nils-herrmann/clean_scirepeval huggingface dataset:
A Statistical Characterization of CMOS Process Fluctuations in Subthreshold Current Mirrors

A novel method to characterize CMOS process fluctuations in subthreshold current mirrors (SCM) is reported in this paper. The proposed model is succinct in methodology and calculation complexity comparing to the reported statistical models, however, provides favorable estimations of CMOS process fluctuations on the SCM circuit, which makes it being promising for engineering applications. The model statistically abstracts physical parameters, which depend on IC process, into random variables with certain mean values and standard deviations, while aggregating all the random impacts into a discrete martingale. The correctness of proposed method is experimentally verified by an SCM circuit implemented in SMIC 0.18 mum CMOS 1P6M mixed signal process with a conversion factor of 100 over an input range from 100 pA to 1 muA. The proposed theory successfully predicted the ~plusmn10% of die-to-die fluctuation measured in experiment, and also suggested the ~ 1 mV of threshold voltage standard deviation over a single die, which meets the process parameters suggested by the design kit from the foundry. The deviations between calculated probabilities and measured data are less than 8%. Meanwhile, pertinent suggestions to high fluctuation tolerance subthreshold analog circuits design are also made and discussed.
Analysis of RF impairments on EVM performance of the OFDM PHY of IEEE 802.11ad standard

The paper presents a system-level simulation platform supporting both top-down and bottom-up design and verifications based on 802.11ad standard, and EVM degradation on the OFDM PHY due to various RF impairments, such as LO phase noise, PA nonlinearity, I/Q mismatch, and ADC/DAC resolution are simulated and elaborated. The platform has been verified based on the EVM comparison between simulation and measured results. This work provides RF designers with comprehensive system-level and circuit-level overviews of 802.11ad RF transceivers for Gbps wireless communications on a pre-silicon base.
A Novel Chaotic Sequence Optimization Neural Network for Concentration Estimation of Formaldehyde by an Electronic Nose

Electronic nose (E-nose), as an artificial olfactory system, can be used for quantification of odor concentration combined with a pattern recognition module. Back propagation neural network (BPNN) has been recognized as the common pattern recognition in E-nose development. Considering the flaw of easily trap into a local optimal of BPNN, this paper presents a novel chaotic sequence optimization BPNN method for improving the accuracy in E-nose quantification prediction. Three chaos dynamic equations including logistic map, tent map and Gaussian map for chaotic queue with ergodic characteristic were applied in chaos based optimization. Through comparisons with standard particle swarm optimization, the experimental results demonstrate the superiority and efficiency of the chaos based optimization algorithm from the point of view of the search ability and robustness.
A 60GHz wideband injection-locked frequency divider with adaptive-phase-enhancing technique

A 60-GHz injection-locked frequency divider (ILFD) with quadrature outputs is designed in 90-nm CMOS technology. An adaptive-coupling scheme is proposed to enlarge the phase shift for a wide-band locking range. The measured results exhibit an input locking range of 12.1GHz or 20.5%from 52.7 to 64.8GHz at an incident power of 0dBm. The core circuit consumes 8.6mW from a 1.2V power supply and occupies an area of 360µm×230µm2.
An Intention-Topic Model Based on Verbs Clustering and Short Texts Topic Mining

Microblog, Twitter, status messages, the classified information website and so on are experiencing explosive growth with the development of web2.0, people prefer to use short texts to express their intentions and activities. Yet, when people submit some requirements through short texts, they hope to get a feedback which can help them to solve their problems rather than relevant content. Sometimes people need corresponding intention rather than similar content. However, current researches cannot solve the problem well. In this paper, we propose an intentiontopic model: Verb-Biterm Topic Model(V-BTM), which aims at corresponding intention matching. Intention is expressed by verbs and topic is expressed by BTM. Intention is the action of people want to express and topic is the goal of the intention. The key of the model is that people tend to express their intention with verbs and tend to express the topic with non-verb. In this model, firstly, we distinguish intentions with the verb clustering with the help of word2vec which is a deep learning tool. Secondly, we mine the topic using Biterm Topic Model(BTM) on the data without verbs. We carry out experiments on real-world short text collections. The results demonstrate that our approach can get better verb clustering and mine more coherent topics. Furthermore, the new model can be the base of our future researches.
Advances in Si-based nanotechnology and quantum devices

