Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidrug-resistant Staphylococcus pettenkoferi isolated from cat in India

Background and Aim: Coagulase-negative staphylococci (CoNS) are considered to be one of the emerging pathogens in human and animals in recent times. Staphylococcus pettenkoferi, a novel pathogen under CoNS, is discovered in 2002 in humans with multiple clinical manifestations in various patients. To date, the pathogens have not yet been reported from any animals. The present study reported the first ever isolation, identification, and characterization of multidrug-resistant S. pettenkoferi from a cat with peritonitis in India. Materials and Methods: Peritoneal fluid was collected aseptically from 3 years old cat processed for bacteriological culture by standard techniques. Isolates were confirmed by BD Phoenix™ automated bacterial identification system and were subjected to plate and tube coagulase tests. All the isolates were tested for antimicrobial sensitivity profile by disc diffusion assay, extended-spectrum β-lactamase production by double disc diffusion assay, in vitro biofilm production ability by microtiter plate assay, and detection of virulence genes and mecA gene by polymerase chain reaction assay. Results: A total of five clonally expanded isolates of S. pettenkoferi were isolated from peritoneal fluid of the affected cat. All the isolates were resistant against 36 antimicrobial agents and were also methicillin-resistant staphylococci. Phenotypically, all the isolates were negative for biofilm production but were carrying multiple biofilm-producing genes (icaA, IS257, nuc, and mecA). Conclusion: Although S. pettenkoferi was previously reported once from animal (cat) environment, this is probably the first ever report of isolation of the organism directly from any animals. This is also probably the first report from any species in India.
A Virtual Phase-Lead Impedance Stability Control Strategy for the Maritime VSC–HVDC System

In the maritime VSC-HVDC system supplying power to passive network on the island, the dc side of the system is prone to oscillation and even instability, which is resulted from the negative incremental input resistance characteristic of the inverter station. First, a virtual phase-lead impedance stability control strategy aiming at rectifier station is proposed. This control strategy can effectively mitigate the dc-side oscillation of the VSC–HVDC system without affecting the load performance of the inverter station. Then, the dc impedance model of the VSC–HVDC system is built, including the output impedance of the rectifier station, dc cable impedance, and the input impedance of the inverter station. In addition, the oscillation mechanism of the VSC–HVDC system is analyzed by impedance-based Nyquist stability criterion. The reason why rectifier station fails to mitigate the dc-side oscillation using traditional virtual resistance stability control is that the output impedance of the rectifier station exhibits negative damping characteristic outside the control bandwidth of voltage outer loop. For this issue, the proposed control strategy can correct the output impedance of the rectifier station to exhibit positive damping characteristic outside the control bandwidth of voltage outer loop, thus mitigating the dc-side oscillation of the VSC–HVDC system. Finally, simulation and experiment results validate the proposed control strategy and analysis.
Draft Genome Sequence of Propionibacterium acnes subsp. elongatum Strain Asn12

Propionibacterium acnes, a non-spore-forming anaerobic Gram-positive bacterium, has been linked to a wide range of opportunistic human infections and conditions, most notably acne vulgaris. Here, we present the draft genome sequence of P. acnes subsp. ABSTRACT Propionibacterium acnes, a non-spore-forming anaerobic Gram-positive bacterium, has been linked to a wide range of opportunistic human infections and conditions, most notably acne vulgaris. Here, we present the draft genome sequence of P. acnes subsp. elongatum strain Asn12, isolated from spinal disc tissue (in the United Kingdom).
Rotor Configuration Which Reduces Copper Loss of Switched Reluctance Motors with Suppression of Torque Ripple and Input Current Ripple

