nils-herrmann/clean_scirepeval huggingface dataset:
A Family of PWM Control Strategies for Single-Phase Quasi-Switched-Boost Inverter

This paper proposed a novel family of pulsewidth modulation (PWM) strategies for single-phase quasi-switched boost inverter (qSBI). By combining shoot-through (ST) mode in the inverter's switches and the turning-on state of an additional switch, the qSBI produced a high voltage gain without adding any passive components. Compared to the conventional PWM strategy for the same input and output voltage gain, the introduced PWM strategies for qSBI could reduce voltage stress across semiconductors and capacitor with the following additional merits: having smaller high-frequency inductor current and capacitor voltage ripples, using high modulation index with low ST duty cycle, and having higher efficiency. Circuit analysis, operating theories, and simulation results of the single-phase qSBI with the introduced PWM5 strategy are shown. A 500-W laboratory prototype was constructed and the effectiveness of the introduced PWM strategy was validated. The qSBI with the proposed PWM strategies is suitable for applications where the required voltage gain lies between 2 and 3.
Ambient-RF-energy-harvesting sensor node with capacitor-leakage-aware duty cycle control

In this paper, we present a software control method that maximizes the sensing rate of wireless sensor networks (WSN) that are solely powered by ambient RF power. Different from all other energy harvesting WSN systems, RF powered systems present a new challenge for the energy management. A WSN node repeatedly charges and discharges at short intervals depending on the energy intake. A capacitor is used for an energy storage in the energy harvesting system because of its efficient charge and discharge performance and infinite recharge cycles. When this charging time is too short, the node is more likely to experience an energy shortage. On the contrary, if it is too long, more energy is lost because of the leakage in the capacitor. Therefore, we implemented an adaptive duty cycle control scheme that is optimized for RF energy harvesting. This method maximizes the sensing rate considering the leakage problem, a factor that has never previously been studied in this context. Our control scheme improves efficiency by aggregate evaluation of the operation reliability and leakage reduction.
Centralized and Distributed Energy Efficiency Designs in Wireless Backhaul HetNets

This paper studies the joint design of downlink transmit beamforming and power allocation in two-tier wireless backhaul small cell heterogeneous networks. We consider reverse time division duplexing combined with equal spectrum splitting between two tiers for interference mitigation. We formulate a constrained optimization problem with the objective of maximizing the proposed access energy efficiency, defined by the ratio of the sum achievable rate at the users to the overall consumed power, where the power consumption model includes the adaptive decoding power. The formulated problem is non-convex and generally NP-hard. To solve it, we first apply the high-complexity branch-and-bound algorithm to find the global optimal solution. Then, we develop a lower complexity algorithm which iteratively solves the convex approximated problem until convergence. Compared with the conventional methods, this algorithm converges faster to a solution that is very close to the global optimal solution achieved by the branch-and-bound approach. Finally, we exploit the framework of the alternating direction method of multipliers on the convex approximated problem to develop a distributed algorithm. Numerical results are obtained to show the improvement of our proposed model with much better power conservation compared with the different design of fixed circuit power assignment.
A study on link quality in single hop sensor networks with Brownian motion

Wireless Sensor Networks (WSNs) play as a key important role in Internet of things era, they provide a lot of utilized applications deployed in quite different environments. Beside its clear benefits, a WSN has to face with several challenges which come from a practical environments such as interference phenomenon, energy limitation, transmission constraints or mobility model. Especially, several WSNs acted in underwater, micro-sensors or human being environments introduce new limitations of network performance due to unpredicted movement of nodes. To exploit this effect, this study considers a link quality issue as in throughput aspect of single hop Brownian Wireless Sensor Networks through analysis and numerical verification methods. The relationship of link throughput with other real conditions such as movement specifications and packet sizes are delivered to reach optimal transmission performance.
A Robust Unsupervised Feature Learning Framework Using Spatial Boosting Networks

To boost up power of unsupervised feature learning and deep learning, there has been a great effort in optimizing network structure to learn more efficient high level features. It is crucial for a network to have a sufficient amount of learnable parameters yet still be able to capture in variances in data. In this paper, the authors propose spatial boosting networks, which employ convolutional feature learning networks as learning components. Each component in a network is assigned to a certain spatial region. This allows the network learn more adaptive features for each region. In order to make spatial boosting networks to capture relationship between regions of the visual field, we also propose convolutional pooling procedure. By expanding pooling scope into overlapping regions, we expect the features pooled in higher level to be more robust to noises and more invariant to transformation. Experiments show that using spatial boosting networks boosts up accuracy up to 3% from conventional approaches in standard datasets CIFAR and STL. Moreover, these results are competitive in comparison with other methods by using only a basic feature learning algorithm.
Design considerations for a PEBB-based Marx-topology ILC klystron modulator

