabilized, the behavioral parameters of the agents are used to build up the power model. The value of the multi-agent approach is tied to its ability to incorporate implicit information about battery drain from operating system events such as network usage. Finally, the proposed modeling method is tested on real users data and several coordination strategies are compared. Index Terms—Smartphone, energy model, multi-agent model, crowd-sourcing, user behavior
Liquid Level Sensing using Lamb waves

The present study examines the possibility of using an ultrasonic guided wave technique for liquid level sensing. The setup consists of an Aluminium thin beam which is partly immersed in a liquid whose level is to be determined. When a wave guide is immersed in the liquid, guided wave in the waveguide is attenuated due to the leakage of its energy into the surrounding water. The amount of leakage is related to the characteristics of the waveguide and the surrounding liquid liquid. With these predictions, it is possible to relate the measured characteristics to the liquid level. Previous research shows that the various modes of Lamb waves are influenced by the surrounding liquid. Therefore, the change in the characteristic of Lamb waves can be used as a function of liquid level. This effect has also been investigated using Finite Element Analysis.
Application of Machine Learning Methods for Classification of Telemetric Frames by Compression Algorithms

Problem statement: adaptive data compression systems include, as an integral part, a certain classifier that allows you to select the most efficient compression algorithm. At the same time, for such systems, parallel training of the classifier and data compression should be ensured, which increases the computational cost and complicates the architecture of the transceiver devices. Thus, the question of developing an effective classifier for compression systems is quite acute. Objective: to assess the possibility of using the simplest neural network architecture as a classifier for telemetric data frames. Results: the behavior of the averaged errors of training, generalization and confirmation depending on the size of the training sample obtained for a number of telemetry data sets is investigated. Based on the obtained data, neural network training parameters are proposed that allow achieving high efficiency of its work. A comparative analysis of the effectiveness of the proposed approach and a number of specialized numerical methods has been carried out, which has shown that even the most versatile artificial neural network architecture is significantly superior to systems using numerical classification methods.
Liposome Induced Mechanical Properties Changes in Cell Membrane

As one of the key parameters of cell activity, the mechanical properties of cells have aroused many researchers’ enthusiasm and been widely studied. Liposome, a high-efficiency vector, has been applied to deliver genetic materials into target cells for the treatment of various diseases, accompanying with negative influences on cell membrane and cellular activities. However, its effects on the mechanical properties of transfected cells remain unrevealed. Atomic force microscope (AFM) has been used for cell morphology studies due to its high spatial resolution, no complex sample preparation, and wide imaging conditions. What’s more, AFM force measurement techniques can also be used to study the mechanical properties of cells. Therefore, AFM force measurement technology is an effective method to evaluate cellular response. In this study, AFM was used to investigate the morphology and mechanical properties of HEK293T cells in response to liposome at living cell level. After transformed with the transfection reagent lipofectamine 2000, AFM data suggested a significant increase in roughness and adhesion force but an apparent decrease in Young’s modulus. Besides, MTT assay indicated that cells viability was reduced. All results combined to implied that liposome impacted the cell mechanical properties, impaired HEK293T cells.
Generalized Sparse Matrix-Matrix Multiplication for Vector Engines and Graph Applications

Generalized sparse matrix-matrix multiplication (SpGEMM) is a key primitive kernel for many high-performance graph algorithms as well as for machine learning and data analysis algorithms. Although many SpGEMM algorithms have been proposed, such as ESC and SPA, there is currently no SpGEMM kernel optimized for vector engines (VEs). NEC SX- Aurora is the new vector computing system that can achieve high performance by leveraging high bandwidth memory of 1.2TB/s and long vector of VEs, where the execution of scientific applications is limited by memory bandwidth. In this paper, we demonstrate significant initial work of SpGEMM kernel for a vector engine and implement it to vectorize several essential graph analysis algorithms: Butterfly counting and Triangle count- ing. We propose a SpGEMM algorithm with a novel hybrid method based on sparse vectors and loop raking to maximize the length of vectorizable code for vector machine architectures. The experimental results show that the vector engine has advantages on more massive data sets. This work contributes to the high performance and portability of the SpGEMM kernel to a new family of heterogeneous computing systems, which is Vector Host (VH) equipped with different accelerators or VEs.
Fast Object Detection in H264/AVC and HEVC Compressed Domains for Video Surveillance

