nils-herrmann/clean_scirepeval huggingface dataset:
A Gate Drive With Power Over Fiber-Based Isolated Power Supply and Comprehensive Protection Functions for 15-kV SiC MOSFET

This paper presents a 15kV silicon carbide (SiC) MOSFET gate drive, which features high common-mode (CM) noise immunity, small size, light weight, and robust yet flexible protection functions. To enhance the gate-drive power reliability, a power over fiberbased isolated power supply is designed to replace the traditional design based on isolation transformer. It delivers the gate-drive power by laser light via optical fiber over a long distance (>1 m), so a high isolation voltage (>20 kV) is achieved, and the circuit size and weight are reduced. More importantly, it eliminates the parasitic CM capacitance coupling the power stage and control stage, and thus eradicates the control signal distortion caused by high dv/dt in switching transients of the high-voltage SiC devices. In addition, the gate-drive circuit design integrates comprehensive protection functions, including the overcurrent protection, undervoltage/overvoltage lockout, active miller clamping, soft turn off, and fault report. The overcurrent protection responds within 400 ns. The experimental results from a 15kV double-pulse tester are presented to validate the design.
A constant output current three-phase diode bridge rectifier employing a novel "Electronic Smoothing Inductor"

This paper presents an improvement of the well-known conventional three-phase diode bridge rectifier with dc output capacitor. The proposed circuit increases the power factor (PF) at the ac input and reduces the ripple current stress on the smoothing capacitor. The basic concept is the arrangement of an active voltage source between the output of the diode bridge and the smoothing capacitor which is controlled in a way that it emulates an ideal smoothing inductor. With this the input currents of the diode bridge which usually show high peak amplitudes are converted into a 120/spl deg/ rectangular shape which ideally results in a total PF of 0.955. The active voltage source mentioned before is realized by a low-voltage switch-mode converter stage of small power rating as compared to the output power of the rectifier. Starting with a brief discussion of basic three-phase rectifier techniques and of the drawbacks of three-phase diode bridge rectifiers with capacitive smoothing, the concept of the proposed active smoothing is described and the stationary operation is analyzed. Furthermore, control concepts as well as design considerations and analyses of the dynamic systems behavior are given. Finally, measurements taken from a laboratory model are presented.
Brain disorders and the biological role of music

Despite its evident universality and high social value, the ultimate biological role of music and its connection to brain disorders remain poorly understood. Recent findings from basic neuroscience have shed fresh light on these old problems. New insights provided by clinical neuroscience concerning the effects of brain disorders promise to be particularly valuable in uncovering the underlying cognitive and neural architecture of music and for assessing candidate accounts of the biological role of music. Here we advance a new model of the biological role of music in human evolution and the link to brain disorders, drawing on diverse lines of evidence derived from comparative ethology, cognitive neuropsychology and neuroimaging studies in the normal and the disordered brain. We propose that music evolved from the call signals of our hominid ancestors as a means mentally to rehearse and predict potentially costly, affectively laden social routines in surrogate, coded, low-cost form: essentially, a mechanism for transforming emotional mental states efficiently and adaptively into social signals. This biological role of music has its legacy today in the disordered processing of music and mental states that characterizes certain developmental and acquired clinical syndromes of brain network disintegration.
Efficient visual odometry and mapping for Unmanned Aerial Vehicle using ARM-based stereo vision pre-processing system

Visual odometry and mapping methods can provide accurate navigation and comprehensive environment (obstacle) information for autonomous flights of Unmanned Aerial Vehicle (UAV) in GPS-denied cluttered environments. This work presents a new light small-scale low-cost ARM-based stereo vision pre-processing system, which not only is used as onboard sensor to continuously estimate 6-DOF UAV pose, but also as onboard assistant computer to pre-process visual information, thereby saving more computational capability for the onboard host computer of the UAV to conduct other tasks. The visual odometry is done by one plugin specifically developed for this new system with a fixed baseline (12cm). In addition, the pre-processed infromation from this new system are sent via a Gigabit Ethernet cable to the onboard host computer of UAV for real-time environment reconstruction and obstacle detection with a octree-based 3D occupancy grid mapping approach, i.e. OctoMap. The visual algorithm is evaluated with the stereo video datasets from EuRoC Challenge III in terms of efficiency, accuracy and robustness. Finally, the new system is mounted and tested on a real quadrotor UAV to carry out the visual odometry and mapping task.
A micro-UAV-borne system for radar imaging: A feasibility study