The preparation technologies of nanostructures were reviewed in tins paper, included nanofabrication and self-assembled growth technique. With the down sizing of present silicon IC technology, validity of Moore's law has become seemingly limited, because the feature size of Si ULSI has reached nanometer regime. It is unavoidable that quantum effects play very important role in the nanoarchitectures. Microelectronic industry faces the severe challenge and opportunities. However, it is undoubted that the novel materials, for example, carbon nanotubes, will provide some solution for future nanoscaling architectures and devices, such as CNT-FET, but it is still a question whether CNT-based technology will actually contribute to the existing CMOS technology or it is totally new device architecture from CMOS technology. The Si-based quantum devices, included electronic and optoelectronic devices, are presented in this paper, they are based on quantum effects and fabricated by the processing compatible with Si IC technology.
Design of Embedded Control System Based on ARM9 Microcontroller

This paper mainly focuses on the design and realization of embedded control system based on ARM kernel. Firstly, hardware structure, configuration and signal detection, processing of the control system based on ARM are described. Secondly, the system application programs, including main control program, user operation interface program, keyboard program and development of drivers for some S3C2410X related functional modules are described. Lastly the closed-loop control of parameter is achieved by way of PID and NN. As practical experience shows, after introducing the control system into the parameter control for microbiology fermentative process, the design of the system reaches expected effect, realizes relevant functions and shows a broad market prospect.
Composition of nanometer-scaled self-assembled SiGe islands

Quantitative relation between composition of self-assembled SiGe islands and critical size for shape evolution was established. The critical size, at which Ge islands change from pyramids to domes, increases with increasing of Si concentration in islands. Based on the critical size obtained from AFM characterization of SiGe islands, the composition of nanometer-scaled SiGe islands was calculated from the dependence of critical size of shape transition on the Si concentration.
Digital calibration implementation for track-and-hold offset in a high-speed timing-interleaved folding and interpolating analog-to-digital converter

A digital calibration implementation for track-and-hold offset in a high-speed timing-interleaved folding and interpolating analog-to-digital converter is proposed in this paper. The spice simulation and measured results both show that the digital calibration technique can efficiently cancel the T/H offset and improve the linearity of the ADC.
A Framework for Maximizing Utility of Sanitized Documents Based on Meta-labeling

Document sanitization, i.e., the process of removing or generalizing sensitive information in order to reduce the security classification of the document, is widely used today in applications of information sharing. Traditional document sanitization systems focus on removal or generalization of certain words and phrases, but do not take into account the utility of the sanitized documents. This leads to a gap between the sanitized documents and the users' requirements. Proposed in this paper is a formal framework and conceptual algorithms for optimal document sanitization based on meta-labeling. Each document is associated with a meta-label, which serves to determine both the security label and the utility of the document. In the sanitization process, the system first computes a new meta-label for the sanitized version and then sanitizes the document through mediators guided by the new meta-label. Algorithms are provided to compute a new meta-label that is proven to satisfy the security requirements and provide maximal utility with respect to users' requirements, which are also represented by a meta-label.
A case study on the IMC for ozone dosing process of drinking water treatment

Ozonation is one of the most important processes during drinking water treatment. To improve the efficiency of ozonation and to stabilize the quality of the treated water, the ozone dosage should be a good trade-off between the requirement of disinfection and the restriction of bromate formation. However, because of the changes of raw water quality and the nonlinear behavior of ozonation process, the ozone dosing process is very difficult to be controlled. In this paper, the control strategy of keeping a constant ozone exposure is attempted instead of conventional keeping a constant ozone dosage or dissolved ozone residual. To this end, internal model control (IMC) scheme based on the radial basis function (RBF) neural network model is proposed to maintain a constant ozone exposure by adjusting ozone dosage. With the proposed control scheme, a RBF neural network model is established which could be used for IMC. And then an IMC controller is designed. The full-scale experimental studies demonstrate the efficiency and effectiveness of proposed control scheme.
Dimensionality reduction by supervised locality analysis