In this paper, we propose a new algorithm for fast object detection in the compressed domain of H.264/AVC and HEVC video coding standards. The proposed algorithm is based on three coding parameters: motions vectors, block types and transform coefficients which are extracted from the partially decoded video bitstream. Each feature is separately processed to remove noise and to empowers its discrimination ability. The obtained feature maps are segmented using a fuzzy clustering algorithm. Finally, a fusion step allows merging the segmented feature maps thanks to a weighted linear combination. The originality of this approach lies in its application to both H.264/AVC and HEVC compressed domains. Experiments are conducted using ten test sequences obtained in order to evaluate the performance of the proposed approach. The proposed method has been compared with a state-of-the-art pixel-domain algorithm in term of F-score and computing time.
Alternative Dielectric Fluids for Transformer Insulation System: Progress, Challenges, and Future Prospects

Ester-based dielectric fluids have gained widespread popularity for applications in high voltage apparatus. Synthetic and natural esters have been subjected to research for decades vis-à-vis mineral insulating oils around the world. Although many researchers favor the application of ester fluids, utilities are still uncertain and application of these alternatives remains a challenge. The intent of this survey is to present recent research progress and highlight the state of the art of key aspects that should be emphasized in future research. The contemporary research scenarios pertaining to the performance of ester fluids versus mineral oils, miscibility, and retrofilling of insulating fluids are discussed. In addition, pre-breakdown phenomena, usage of esters in on-load tap changers, environmental and fire resistance properties, and use of esters in cold climates are also discussed. Importantly, challenges and future aspects that should be investigated to improve the existing knowledge of ester dielectric fluids for applications in transformer technology are highlighted.
An approach to develop a secure and decentralized internet

In the modern world, the tech-giants are invading the users' privacy by accumulating their data. This stored data can be misused by the tech-giants or any third-party who gains the access to the data, the recent example being the Facebook-Cambridge Analytica data breach. Another issue with the current scenario is that people have to pay for internet tariff packs with fixed rates, while they use only a limited number of services. The overall concept of net neutrality is also being hampered. In this paper, we propose to develop a decentralized internet using the upcoming technology of Inter-Planetary File System along with IPv6 addressing to address the users' concerns about the data privacy as well as the unjustified internet tariff packs. Inter-Planetary File System (IPFS) is a protocol which builds on a Peer to Peer (P2P) network to create a content-addressable system used for sharing and storing content in a distributed file system. The users can securely access the services provided by various service providers. The system, coupled with blockchain technology, would help in eliminating the Internet Service Providers as well as protect the users' data from theft.
The use of national institute of health stroke scale and establishment of the regression model in acute cerebral thrombosis

Objective To assess the severity and the prognosis of the acute cerebral thrombosis using National institute of health stroke scale(NIHSS). Methods 441 patients with acute cerebral thrombosis in department of neurology were studied.NIHSS scores were calculated and the regression model was established.Results NIHSS score were from 2 to 31 and which was 9.94±4.51 in the 279 survivors and 19.72±6.07 in the 162 dead cases.The scores were significantly different between the two groups(P 0.01). The regression model was established by logistic regression.Conclusions The regression model of NIHSS score system could be used to predict the prognosis of acute cerebral thrombosis.
Association of homocysteine,methylenetetrahydrofolate reductase gene polymorphism with type 2 diabetes mellitus with cerebral infarction