Radar mounted onboard micro-UAV is an early stage technology and its potentiality is far from being focused, even if radar sensors having costs compatible with micro-UAV are currently developed. As a contribution to this topic, this paper describes a radar-equipped hexacopter assembled thanks to complementary skills available at IREA and DII. In order to test the operation mode of the system as well as to investigate its target detection and localization capabilities, a feasibility experiment has been carried out in December 2016. The results of this flight campaign are presented, in terms of both raw data and images obtained by means of an ad-hoc data processing approach. These results provide an encouraging preliminary proof of the achievable outcomes.
Enhancing geometric maps through environmental interactions

The deployment of rescue robots in real operations is becoming increasingly common thanks to recent advances in AI technologies and high performance hardware. Rescue robots can now operate for extended period of time, cover wider areas and process larger amounts of sensory information making them considerably more useful during real life threatening situations, including both natural or man-made disasters. In this thesis we present results of our research which focuses on investigating ways of enhancing visual perception for Unmanned Ground Vehicles (UGVs) through environmental interactions using different sensory systems, such as tactile sensors and wireless receivers. We argue that a geometric representation of the robot surroundings built upon vision data only, may not suffice in overcoming challenging scenarios, and show that robot interactions with the environment can provide a rich layer of new information that needs to be suitably represented and merged into the cognitive world model. Visual perception for mobile ground vehicles is one of the fundamental problems in rescue robotics. Phenomena such as rain, fog, darkness, dust, smoke and fire heavily influence the performance of visual sensors, and often result in highly noisy data, leading to unreliable or incomplete maps. We address this problem through a collection of studies and structure the thesis as follow: Firstly, we give an overview of the Search & Rescue (SAR) robotics field, and discuss scenarios, hardware and related scientific questions. Secondly, we focus on the problems of control and communication. Mobile robots require stable communication with the base station to exchange valuable information. Communication loss often presents a significant mission risk and disconnected robots are either abandoned, or autonomously try to back-trace their way to the base station. We show how non-visual environmental properties (e.g. the WiFi signal distribution) can be efficiently modeled using probabilistic active perception frameworks based on Gaussian Processes, and merged into geometric maps so to facilitate the SAR mission. We then show how to use tactile perception to enhance mapping. Implicit environmental properties such as the terrain deformability, are analyzed through strategic glances and touches and then mapped into probabilistic models. Lastly, we address the problem of reconstructing objects in the environment. We present a technique for simultaneous 3D reconstruction of static regions and rigidly moving objects in a scene that enables on-the-fly model generation. Although this thesis focuses mostly on rescue UGVs, the concepts presented can be applied to other mobile platforms that operates under similar circumstances. To make sure that the suggested methods work, we have put efforts into design of user interfaces and the evaluation of those in user studies.
LatticeRnn: Recurrent Neural Networks Over Lattices

We present a new model called LATTICERNN, which generalizes recurrent neural networks (RNNs) to process weighted lattices as input, instead of sequences. A LATTICERNN can encode the complete structure of a lattice into a dense representation, which makes it suitable to a variety of problems, including rescoring, classifying, parsing, or translating lattices using deep neural networks (DNNs). In this paper, we use LATTICERNNs for a classification task: each lattice represents the output from an automatic speech recognition (ASR) component of a spoken language understanding (SLU) system, and we classify the intent of the spoken utterance based on the lattice embedding computed by a LATTICERNN. We show that making decisions based on the full ASR output lattice, as opposed to 1-best or n-best hypotheses, makes SLU systems more robust to ASR errors. Our experiments yield improvements of 13% over a baseline RNN system trained on transcriptions and 10% over an nbest list rescoring system for intent classification.
Minimizing nfa's and regular expressions

We show inapproximability results concerning minimization of nondeterministic finite automata (nfa’s) as well as regular expressions relative to given nfa’s, regular expressions or deterministic finite automata (dfa’s). We show that it is impossible to efficiently minimize a given nfa or regular expression with n states, transitions, resp. symbols within the factor o(n), unless P = PSPACE. Our inapproximability results for a given dfa with n states are based on cryptographic assumptions and we show that any efficient algorithm will have an approximation factor of at least n poly(log n) . Our setup also allows us to analyze the minimum consistent dfa problem.  Automata and Formal Languages, Computational Complexity, Approximability
Adoption and effectiveness of electronic banking in Kenya

1567-4223/$ see front matter 2009  A doi:10.1016/j.elerap.2009.12.003 * Corresponding author.  joyce.gikandi@pg.canterbury.ac.nz A global explosion in the use of electronic commerce has been witnessed in recent times with the monetary value of products and services exchanged electronically being estimated at above US$ 7 trillion in the year 2004. Research has followed the same pattern especially in investigating factors influencing the adoption and effectiveness of e-commerce in retail businesses. However, little has been done to establish these factors in electronic banking (e-banking) in developing countries. The aim of this research was to investigate the factors influencing the adoption and effectiveness of e-banking in retail banking. Initial and follow-up surveys were carried out in the years 2005 and 2009, respectively, which involved banks controlling approximately 90% of formal retail banking in Kenya. The follow-up survey was meant to monitor trends between the periods of the two surveys.  varied across the period.
A 40-to-64 Gb/s NRZ Transmitter With Supply-Regulated Front-End in 16 nm FinFET