Switched reluctance motors (SRMs) are attractive motors for electric vehicle propulsion because of their cost-effectiveness or robust structures. However, SRMs suffer from input current ripple and torque ripple. The authors have proposed derivation method of phase current profile which suppresses input current ripple and torque ripple and experimentally verified the suppression of both ripples. However, the rms value of derived phase current was much greater than square wave current, which is implemented widely, and increased copper loss. For the EV application, the rms value should be reduced. To break through the issue, the author addressed the characteristic that phase current profiles are shaped based on each reluctance profile, which is shaped by motor configuration, and noticed that phase current profile with low rms value may be derived by a novel rotor configuration. Therefore, this paper proposes a novel rotor configuration which can reduce the copper loss. The proposed rotor configuration is designed to satisfy the reluctance profile which is derived by previously proposed derivation method of phase current. This paper also presents the experiment for the evaluation of the reduction of copper loss.
A new path planning algorithm for moving a point object amidst unknown obstacles in a plane

A nonheuristic path planning for moving a point object, or mobile automation (MA), in a two-dimensional plane, amidst unknown obstacles, is considered. A path is to be generated, point by point, using only the local information, like the MA's current position and whether it is in contact with an obstacle. A path-planning algorithm to solve this problem is proposed. The algorithm is used to realize the smallest worst-case path length possible in its category. The procedure for the algorithm is presented with explanations. Its various characteristics, such as local cycle creation, worst-case path length, target reachability conditions, etc. are dealt with. Its performance is compared with that of the existing algorithms. Examples showing the operation of the algorithm are presented. <<ETX>>
A Computing Efficient Hardware Architecture for Sparse Deep Neural Network Computing

Convolutional Neural Networks (CNNs) have demonstrated significant performance in AI (artificial intelligence) systems. However, CNNs often have tens or even hundreds of neural layers with millions of parameters to achieve state-of-the-art performance, which hinders the deployment to some resource limited scenarios. Meanwhile, those parameters and data usually are sparse, which results in useless calculation as well as unbalanced calculation. To solve these problem, we propose a computing efficient hardware architecture. In order to decrease calculating redundancy, we filter zero-valued weights and zero-valued feature maps. To reduce redundant memory consumption, we propose a memory division and a data reuse mechanism. To resolve load imbalance, we implement a near-zero-cost scheduling switching strategy. Experimental results show that our architecture saves, on average, 22.6% memory times and 60.5% computing time over the state-of-the-art NN accelerator.
Wireless Power Transfer Using Longitudinally Asymmetrical Leaky-Wave Antenna

This paper experimentally demonstrates the wireless power transfer using two longitudinally asymmetrical leaky-wave antennas. Such a device is also useful for harvesting RF wireless energy in the environment. Based on the experiments, we used the longitudinally asymmetrical leaky-wave antenna operating in the microwave frequency 5.75 GHz. The experiment uses two antennas as the transmitter and receiver, and an RF rectifying circuit for DC conversion. The RF energy transmitted by one antenna array is picked up by another antenna array in the receiver section. This RF energy is passed through rectifying circuit to get a resultant voltage. The maximum voltage on load resistance achieved was recorded 2.6V.
Cryogenic Characterization of RF Low-Noise Amplifiers Utilizing Inverse-Mode SiGe HBTs for Extreme Environment Applications

The cryogenic performance of radiation-hardened radio-frequency (RF) low-noise amplifiers (LNAs) is presented. The LNA, which was originally proposed for the mitigation of single-event transients (SETs) in a radiation environment, uses inverse-mode silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) in its core cascode stages. In this prototype, the upper common-base SiGe HBT is configured in inverse mode for balanced RF performance and reduced SET sensitivity. In order to better exploit the inverse-mode LNAs in a variety of extreme-environment applications, the RF performance of the LNA was characterized using liquid nitrogen to evaluate cryogenic operation down to 78 K. While the SiGe LNA exhibits acceptable RF performance for all temperature conditions, there is a noticeable gain drop observed at 78 K compared to the conventional forward-mode design. This is attributed to the limited high-frequency performance of an inverse-mode SiGe HBT. As a guideline, compensation techniques, including layout modifications and profile optimization, are discussed for the mitigation of the observed gain degradation.
Genome Sequence of a Single-Stranded DNA Virus Identified in Gila Monster Feces