High-dimensional feature representations have recently been widely used for image classification, which not only induce large storage requirement and high computational complexity, but also tend to be lack of discrimination due to redundant and noisy features. In this paper, we propose a novel algorithm named supervised locality analysis (SLA) for dimensionality reduction. In contrast to conventional dimensionality reduction methods, the proposed SLA incorporates supervision into locality analysis by fully exploring multi-class distributions, which can handle the non-linear data structure while preserving intrinsic discriminative information. The obtained compact and highly discriminative features by the SLA is enables more accurate and efficient classification. Moreover, the SLA can be used for supervised dimensionality reduction of both handcrafted and deep learning based features. We have conduced experiments to evaluate the proposed SLA on three datasets for image classification. The SLA has produced state-of-the-art performance and largely outperformed widely-used dimensionality reduction methods.
Clutter property analysis and clutter suppression for Hypersonic airborne radar

Clutter property of a forward-looking array radar on Hypersonic Vehicle is analyzed and a method for clutter suppression using multi-stage space-time adaptive processing (STAP) is proposed. This method employs the elevation degree of freedom of the planar array to extract various range ambiguous clutter. Then range-unambiguous non-stationary clutter is compensated by using Doppler Warping (DW) algorithm, and finally STAP in azimuth-Doppler domain is used to suppress the residual clutter. Simulation results are given to demonstrate the validity of the presented method.
Dispersing Social Content in Mobile Crowd through Opportunistic Contacts

Crowdsourced content sharing has become a fast-growing activity in today's online social networks, where contents of interest are created by diverse source users and conveyed over the network as friends view and reshare. The rapid and boundless propagation in a mobile crowd however often creates severe bottlenecks on the server side and incurs significant energy and monetary costs on the mobile side, particularly with the still expensive 3G/4G cellular connections. This paper presents SoCrowd, a novel framework for large-scale content sharing in a mobile crowd by exploiting contacts, i.e., users happen to move close with such short range low power communications as WiFi and bluetooth being enabled. We formulate the scheduling problem for social content propagation in a mobile crowd with contacts, and present optimal dynamic programming solution, which further motivates a series of practical heuristics. The effectiveness of SoCrowd has been demonstrated by extensive simulations driven by realworld traces and datasets.
A linear subspace learning approach via sparse coding

Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower dimensional space by linear projection. Most LSL methods compute directly the statistics of original training samples to learn the subspace. However, these methods do not effectively exploit the different contributions of different image components to image recognition. We propose a novel LSL approach by sparse coding and feature grouping. A dictionary is learned from the training dataset, and it is used to sparsely decompose the training samples. The decomposed image components are grouped into a more discriminative part (MDP) and a less discriminative part (LDP). An unsupervised criterion and a supervised criterion are then proposed to learn the desired subspace, where the MDP is preserved and the LDP is suppressed simultaneously. The experimental results on benchmark face image databases validated that the proposed methods outperform many state-of-the-art LSL schemes.
A risk metric for designing a highly reliable real-time Ethernet network

The recent developments of industrial Ethernet are brushing aside people's doubt whether industrial Ethernet could fulfill all of their requirements to real-time performance and reliability. Although designing a highly reliable network with strict real-time constraints is becoming possible, no method has been proposed to help network design systematically considering reliability and real-time constraints. A risk metric is proposed in this paper for evaluating the relationship between reliability mechanisms of an industrial Ethernet network and its real time performance. By simulation of an example network with ring topology the metric is shown to be a suitable basis for effective network optimization.
A battery/supercapacitor hybrid energy storage system for DC microgrids

This paper explores the frequency coordinating virtual impedance concept and proposes a control strategy for the co-ordination control of a hybrid energy storage system in DC microgrids. It investigates power decoupling methods to better attenuate the ripple power by filtration. Lead-acid battery and supercapacitor are used to form a hybrid energy storage system and are connected to a common DC grid through bi-directional DC-DC converters. Battery converter can absorb low frequency power variations while the high frequency power variations can be absorbed by supercapacitor converter. Battery supplies the long term power demand and supercapacitor responds to the short term power fluctuations during transient process in this proposed method. For the reduction of ripple power in the battery, this paper discusses the insertion of high order low/high pass filters for battery and supercapacitor converters instead of lower order low/high pass filters. The effectiveness of the proposed concept is shown through simulation and experimental results.
A Distributed De-Embedding Solution for CMOS mm-Wave On-Wafer Measurements Based-on Double Open-Short Technique

In this letter, a novel distributed de-embedding solution with double open-short structures are proposed and investigated. The distributed effect is well considered by regarding the interconnection as scalable transmission line at high frequency and all the parasitic parameters can be extracted directly by three steps. Experimental results show this general distributed de-embedding technique provides more stable on-wafer parasitic extraction than well-adopted SLOT de-embedding technique at millimeter-wave frequency.
Activity invariant sets and stable attractors of Lotka-Volterra recurrent neural networks

