nils-herrmann/clean_scirepeval huggingface dataset:
Application of neural networks for linear/nonlinear microwave modeling

This paper presents an overview of emerging artificial neural network (ANN) techniques for linear and nonlinear microwave modeling. ANN based models can automatically learn the microwave component or circuit behaviors with satisfactory accuracy, and the trained ANN models are able to implement into commercial circuit simulators for efficient design and optimization. ANN modeling techniques are successfully applied on EM, nonlinear device, and circuit behavior modeling, with speed and accuracy advantages over conventional techniques.
Design of 60-GHz amplifiers based on over neutralization and optimized inter-stage matching networks in 65-nm CMOS

This paper proposes a 60-GHz two stage low-noise amplifier (LNA) and a three stage power amplifier (PA) designed with over neutralization and optimized inter-stage matching techniques in 65nm CMOS process. Thanks to the gain-boosting from over neutralization techniques and insertion loss reduction with bandwidth extension from proposed inter-stage matching technique based on micro-strip lines and transformers, the LNA achieves a 18dB gain, 7GHz 3-dB bandwidth, 2.1dBm ZW with a noise figure of 4.9dB, while consuming only 20mW from a supply of 1.2V. And the PA features 20dB gain, >8GHz bandwidth, a 10.4dBm Z1dB with 14% PAE and 14dBm Psat. The proposed techniques also help to reduce the die sizes of the LNA and PA are reduced to 1.18*0.51mm2 and 1.17*0.47mm2 as well.
An improved addintent algorithm for building concept lattice

The algorithm research on building concept lattice is one of the most important topics in formal concept analysis. So far, two types of algorithms called the batch algorithm and the incremental algorithm have been proposed. As the fastest incremental algorithm, AddIntent algorithm is a very promising algorithm. But in the process of recursive construction of concept lattice in AddIntent algorithm, some concepts have been accessed repeatedly so that unnecessary time consuming is caused. Making full use of the structure of concept lattice, the improved AddIntent algorithm proposed in this paper can avoid repeated comparison of concepts by adding marks to the concepts which are accessed, new or updated. Experimental results show that this algorithm can improve the efficiency significantly compared with the original AddIntent algorithm.
A Feature-Enriched Completely Blind Image Quality Evaluator

Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinion-unaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk/~cslzhang/IQA/ILNIQE/ILNIQE.htm.
A lightweight framework for data acquisition and quality monitoring in power system

In power system, data acquisition plays an increasing important role in the field of modern power quality monitoring system. However, data communication expanding range of protocols, increasing the signal type, growing the supervisory instruments has become the major problem. In this paper, we present a power data acquisition and quality monitoring framework (F-PDAQM) that is a software framework which abstracts the most common attributes and behaviors in power quality monitoring system. F-PDAQM was developed in object-oriented with some design patterns, and implemented by using C++. It can achieve data visualization of power system via combining virtualization technology and power quality monitoring system. A significant contribution which F-PDAQM brings is that the problem of multiple communication protocols match with a variety of communication channel is solved. A voltage prediction component is provided to realize the voltage quality prediction. In addition, we propose a new concept of soft data acquisition and provide interfaces which are conducive to realize different algorithms. Soft data acquisition is an extremely important method to assure data integrity. F-PDAQM is composed of four layers. This four-layer architecture is designed for extensibility and reusability so that more complex power system problems can be tackled within the framework. It is complementary and compatible of the international standard of the power system-IEC 61850. The research in this paper has been applied to a data acquisition and supervisory system for a substation which demonstrates the validity and flexibility of the framework.
A two-stage variation-aware task mapping scheme for fault-tolerant multi-core Network-on-Chips

With technology scaling, process variations influence the performance, power and reliability significantly, especially for multi-core and many-core systems. Faulty-tolerant multi-core architectures are paid widely attention to, which integrate redundant cores to improve the manufacturing yield. In this paper, a two-stage variation-aware task mapping scheme is proposed for multi-core NoCs with redundant cores. Firstly, a static genetic task mapping algorithm is presented to generate multiple task mapping solutions to cover a maximum range of chips. Then, at runtime, one optimal mapping solution is selected, and logical cores are mapped to physical available cores. Both core asymmetry and topology changes are considered in the proposed approach. Experimental results demonstrate that the proposed approach increases the performance yield by 56%, and the communication cost is reduced by 11.3%.
A Novel MEMS Based Infrared Biosensor for Ultra-Sensitive Detection of Waterborne Pathogens