The Gila monster (Heloderma suspectum) is native to the Sonoran Desert. Metagenomic analyses of a Gila monster fecal sample revealed the presence of a small, circular, single-stranded DNA virus that is most closely related to a gemykrogvirus (family Genomoviridae) genome from caribou feces sharing 88% genome-wide pairwise identity. ABSTRACT The Gila monster (Heloderma suspectum) is native to the Sonoran Desert. Metagenomic analyses of a Gila monster fecal sample revealed the presence of a small, circular, single-stranded DNA virus that is most closely related to a gemykrogvirus (family Genomoviridae) genome from caribou feces sharing 88% genome-wide pairwise identity.
A Learning Method for a Daily Assistive Robot for Opening and Closing Doors Based on Simple Instructions

Remote-controlled daily assistive robots that can help handicapped individuals are expected to be a key technology in supporting aging communities. We present a method to simplify user instruction via a human-machine interface for robotic manipulation of various types of doors. Using our system, the user can teach the robot to open an unknown door by just a few clicks on an image generated from the robot's perspective. After the teaching stage, the robot is able to manipulate the door based on just a single user click on the position of the door handle, and an instruction determining if the door is to be opened or closed. The contributions of this paper to the research area are to (i) present a method to detect door candidates effectively from the point cloud of the surrounding environment using hypotheses on the plane and the shape of doors, to (ii) introduce a door feature to learn and remember the manipulation model, and to (iii) implement the system on a daily assistive robot and evaluate it. We tested our system on doors with various shapes and manipulation models, obtaining results supporting the effectiveness of our approach.
A Novel Method for Fabricating Graphene Sensors in Channel for Biomedical Applications

Graphene is a 2D material possessing extraordinary electrical, mechanical and optical properties which paves way for biomedical applications. A well-known method to fabricate graphene-based devices is depositing graphene using chemical vapor deposition and transferring it to any substrate using polymer support like PMMA (Polymethyl methacrylate) or PDMS (Polydimethylsiloxane). In this process multiple wetting and drying steps are required which compromises the quality of graphene. Also, PMMA leaves residues that are difficult to remove. Here, we report a novel method to fabricate graphene sensors inside PDMS channels. In this method graphene/copper stack is bonded on a PDMS surface using mechanical pressure and temperature. This PDMS layer is then bonded to another PDMS substrate containing microchannel. Etchant is flown through the microchannel to etch the copper and obtain graphene on PDMS. The graphene obtained on PDMS is characterized using optical images, scanning electron microscopy and Raman spectroscopy. This is an easy, simple to use and scalable method to obtain graphene sensors inside microchannels for biomedical applications.
On-line Compensation of Periodic Error in Resolver Signals for PMSM Drives

This paper proposes a novel method for online compensation of periodic position error in resolver signals based on model reference adaptive system (MRAS) and phase-locked loop (PLL) for the first time. Analytical expressions are derived to demonstrate the detrimental effects of periodic position error on the performance of PMSMs. It can be interpreted that periodic position error will lead to a distinction between stator currents fed back to current controller and real stator currents, generating current harmonics and torque ripples in PMSMs. It is proved that there exist fundamental and harmonic current vectors simultaneously due to periodic position error in resolver signals. The cross-product of fundamental and harmonic current vectors is implemented to obtain the information of position error. The principle of the novel method is elaborated, in which a reference model and an adaptive model are constructed. The reference model is based on coordinate transformation and PLL while the adaptive model is simple, containing only a coordinate transformation. Finally, the feasibility and reliability of the compensation method based on MRAS and PLL is verified by simulation and experiments.
A shop-level location algorithm based on CNN for crowdsourcing fingerprint