Objective To evaluate the role of homocysteine,methylene tetrahydrofolate reductase(MTHFR)gene polymorphisms in type 2 diabetes mellitus with cerebral infarction.Methods By PCR-RFLP,MTHFR C677T mutation was detected in 42 type 2 diabetes mellitus with cerebral infarction and 41 type 2 diabetes mellitus with no complications as control.The serum homocysteine(Hcy)levels were measured by enzyme-labeled immunoassay(ELISA). Results The serum Hcy levels of type 2 diabetes mellitus with cerebral infarction was higher than type 2 diabetes mellitus with no complications.There was no significant difference of the frequency of MTHFR C677T gene between the two groups.In subject with serum folate ≤6.92 nmol/L,the frequency genotype TT and allele frequency T of type 2 diabetes mellitus with cerebral infarction was higher than that of control group(P0.05). Conclusions Diabetic patients with MTHFR gene C677T mutation is predisposed to developing cerebral infarction,and the high-normal serum folate may prevent the progression of cerebral infarction.
Measurement of the Upsilon(nS) cross section at CDF

Meier, Michael D. Ph.D., Purdue University, August 2016. Measurement of the Υ(nS) Cross Section at CDF. Major Professor: Matthew Jones. Since the bound bb̄ system was first discovered, researchers have been trying to explain the production mechanism for quarkonium to learn more about this system. Several different theories try to describe quarkonium production, and while these theories agree with experimental measurements of production rates, theoretical predictions for quarkonium polarization vary. Careful measurement of the Υ(nS) angular distribution along with the Υ(nS) cross section can provide insight into the quarkonium production mechanism. This analysis measures the Υ(nS) cross section and Υ(1S) polarization parameters.
fMRI Research of Acupuncture on Brain Regional Homogeneity in Resting-State of Patients with Ischemic Stroke

Objective:Taking healthy volunteers and patients with ischemic stroke as the research subjects,the research explored the central mechanisms of ischemic stroke from the regional homogeneity,aiming at providing evidences for acupuncture to treat ischemic stroke.Methods:BOLD-fMRI technology was adopted in the research.We chose 10 healthy volunteers(A group) and 9 patients with ischemic stroke(B group) as participants.The dynamic change of regional homogeneity of the whole brain for B groups was observed before and after treatment.Results:Before treatment,compared with healthy volunteers(A group),the abnormality of regional homogeneity occurred in many encephalic regions for B group.After treatment,compared with healthy volunteers(A group),regional homogeneity signal changed obviously in SG and cerebellum,M1,PMA,SMA and IPL for B group.Conclusion:Abnormality of regional homogeneity in the resting-state occurred in many encephalic regions of patients with ischemic stroke,SG was the main encephalic region which resulted in motor function injuries;M1,PMA,SMA and IPL were the critical regions for patients with ischemic stroke to accomplish reorganization and compensation of the brain function in the resting-state,which became the important focus for acupuncture treating patients with ischemic stroke.
A review based on the European integrated project "Qubit Applications (QAP)"

We perform a review of various approaches to the implementation of quantum memories, with an emphasis on activities within the quantum memory sub-project of the EU integrated project "Qubit Applications". We begin with a brief overview over different applications for quantum memories and different types of quantum memories. We discuss the most important criteria for assessing quantum memory performance and the most important physical requirements. Then we review the different approaches represented in "Qubit Applications" in some detail. They include solid-state atomic ensembles, NV centers, quantum dots, single atoms, atomic gases and optical phonons in diamond. We compare the different approaches using the discussed criteria.
Is Germany's GDP Trend-Stationary? A Measurement-with-Theory Approach

The time series properties of German GDP have been re-examined in recent research. Extending the sample to include GDP data from 1950 onwards, some researchers argued in favor of a trend-stationary rather than difference stationary representation of real log GDP. We show that this conclusion is based on an atheoretic trend model underlying the unit root tests. A simple linear trend model fails to take the post World-War II catch-up process properly into account. We use the Solow growth model to discriminate between transitional catch-up dynamics and long-run equilibrium growth. With the proper transformation of GDPdata, we are able to use standard unit root tests and find that both ADF and KPSS tests suggest a difference stationary model. This evidence is supported by non-standard unit root tests which allow for polynomial trend representations.