In this paper, a new concept of MEMS based biosensor is presented as a promising solution for the ultra- low level and real time detection of waterborne pathogens. This novel infrared biosensor employs the SiGe/Si quantum well thermistor with a high TCR and low 1/f noise to achieve a high sensitive detection through its resistance change with the absorption of infrared radiation from the dark quencher. Meanwhile, a manufacturing process applying the adhesive wafer bonding is proposed.
Adaptive Distributed Beamforming for Relay Networks Based on Local Channel State Information

Most of the existing distributed beamforming algorithms for relay networks require global channel state information (CSI) at relay nodes and the overall computational complexity is high. In this paper, a new class of adaptive algorithms is proposed which can achieve a globally optimum solution by employing only local CSI. A reference signal based (RSB) scheme is first derived, followed by a constant modulus (CM) based scheme when the reference signal is not available. Considering individual power transmission constraint at each relay node, the corresponding constrained adaptive algorithms are also derived as an extension. An analysis of the overhead and stepsize range for the derived algorithms are then provided and the excess mean square error (EMSE) for the RSB case is studied based on the energy reservation method. As demonstrated by our simulation results, a better performance has been achieved by our proposed algorithms and they have a very low computational complexity and can be implemented on low cost and low processing power devices.
All-Wheel Braking Force Allocation During a Braking-in-Turn Maneuver for Vehicles With the Brake-by-Wire System Considering Braking Efficiency and Stability

A novel method of all-wheel braking force allocation during a braking-in-turn maneuver is proposed for vehicles with the brake-by-wire (BBW) system. The concept of stability priority is applied in distributing the braking force to each wheel, which means that the stability performance has priority over the braking demand during a braking-in-turn maneuver. Based on this principle, I-curves for braking-in-turn maneuvers with respect to three parameters are developed using the graphics of a single-track vehicle model, defining the force allocation between the front and rear axles. Then, the distributed braking force of the front and rear axles is allocated between the inner and outer wheels based on the load transfer. The simulation and hardware-in-the-loop (HIL) experimental results show that the proposed allocation strategy makes good use of adhesion and ensures vehicle stability during a braking-in-turn maneuver. The proposed method provides theoretical guidelines on independent braking force control in the BBW system, which enhances vehicle stability and braking performance.
Computer aided optimum design of friction materials using uniform design

The application of Uniform design in friction material formulation and a multi-layer back propagation artificial neural network (BPANN) methods of Data Handling are investigated for modeling and prediction of the wear properties of composites. The friction and wear properties show that Uniform design can distribute the experiment points evenly so as to have fewer number of tests. With computer aided artificial neural network (ANN) modeling and optimization technology, the relationships between factors and properties can be predicted.
Differential power analysis attack and countermeasures on MCrypton

MCrypton is an ultra-lightweight symmetric cryptographic algorithm, which is designed to adapt to the resource constrained computing scenarios, such as RFID tags and sensor networks, etc. With the continuous development of Internet of things (IoT), the application of this kind algorithm becomes increasingly popular, so the implementation security of the algorithm has correspondingly become an imperative research area. Among the attack methods, the power consumption attack (PA) is widely adopted because of its advantages such as easy acquisition, fast efficiency and high accuracy. This paper has designed a complete differential power attack (DPA) platform, targeting at the mCrypton algorithm implemented on a Mega16 micro-controller. Test results indication that the mCrypton algorithm is vulnerable to power consumption attack under the current hardware environment. Also, to resist these attacks, the present paper proposes a fixed-value mask algorithm for mCrypton.
A Hybrid Optoelectronic Networks-on-Chip Architecture

Optical/Photonic networks-on-chip (NoC) have been considered as a promising and viable paradigm to interconnect a large number of processing cores at chip level. Hybrid optoelectronic NoCs provide a more practical solution by using the electronic network for local communication while the optical network for global communication. This paper explores how to efficiently combine optical and electronic networks to build a hybrid NoC. A butterfly fat tree-based hybrid optoelectronic NoC architecture is proposed using the generic wavelength-routed optical router. Simulation results demonstrate that, compared with electronic mesh- and CMesh-based NoCs, the proposed hybrid NoC achieves the best power efficiency with comparable throughput and significantly reduces the latency under localized traffic.
Displacement damage analysis of single-junction and triple-junction GaAs solar cells induced by electron irradiation