Recent years, the use of widely covered Wi-Fi signal to achieve accurate indoor positioning has become a research hotspot. The Wi-Fi Positioning Algorithm based on the existing RF Fingerprint can obtain high positioning accuracy, but there is a big shortage of large deployment and maintenance cost. In order to reduce the cost of Wi-Fi fingerprints collection, this paper utilizes the RSS collected by the consumers using the mobile phone for electronic payment, constructs the low cost crowdsourcing Wi-Fi RF Fingerprint, and realizes the shop-level accurate positioning with CNN. Based on the feature of CNN extraction from local to global, this paper constructs a characteristic group which includes signal intensity, user transaction time and shop information. The statistics of each feature in the statistical interval are obtained by using the interval of 1 to 23 days before the current time. The Min-Max normalization of the statistical value is to avoid inconsistencies in the data distribution caused by the loss of data. In this way, the feature map of window length and statistic feature is constructed, the different feature groups of Wi-Fi and Shop are used as input matrices of multiple CNN, then the other manual features are combined to train a CNN positioning classifier to realize the position estimation of shop-level. A large number of experimental data tests show that the proposed algorithm can obtain higher positioning accuracy by 91% compared with the positioning algorithm based on LR(Logistic Regression), AdaBoost and XGBoost.
Cartographies of the Legal World. Rise and Challenges of Visual Legal Analytics

In recent years, together with the adoption of data driven approaches and the development of computational heuristics, visualization has become part of a process that is gradually changing methods of social sciences. In this paper we present some applications of visual computing in the legal science and practice. After a brief introduction to the rise and challenges of what we define as Visual Legal Analytics, we briefly sketch the results of three research projects dealing with visualization at the boundaries between law and computer science discussing their objectives, advantages and perspectives.
Analytic Signal Decomposition Using M-Channel Signal Matched Filter Bank

Current signal decomposition and TFA methods like STFT (uses pre-defined computation window), EMD (not completely intrinsic, uses interpolator), PCA and ICA (both statistical based) have inherent limitations of decomposing signals into uncorrelated narrow bands and are not completely signal adaptive. In this paper, we propose an algorithm for a given data case based on signal matched filter banks having signal adaptive feature to decompose and analyze any time varying signal into uncorrelated narrow bands evaluated at immediate arrival of data. To achieve this, we have modified synthesis side of SMFB and used analytic counter-part of the original signal.
An Efficient and Certificateless Conditional Privacy-Preserving Authentication Scheme for Wireless Body Area Networks Big Data Services

To gurantee the security and privacy of the patient’s physiological data in wirelss body area networks (WBANs), it is important to secure the communication between the personal digital assistance held by the WBANs client and the application provider, such as a medical institution, physician, or hospital. These physiological data are so large, traditional methods cannot process them efficiently and securely, thus big data services are needed. In the existing anonymous authentication schemes for WBANs, most of them did not consider when a malicious WBANs client sends a false message to cheat the application provider and cause a medical accident, how to trace the real identity of this client and punish him. In order to overcome the above issues, an efficient and certificateless conditional privacy-preserving authentication scheme for WBANs big data services is proposed in this paper. Due to the proposed scheme is based on big data, the capabilities of the proposed WBANs system is better than traditional WBANs. To improve the performance, the proposed scheme supports batch authentication of multiple clients, which significantly reduces the computational overhead of the application provider. Moreover, the proposed scheme provides anonymity, un-linkability, mutual authentication, traceability, session key establishment, forward secrecy, and attack resistance. The simulation experiment demonstrates that the proposed scheme for WBANs needs less computational time than recent schemes.
Shoreline Monitoring Using WorldView-2 Images Along the Coast of Sendai after the Tohoku Tsunami in Presence of Foam

This study focuses on the monitoring of the shoreline in the bay of Sendai with high spatial resolution satellite images, after the Tohoku Tsunami, with the presence of foam which usually leads to an invalid shoreline detection. The monitoring uses multispectral classifications: Several distances for multispectral classifications are compared: Euclidian distance (ED), Spectral Angle Mapper (SAM) or Mahalanobis Distance (MAH) in order to classify pixels into three classes: land, foam and water. The fusion between water and foam classes shows a separation between water and land. Results of each method are compared to the validation data. This comparison is operated with WorldView-2 multispectral images (8 bands, 2 m resolution) acquired before and after the Tohoku tsunami. The MAH is the distance delivering the best results.
Miniaturized Wideband Coupler for 60-GHz Band in 65-nm CMOS Technology