A 3-tap 64 Gb/s NRZ transmitter using a quad-rate architecture is designed in 16 nm FinFET. The design incorporates circuit techniques and topologies that take into account device properties specific to FinFET process. A 4:1 MUX consisting of static CMOS pulse generators and a tailless CML multiplexing stage is used at the final stage of serialization. An on-chip regulator provides power to the pulse generators and CMOS clock buffers. A phase error correction circuit corrects the phase errors of the four-phase clocks generated by an LC-PLL. The transmitter achieves 800 mV-ppd with 150 fs RJ while consuming 225 mW at 64 Gb/s.
Large-Scale Data Analysis Using Heuristic Methods

Estimation and modelling problems as they arise in many data analysis areas often turn out to be unstable and/or intractable by standard numerical methods. Such problems frequently occur in fitting of large data sets to a certain model and in predictive learning. Heuristics are general recommendations based on practical statistical evidence, in contrast to a fixed set of rules that cannot vary, although guarantee to give the correct answer. Although the use of these methods became more standard in several fields of sciences, their use for estimation and modelling in statistics appears to be still limited. This paper surveys a set of problem-solving strategies, guided by heuristic information, that are expected to be used more frequently. The use of recent advances in different fields of large-scale data analysis is promoted focusing on applications in medicine, biology and technology.
Enabling Technologies on Cloud of Things for Smart Healthcare

Nowadays research is heading towards the integration of cloud computing and Internet of Things thus creating a Cloud of Things (CoT). This combination generates a new paradigm for pervasive and ubiquitous computing. However, reliable CoT-based services, particularly, highly delay-sensitive services, such as, healthcare, require energy-efficient CoT architectures. Considerable efforts have been proposed to improve the efficiency of CoT architectures. This paper analyses CoT architectures and platforms, as well as the implementation of CoT in the context of smart healthcare. Subsequently, the paper explains some related issues of CoT, including the lack of standardization. Moreover, it focuses on energy efficiency with an in depth analysis of the most relevant proposals available in the literature. An evaluation of all the energy efficiency solutions investigated in this paper shows there is still a need to improve energy efficiency, especially regarding QoS and performance.
Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometry

For the autonomous navigation of mobile robots, robust and fast visual localization is a challenging task. Although some end-to-end deep neural networks for 6-DoF Visual Odometry (VO) have been reported with promising results, they are still unable to solve the drift problem in long-range navigation. In this paper, we propose the deep global-relative networks (DGRNets), which is a novel global and relative fusion framework based on Recurrent Convolutional Neural Networks (RCNNs). It is designed to jointly estimate global pose and relative localization from consecutive monocular images. DGRNets include feature extraction sub-networks for discriminative feature selection, RCNNs-type relative pose estimation subnetworks for smoothing the VO trajectory and RCNNs-type global pose regression sub-networks for avoiding the accumulation of pose errors. We also propose two loss functions: the first one consists of Cross Transformation Constraints (CTC) that utilize geometric consistency of the adjacent frames to train a more accurate relative sub-networks, and the second one is composed of CTC and Mean Square Error (MSE) between the predicted pose and ground truth used to train the end-to-end DGRNets. The competitive experiments on indoor Microsoft 7-Scenes and outdoor KITTI dataset show that our DGRNets outperform other learning-based monocular VO methods in terms of pose accuracy.
30-100-GHz inductors and transformers for millimeter-wave (Bi)CMOS integrated circuits

Silicon planar and three-dimensional inductors and transformers were designed and characterized on-wafer up to 100 GHz. Self-resonance frequencies (SRFs) beyond 100 GHz were obtained, demonstrating for the first time that spiral structures are suitable for applications such as 60-GHz wireless local area network and 77-GHz automotive RADAR. Minimizing area over substrate is critical to achieving high SRF. A stacked transformer is reported with S/sub 21/ of -2.5 dB at 50 GHz, and which offers improved performance and less area (30 /spl mu/m/spl times/30 /spl mu/m) than planar transformers or microstrip couplers. A compact inductor model is described, along with a methodology for extracting model parameters from simulated or measured y-parameters. Millimeter-wave SiGe BiCMOS mixer and voltage-controlled-oscillator circuits employing spiral inductors are presented with better or comparable performance to previously reported transmission-line-based circuits.
Heterogeneous chip multiprocessor architectures for big data applications