Space-graded single-junction (SJ) and triple-junction (TJ) GaAs solar cells, produced by MOCVD, are evaluated through electron-irradiation at the energy of 1MeV, 1.5MeV and 2MeV to compare radiation effects of electrons on these solar cells, and also to provide experimental data for predictions of the cell performances in space. Mean degradations of the short circuit current, open circuit voltage and maximum power are presented and analyzed. Compared to the radiation data of the single-junction GaAs cell, the triple-junction GaAs cell has a superior radiation-hardness performance at the same electron energy and fluence. Degradations at different electron energies have been correlated with displacement damage dose. A good linearity are obtained between the effective electron displacement damage dose and the degradation parameters. The performance prediction of both solar cells in space can be readily predicted by the approach.
An effective pansharpening algorithm for GeoEye-1 satellite images

Pansharpening has been an important tool in remote sensing applications, which is a process of transforming a set of low-spatial-resolution multispectral images to high-spatial-resolution images, by fusing a co-registered fine-spatial-resolution panchromatic image. To date, a variety of pansharpening methods have been proposed. However, when dealing with the new-style GeoEye-1 satellite images, the pansharpened multispectral images often produce information distortion severely. We present a new and effective pansharpening algorithm that employs correspondence analysis and redundant wavelet transform. The experimental evaluations are carried out on GeoEye-1 data and their analysis show preliminarily that our algorithm gives out indeed a better tradeoff between the spatial resolution enhancement and the spectral information preservation than some existing algorithms.
Delay-Optimal Channel Selection Method for Wireless Cognitive Networks

Driven by government policy and advanced front-end radio technologies, cognitive radio offers an ever-promising future in the next generation communication system. Due to the random characteristics of primary users, it is impossible to fully describe the activity feature in terms of time and space, rendering opportunity access unsafe to primary user. In order to minimize interference to primary users and find available channel as fast as possible, a history knowledge based algorithm is proposed in this work, which compound historical information with current observed information. Random early detection (RED) algorithm, which proves to be efficient in active queue management (AQM) for switching fabrics, is introduced to help select the desirable channel. In order to further suppress the collision probability, all the channels are sorted according to availability. Simulation results show that, compared with completely random spectrum selection method, the proposed RED-based channel access method gives better performance in terms of channel selection delay.
An oscillator-based CMOS magneto-sensitive biosensor with a low noise and low temperature coefficient LDO regulator

This paper presents a high sensitivity oscillator-based magneto-sensitive biosensor. A fully integrated on-chip low noise low-dropout voltage regulator (LDO) with a low temperature coefficient double-stacked diode bandgap is introduced to suppress the environmental variations, e.g. voltage vibration, temperature drift. As an implementation example, a biosensor cell is designed in a standard 180-nm CMOS process. An ultra low output noise of the LDO is achieved, i.e. 443 and 150nV/√Hz at 100Hz and 1kHz, respectively. The biosensor with the proposed LDO achieves an extra 6.1dB suppression of the frequency drift. The magnetic sensing capability of the presented biosensor is verified by avidin-labeled MNPs with diameters of 200nm, indicating a frequency shift of 0.03MHz from the 1μg/ml biotin-IgG sample.
A Privacy-Preserving and Secure Framework for Opportunistic Routing in DTNs

Opportunistic routing has been extensively studied and utilized in delay/disruption-tolerant networks. The extensive use of nodes' local information, e.g., the distance to the destination or the contact frequency with the destination, in such routing schemes can cause severe security and privacy problems. Existing solutions of anonymous routing can introduce undesired overhead and fail to provide the confidentiality of the routing metric. In this paper, we propose an advanced framework for opportunistic routing schemes, providing the following properties: confidentiality of the nodes' routing metric, anonymous authentication, and efficient key agreement for pairwise communication. A comprehensive evaluation, including security analysis, efficiency analysis, and simulation evaluation, is presented to show the security and feasibility of the proposed framework.
A variational multiphase level set approach to simultaneous segmentation and bias correction