The concept of Power Electronic Building Blocks (PEBBs) has its origin in the U.S. Navy during the last decade of the past century. As compared to a more conventional or classical design approach, a PEBB-oriented design approach combines various potential advantages such as increased modularity, high availability, and simplified serviceability. This relatively new design paradigm for power conversion has progressively matured since then and its underlying philosophy has been clearly and successfully demonstrated in a number of real-world applications. Therefore, this approach has been adopted here to design a Marx-topology modulator for an International Linear Collider (ILC) environment where easy serviceability and high availability are crucial. This paper describes various aspects relating to the design of a 32-cell Marx-topology ILC klystron modulator. The concept of nested droop correction is introduced and illustrated. Several design considerations including cosmic ray withstand, power cycling capability, fault tolerance, etc., are discussed. Details of the design of a Marx cell PEBB are included.
An Absolute QoS Framework for Loss Guarantees in Optical Burst-Switched Networks

In order to meet the requirements of real-time applications, Optical Burst Switched backbone networks need to provide quantitative edge-to-edge loss guarantees to traffic flows. For this purpose, there have been several proposals based on the relative differentiation quality of service (QoS) model. However, this model has an inherent difficulty in communicating information about internal network states to the edge in a timely manner for making admission control decisions. In this paper, we propose an absolute QoS framework to overcome this difficulty. The key idea is to offer quantitative loss guarantees at each hop using a differentiation mechanism and an admission control mechanism. The edge-to-edge loss requirement is then translated into a series of small per-node loss probabilities that are allocated to the intermediate core nodes. The framework includes a preemptive differentiation scheme, a node-based admission control scheme and an edge-to-edge reservation scheme. The schemes are analyzed and evaluated through simulation. It is shown that the framework can effectively offer quantitative edge-to-edge loss guarantees under various traffic conditions.
Comparison of a PWM and a hybrid force control for a pneumatic actuator using on/off solenoid valves

This paper presents a new predictive hybrid control law for the control of electro-pneumatic systems using solenoid valves. The method is based on a predictive model of the mass flow rate of the valves. In order to evaluate this strategy, a comparison study in simulation has been performed with a classical PWM control for a force tracking problem. The results show that the accuracy in steady state but also the dynamic behavior of the pressures are better in the case of the hybrid control than the PWM control. The validity of the theory has been verified through simulations.
A new approach of stochastic computing for arithmetic functions in wideband RF transceivers

This paper presents a new approach to approximate various complex arithmetic functions for applications in wideband RF transceivers by using stochastic computing based on the piecewise linear (PWL) approximation. The optimized design parameters for PWL approximation are chosen by an optimization algorithm targeting the best tradeoff between hardware complexity and approximation accuracy. Then, this approximation is implemented by stochastic logic circuits. The proposed approach outperforms previous works based on Maclaurin expansions, Bernstein polynomial and finite-state-machine implementations while achieve similar computation accuracy.
Bit-Error-Rate Performance Enhancement of All-Optical Clock Recovery at 42.66 Gb/s Using Passive Prefiltering

In this letter, we demonstrate the bit-error-rate (BER) performance enhancement of an all-optical clock recovery device at 42.66 Gb/s using a prefiltering operation in front of a self-pulsating semiconductor laser. The prefilter is composed of a simple passive fiber-Bragg-grating-based Fabry-Perot bandpass filter. The assessment is obtained thanks to BER measurement using a data remodulation technique.
A software-defined model for IoT clusters: Enabling applications on demand

Sensors and smart devices have become in-dispensable elements in the Internet of Things (IoT) systems. Collectively, they interconnect in vast number and provide massive amount of data about themselves and their physical environments. More importantly, numerous IoT services are envisaged to serve us in various capacities from personal health, smart homes to smart cities and critical infrastructures. Current IoT systems are mostly application-specific, rigidly configured and non-programmable and hence their applications are limited and difficult to reuse. The challenge is in handling a large number of sensors/IoT devices, many with limited capability, into intelligent, useful services on-demand. Software-defined concept of Software-Defined Networking (SDN) and software implementation of networked devices of Network Functions Virtualization (NFV) promise an effective solution to the challenge. This paper proposes a software-defined IoT (SD-IoT) model through leveraging these two emerging SDN and NFV technologies. In particular, the paper presents the design of a streamline SD-IoT controller, a lightweight and reconfigurable software-defined virtual sensor (SD-VSensor) to represent its underlying sensors, and a communication protocol (S-MANAGE) between them. The implementation results demonstrate the feasibility and efficiency of the proposed architecture.
Combining GOR techniques with support vector machines for protein secondary structure prediction