This paper proposes to study the activity invariant sets and exponentially stable attractors of Lotka-Volterra recurrent neural networks. The concept of activity invariant sets deeply describes the property of an invariant set by that the activity of some neurons keeps invariant all the time. Conditions are obtained for locating activity invariant sets. Under some conditions, it shows that an invariant set can have one equilibrium point which is exponentially stable. Since the attractors are located in activity invariant sets, each attractor has binary pattern and also carries analog information. Such results can provide new perspective to apply attractor networks for applications such as group winner-take-all, associative memory, etc..
An environment adaptation method for robust speech recognition

It is well known that differences between training and testing environments seriously affect speech recognition accuracy. Several environment adaptation techniques have been proposed for eliminating environmental difference, and they are effective when the adaptation data in the testing environment is available beforehand. However, testing environments are not always known beforehand. Therefore, a new framework, which uses testing utterances themselves for adaptation, is effective to cope with such variation. In this paper, a method of testing environment adaptation is proposed, which gradually learns the features of testing environment as the testing procedure, and does not need to get some testing samples beforehand.
An integrated nine-switch power conditioner

A nine-switch power converter having two sets of output terminals was recently proposed in place of the traditional back-to-back power converter that uses twelve switches in total. The nine-switch converter has already been proven to have certain advantages, in addition to its component saving topological feature. Despite these advantages, the nine-switch converter has so far found limited applications due to its many perceived performance tradeoffs like requiring an oversized dc-link capacitor, limited amplitude sharing and phase shift between its two sets of output terminals. Instead of accepting these tradeoffs as limitations, a nine-switch power conditioner is proposed here that virtually “converts” most short-comings of the nine-switch topology into interesting performance advantages. Aiming further to reduce its switching losses, an appropriate discontinuous modulation scheme is proposed and studied here in details to doubly ensure that maximal reduction of commutations is achieved. With an appropriately designed control scheme then incorporated, the nine-switch converter is shown to favorably raise the overall power quality in simulation, hence justifying its role as a power conditioner at a reduced semiconductor cost.
Application of Radial Basis Function Neural Network in the Starting Process of Electric Forklift

Since the start process for control system of electric forklift has the characters of nonlinearity and fast time-variety, and routine PID method is difficult to satisfy the nonlinear and variable request. So this paper applied a control strategy based on Radial Basis Function neural network, RBFNN PID, to control the motor through closed-loop control, in order to compensate the perturbation, nonlinearity and outside disturbance of system parameter, and achieve the purpose of a smooth start-up of electric forklift. Proved through the simulation and experiment, this control strategy of starting process can control starting current which is rapid, stable and robust.
A novel windowing scheme to suppress spectral sidelobes for OFDMA system

Orthogonal frequency division multiplexing access (OFDMA) inherently suffers from large spectral sidelobes, which will lead to interference between users and can not be ignored. In this paper, in order to suppress spectral sidelobes, we propose a novel windowing scheme. The derivation of the optimum window function is based on the solution of an optimization problem. It aims to minimize in-band-out-of-subband (IBOSB) power radiation. An algorithm named GSSO is designed to approximate the optimum window function. The simulation results show that with a lower roll-off factor (2.5%) the proposed windowing scheme can significantly improve the performance of spectral sidelobes suppression.
Directional independent component analysis with tensor representation

Conventional independent component analysis (ICA) learns the statistical independencies of 2D variables from the training images that are unfolded to vectors. The unfolded vectors, however, make the ICA suffer from the small sample size (SSS) problem that leads to the dimensionality dilemma. This paper presents a novel directional multilinear ICA method to solve those problems by encoding the input image or high dimensional data array as a general tensor. In addition, the mode-k matrix of the tensor is re-sampled and re-arranged to form a mode-k directional image to better exploit the directional information in training. An algorithm called mode-k directional ICA is then presented for feature extraction. Compared with the conventional ICA and other subspace analysis algorithms, the proposed method can greatly alleviate the SSS problem, reduce the computational cost in the learning stage by representing the data in lower dimension, and simultaneously exploit the directional information in the high dimensional dataset. Experimental results on well-known face and palmprint databases show that the proposed method has higher recognition accuracy than many existing ICA, PCA and even supervised FLD schemes while using a low dimension of features.