A compact and wideband coupler for the 60-GHz frequency band applications using 65-nm CMOS technology is presented. By meandering the coupled line, a miniaturized coupler can be obtained. The proposed coupler achieves a measured insertion loss between the input port and the through port (<inline-formula> <tex-math notation="LaTeX">${S}_{21}$ </tex-math></inline-formula>) of 6.3 dB and between the input port and the coupled port (<inline-formula> <tex-math notation="LaTeX">${S}_{31}$ </tex-math></inline-formula>) of 5.6 dB at 60 GHz. The measured amplitude imbalance and phase difference are 0.75 dB and 92°, respectively, at 60 GHz. Overall, the proposed coupler can achieve a measured amplitude imbalance of <1 dB, phase imbalance of <4°, return loss, and isolation better than 13 and 20 dB, respectively, from 50 to 67 GHz. In addition, the proposed coupler occupies a core area of only <inline-formula> <tex-math notation="LaTeX">$156\,\,\mu \text{m}\,\,\times 157\,\,\mu \text{m}$ </tex-math></inline-formula> (0.024 mm<sup>2</sup>).
Submodule Level Power Loss Balancing Control for Modular Multilevel Converters

This paper investigates the power loss imbalance in Modular Multilevel Converters (MMCs) with Nearest Level Modulation (NLM) resulting from the low switching frequency operation and the parameter mismatch. The imbalance might pose a challenge to the cooling system design as well as the reliability of the MMC. To address this problem, a submodule-level power loss balancing control (PLBC) is proposed. Compared with the normal control strategy without the thermal balancing, this method is able to decrease the degree of power loss imbalance among submodules (SMs) to at least half without deteriorating the performance of the converter efficiency and the capacitor voltage ripple. The effectiveness of the proposed control is validated by simulations.
Emotion Analysis Architecture Based on Face and Physiological Sensing Applied with Flight Simulator

This work presents an architecture as an important contribution regarding to emotional events along tasks based on flight simulations. This architecture considered eight beginner users of flight simulator (n = 8) while they execute a simulated flight according with the basic concepts of visual flight rules (VFR). The acquired physiological sensing were: heart rate (HR), electroencephalography (EEG) and galvanic skin response (GSR). One small camera was also used to record the users’ face in order to extract, after the post-processing, the emotions of the user during the flight. The considered emotions were: happy, sad, angry, surprised, scared and disgust. Initial analysis of the GSR signals shown that the takeoff task presented 13% more variability (or emotional events) between the climb and approach tasks together; and in the same way, the landing task presented 16% more variability between the climb and approach tasks together, what shows the importance of these researches in flight safety context, mainly in these critical phases.
Analysis of Airgap Field Modulation Principle of Flux Guides

This paper is extended to characterize the performances of machines featuring flux guides based on the general airgap field modulation theory (GAFMT). The magnetomotive force (MMF) modulation operator of variable flux guides is proposed to describe the detailed modulation behavior on the primitive magnetizing MMF distribution, and two popular flux guides topologies, namely synchronous reluctance machine (SynRM) and brushless doubly fed reluctance machine (BDFRM) are described from the perspective of airgap field modulation mechanism. Three kinds of harmonic components namely $\pmb{pf},(\pmb{lN}_{\pmb{MB}}+\pmb{pf})$ and $(\pmb{lN}_{\pmb{MB}}-\pmb{pf})$ influenced by corresponding magnetic field conversion factors contribute to the effective flux density and torque production. In addition, electromagnetic performances such as airgap flux density, torque density, cogging torque and inductance characteristics are correlated with their initial key topological parameters by certain analytical expressions and compared fairly between the SynRM and BDFRM. The agreement among the theoretical analysis and the 2-D finite element analysis (FEA) verifies the effectiveness of the proposed analysis.