We propose a novel approach to predict protein secondary structure by combining different types of GOR (Garnier, Osguthorpe, and Robson) classifiers with Support Vector Machines (SVMs). The new prediction scheme achieves an accuracy of 69.3% when using the sevenfold cross validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins significantly increases the prediction accuracy to 72.1%. We show that it is possible to obtain a higher accuracy with combined classifiers than GOR classifiers or Support Vector Machines alone, in protein secondary structure prediction.
Design, analysis and measurement of very fast kicker magnets at SLAC

Recent experience with SLC (Stanford Linear Collider) has shown that very fast, ferrite-loaded, transmission-line, beam-kicker magnets can cause significant and undesirable distortion of a 1.5-2.5-kA, 20-40-kV pulse as it travels through the magnet. In general, there is a net lengthening of the pulse, with increases in its rise and fall times, a decrease in amplitude, and an unsymmetrical rounding of the flat top. A number of practical design considerations are discussed in terms of equivalent circuits, magnet circuit dispersion and dissipation, undesired circuit shunting and coupling, high-voltage breakdown problems and high-order-mode losses that lead to beam tube heating. These effects are linked to the properties of the materials, the presence of radiation, and realizable magnet topologies. Measurements and calculations of some of these characteristics for several magnet design are reviewed.<<ETX>>
Complex Daubechies wavelet based affine invariant representation for object recognition

An algorithm based on the dyadic wavelet transform and complex Daubechies wavelets to recognise a planar object undergoing a general affine transformation is presented. The affine transformation most appropriately represents shape distortions arising from observing an object by a camera under arbitrary orientations. The proposed algorithm includes two steps: constructing the representation and the matching process. Experimental results show that the representation is robust and combined with the matching algorithm, it efficiently classifies unknown objects.<<ETX>>
AutoHydrate: A wearable hydration monitoring system

Water is a highly abundant nutrient in the human body and monitoring of its regulation is essential to keep the body hydrated. A number of critical health conditions including swelling of the brain and short/long term memory loss are associated with poor or excessive drinking habits. This can be prevented with the use of a real time hydration monitoring system. In this paper we presented AutoHydrate, a wearable hydration monitoring system which continuously monitors the drinking activities and daily fluid requirements of the user through automatic detection of drinking and body activities. The system is built using a throat microphone for collecting acoustic signals, a smartwatch for collecting body activity, an embedded computer for processing the signals and sending recommendation to a smartphone app in real time for an interactive information display. After different time, frequency and cepstral domain features are extracted from the signals, drinking activities are classified using Support Vector Machine (SVM) and body activity is classified using Gradient Boosting Decision Tree algorithm. The Dietary Reference Intake standard is followed for recommending the amount of fluid required using our detection. Based on our experimental results on 8 subjects, a Drinking detection accuracy of 91.5% and Body activity classification accuracy of 89.12% are obtained. Results show that our system is feasible for real time monitoring of body hydration.
Design, Deployment and Bench of a Large Infiniband HPC Cluster

This paper provides an overview of the Universite de Sherbrooke (UdeS) experience, in terms of design, deployment and bench of a large InfiniBand HPC cluster. In particular, attention is focused on tuning MVAPICH and the linpack benchmark in order to achieve 83.9% of the theoretical peak, which is usally reserved for high end proprietary clusters.
Comparison of model reference adaptive control and cascade PID control for ASTank2

This paper presents Model Reference Adaptive Control (MRAC) compared to Cascade PID control in order to evaluate their performances. The cascade control has an outer loop for the level control and an inner loop for flow control that can make the response faster. There the linearization technique is applied to obtain a new linear model. MRAC is designed based on Lyapunov theory and Barbalet's lemma that will make the system stable and obtain convergences. The reference model can be selected as the third order system with relative degree two to satisfy rise time, settling time, peak time, and overshoot requirements. General adaptive control laws are developed for tuning three gains based on the reference model. These control gains cover level tracking performance. MRAC also help to remove dynamic uncertainties and modeling errors. Simulation results on Matlab/Simulink indicate that the MRAC can improve the control performances compared to those of the classical PID control effectively.
A novel hybrid routing protocol for Vehicular ad-hoc network

The Vehicular ad-hoc network (VANET) is a collection of mobile nodes without any existing infrastructure or central administrator. This kind of network is very flexible in the topology structure, so it requires the routing protocols with fast convergence in routing process, and minimal drop links. In this paper we proposed a novel hybrid routing protocol, it is the combined between of the table-driven routing and on-demand routing in VANET. With neighbor table was updated instantaneously, and using strong neighbor for routing process made route discovery process start whenever it received requirement, and it restrict route overhead at each node from 30 to 50 percentage. The simulation result illustrated the outstanding properties of our proposed routing protocol.
All-Digital Calibration of Timing Skews for TIADCs Using the Polyphase Decomposition