Emerging big data analytics applications require a significant amount of server computational power. The costs of building and running a computing server to process big data and the capacity to which we can scale it are driven in large part by those computational resources. However, big data applications share many characteristics that are fundamentally different from traditional desktop, parallel, and scale-out applications. Big data analytics applications rely heavily on specific deep machine learning and data mining algorithms, and are running a complex and deep software stack with various components (e.g. Hadoop, Spark, MPI, Hbase, Impala, MySQL, Hive, Shark, Apache, and MangoDB) that are bound together with a runtime software system and interact significantly with I/O and OS, exhibiting high computational intensity, memory intensity, I/O intensity and control intensity. Current server designs, based on commodity homogeneous processors, will not be the most efficient in terms of performance/watt for this emerging class of applications. In other domains, heterogeneous architectures have emerged as a promising solution to enhance energy-efficiency by allowing each application to run on a core that matches resource needs more closely than a one-size-fits-all core. A heterogeneous architecture integrates cores with various micro-architectures and accelerators to provide more opportunity for efficient workload mapping. In this work, through methodical investigation of power and performance measurements, and comprehensive system level characterization, we demonstrate that a heterogeneous architecture combining high performance big and low power little cores is required for efficient big data analytics applications processing, and in particular in the presence of accelerators and near real-time performance constraints.
Soft-tissue lesions: when can we exclude sarcoma?

A wide spectrum of space-occupying soft-tissue lesions may be discovered on MRI studies, either as incidental findings or as palpable or symptomatic masses. Characterization of a lesion as benign or indeterminate is the most important step toward optimal treatment and avoidance of unnecessary biopsy or surgical intervention.



The systemic MRI interpretation approach presented in this article enables the identification of cases in which sarcoma can be excluded.
Smart Grid Cybersecurity: Standards and Technical Countermeasures

Cybersecurity in smart grids is of critical importance given the heavy reliance of modern societies on electricity and the recent cyberattacks that resulted in blackouts. The evolution of the legacy electric grid to a smarter grid holds great promises but also comes up with an increasesd attack surface. In this article, we review state of the art developments in cybersecurity for smart grids, both from a standardization as well technical perspective. This work shows the important areas of future research for academia, and collaboration with government and industry stakeholders to enhance smart grid cybersecurity and make this new paradigm not only beneficial and valuable but also safe and secure.
An Iterative Spanning Forest Framework for Superpixel Segmentation

Superpixel segmentation has emerged as an important research problem in the areas of image processing and computer vision. In this paper, we propose a framework, namely Iterative Spanning Forest (ISF), in which improved sets of connected superpixels (supervoxels in 3D) can be generated by a sequence of Image Foresting Transforms. In this framework, one can choose the most suitable combination of ISF components for a given application - i.e., i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF-based methods to illustrate different choices for those components. These methods are compared with a number of state-of-the-art approaches with respect to effectiveness and efficiency. Experiments are carried out on several datasets containing 2D and 3D objects with distinct texture and shape properties, including a high-level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show ISF-based methods rank consistently among the best for all datasets.
Energy-Efficient Tracking and Localization of Objects in Wireless Sensor Networks

The energy-efficient tracking and precise localization of continuous objects have long been key issues in research on wireless sensor networks (WSNs). Among various techniques, significant results are reported from applying a clustering-based object tracking technique, which benefits the energy-efficient and stable network in large-scale WSNs. As of now, during the consideration of large-scale WSNs, a continuous object is tracked by using a static clustering-based approach. However, due to the restriction of global information sharing among static clusters, tracking at the boundary region is a challenging issue. This paper presents a complete tracking and localization algorithm in WSNs. Considering the limitation of static clusters, an energy-efficient incremental clustering algorithm followed by Gaussian adaptive resonance theory is proposed at the boundary region. The proposed research is allowed to learn, create, update, and retain clusters incrementally through online learning to adapt to incessant motion patterns. Finally, the Trilateration algorithm is applied for the precise localization of dynamic objects throughout the sensor network. The performance of the proposed system is evaluated through simulation results, demonstrating its energy-efficient tracking and stable network.
An IDMS approach towards privacy and new requirements in IoT

Identities are known as the most sensitive information. With the increasing number of connected objects and identities (a connected object may have one or many identities), the computing and communication capabilities improved to manage these connected devices and meet the needs of this progress. Therefore, new IoT Identity Management System (IDMS) requirements have been introduced. In this work, we suggest an IDMS approach to protect private information and ensures domain change in IoT for mobile clients using a personal authentication device. Firstly, we present basic concepts, existing requirements and limits of related works. We also propose new requirements and show our motivations. Next, we describe our proposal. Finally, we give our security approach validation, perspectives, and some concluding remarks.