This paper presents a novel level set approach to simultaneous tissue segmentation and bias correction of Magnetic Resonance Imaging (MRI) images. We first model the distribution of intensity belonging to each tissue as a Gaussian distribution with spatially varying mean and variance. Then a sliding window is used to transform the intensity domain to another domain, where the distribution overlap between different tissues is significantly suppressed. A maximum likelihood objective function is defined for each point in the transformed domain, which is then integrated over the entire domain to form a variational level set formulation. Tissue segmentation and bias correction are simultaneously achieved via a level set evolution process. The proposed method is robust to initialization, thereby allowing automatic applications. Experiments on images of various modalities demonstrated the superior performance of the proposed approach over state-of-the-art methods.
A new global shape prior for level set based segmentation

The global shape prior knowledge has been exploited by many image segmentation approaches in order to improve segmentation results when there are such problems as occlusion, cluttering, low contrast edges, etc. We propose a global shape prior representation and incorporate it into a level set based segmentation framework. This global shape prior can effectively help remove the cluttered elongate structures and island-like artifacts in the segmentation. We experimentally compare the performance of our global shape prior with an extensively used global shape prior introduced in . The experimental results show that our global shape prior averagely achieves 15% higher precision rates with comparable recall rates, which demonstrates the efficacy of the proposed shape prior.
An inductorless wideband low noise amplifier with current reuse and linearity enhancement

In this paper, a wide-band inductor-less LNA for low voltage operation is presented. The input matching is implemented by using a feedback transistor with a resistor terminated to the source to enhance linearity and to reduce noise. Current reuse technique is employed to increase the total transconductance. Under 1V supply voltage and a DC of 5.6 mA, the LNA achieves an average noise figure of 3.2 dB, a maximum voltage gain of 18.6 dB, and 3 dB bandwidth ranges from 0.2-4.5 GHz.
A Stochastic Perturbing Particle Swarm Optimization Model

The particle swarm optimization (PSO) algorithmis a generally used optimal algorithm, which exhibits good performance on optimization problems in complex search spaces. However, traditional PSO model suffers from a local minima, and lacks of effective mechanism to escape from it. This is harmful to its overall performance. This paper presents an improved PSO model called the stochastic perturbing PSO(SPPSO), which tries to overcome such premature convergence through perturbing the swarm with the perturbation and acceptance probability. The performance of the SPPSO is compared with the basic PSO (bPSO) on a set of benchmark functions. Experimental results show that, the new model not only effectively prevent the premature convergence, but also keep the rapid convergence rate like the bPSO.
Canny edge detection enhancement by scale multiplication

The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.
Comparison of Sorption Coefficients of Triclosan in Different Activated Sludge and Application in Sewage Treatment System

Sorption experiments of triclosan (TCS) were carried out on different sludge (anaerobic, aerobic and anoxic sludge) in different type (activated sludge, sterilized sludge and antibacterial sludge). The sorption models under different temperatures (10, 20 and 30 ¿) were investigated and the corresponding sorption coefficients were calculated. The results showed that TCS sorption agree with the Freundlich and Henry linear sorption model, and the coefficients Kd and KF varied with the increasing of temperature. TCS sorption show no significantly difference on activated sludge or antibacterial sludge, so the antibacterial sludge can be used in sorption experiment in stead of activated sludge. According to the validation experiment of sorption coefficients in sewage treatment system, TCS sorption on activated sludge agree with linear model, the sorption ratio on the sludge can be predicted by the linear distribution coefficient Kd.
A Background Reconstruction Algorithm Based on Modified Basic Sequential Clustering

Based on the assumption that background appears with large appearance frequency, a new background reconstruction algorithm based on modified basic sequential clustering is proposed in this paper. First, pixel intensity in period of time are classified based on modified basic sequential clustering. Second, merging procedure is run to classified classes. Finally, pixel intensity classes, whose appearance frequencies are higher than a threshold, are selected as the background pixel intensity value, so the background model can represent the scene well. Compared with the background reconstruction method based on basic sequential clustering, the simulation results show that an assignment for the data is reached after the final cluster formation, at the same time those near classes are avoided at all and the effect of input order of data has been reduced greatly. And the background model can represent the scene well.
Comparison of amplitude-optimised bit-edge equalisation with pulse-width modulation in high-speed wireline application

An amplitude-optimised bit-edge equalisation (BEE) scheme is presented and compared with pulse-width modulation for mitigating intersymbol interference in high-speed backplane applications. With transmitter pre-coding and pre-emphasis, this BEE uses an adaptive LMS algorithm as a receiver error convergence engine by adjusting error derivation points. The optimality of the proposed BEE scheme is discussed.