This brief proposes a new all-digital calibration technique suppressing the timing mismatch effect in time-interleaved analog-to-digital converters (TIADCs) for input at any Nyquist band (NB) using the equivalent polyphase structure of the TIADC. The correction technique is simple and does not require the adaptive digital synthesis filters. The timing mismatch is estimated based on an adaptive stochastic gradient descent technique, which is a promising solution for TIADCs operating at a very fast sampling rate. The digital circuit of the proposed calibration algorithm is designed and synthesized using a 28-nm fully depleted Silicon on insulator (FD-SOI) CMOS technology for the 11-b 60-dB SNR TIADC clocked at 2.7 GHz with the input in the first four NBs. The designed circuit occupies the area of 0.05 mm2 and dissipates the total power of 41 mW.
Altruism for Energy Efficiency in Ad Hoc Networks

In ad hoc networks, the energy consumption can be critical as wireless devices are mainly not connected to some constant source of energy. Algorithms that optimize the energy consumption are mainly based on local decisions, as each node tries to minimize the cost of forwarding packets by using information of his own battery state. In this paper, we try to approach the problem of optimizing energy consumption from another point of view. Considering the ad hoc network as a population of nodes, we try to exploit the idea of changing the behavior of the given node, so that they should not work selfishly but altruistically. As showed in evolution theory or in economics, the altruism can bring benefits to the whole population. As our simulation results show, the altruist behavior of the nodes can help us to decrease the energy consumption of the overall population in georouting based ad hoc networks.
A fast mixed-band lifting wavelet transform on the GPU

Discrete wavelet transform (DWT) has been widely used in many image compression applications, such as JPEG2000 and compressive sensing MRI. Even though a lifting scheme  has been widely adopted to accelerate DWT, only a handful of research has been done on its efficient implementation on many-core accelerators, such as graphics processing units (GPUs). Moreover, we observe that rearranging the spatial locations of wavelet coefficients at every level of DWT significantly impairs the performance of memory transaction on the GPU. To address these problems, we propose a mixed-band lifting wavelet transform that reduces uncoalesced global memory access on the GPU and maximizes on-chip memory bandwidth by implementing in-place operations using registers. We assess the performance of the proposed method by comparing with the state-of-the-art DWT libraries, and show its usability in a compressive sensing (CS) MRI application.
A 3D Simulation Model for Experimental Analysis from Moving Target Detection and Localization Using UWB Radar System

In this paper, we propose a 3D simulation model to characterize the scattered far-field by a scenario. The scenario is composed of a metallic plate representing wall and a trihedral, which is located behind the wall in non-line-of-sight (NLOS) areas. This model is based on a ray-tracing (RT) technique combined with the asymptotic method Uniform Theory of Diffraction (UTD). Two ultra-wideband (UWB) radars are configured as transmitter and receiver, able to work in bistatic or monostatic mode. The modeling is useful not only in explicating the mechanisms of wave propagation through the observed area, but also in evaluating the amplitude and the phase of any given point in the far-zone above the ground. Furthermore, our model is tested against 33-37GHz (Ka-band) indoor measurements conducted in the anechoic chamber. These comparisons have shown that the model gives a good agreement with the experimental results.
A Grammatically Structured Noun Phrase Extractor for Vietnamese

Noun phrase (NP) extraction is a vital part of any Natural Language Processing (NLP) system. However, it would be much better if the system can also parse the grammar structure of the extracted NPs. Grammatically structured NP (GSNP) is helpful in many research fields (Conceptual Indexing, Syntactic variant generating, Nested NP identifying, etc). This paper introduces a system that extracts NPs from Vietnamese Documents and parses each NP into a tree representing its grammar structure. These trees, in one hand, can be saved as XML documents, and in the other hand, can be loaded from these XML documents by some particular Java classes.
Active Learning for Co-Clustering Based Collaborative Filtering

Collaborative filtering, a technique for making predictions about user preferences by exploiting behavior patterns of groups of users, has become a main prediction technique in recommender systems. One crucial problem for collaborative filtering algorithms is how best to know about the preferences of a new user, who has rated none or few examples. Active learning provides effective strategies to select the most informative ratings though minimum interaction with new users. In this paper, we present a new method for actively acquiring ratings from new users. Using a co-clustering based collaborative filtering framework, we propose combining expected value of rating information with likelihood of getting ratings from the users to form the sample selection criterion. Empirical studies with two datasets of movie ratings show that the proposed method outperforms three popular active learning strategies for collaborative filtering