nils-herrmann/clean_scirepeval huggingface dataset:
An Investigation of SPSA for Signal Timing Optimization at Isolated Intersections

Simultaneous Perturbation Stochastic Approximation (SPSA) has gradually gained favor as an efficient method for optimizing computationally expensive, "black box" traffic simulations. However, few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but deliver those solutions very slowly. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, do not have enough time to reach a high-quality solution. Based on research results in the literature, SPSA holds the possibility of upgrading both desktop and adaptive solutions alike, by delivering high-quality solutions within seconds. This paper describes an extensive set of optimization tests involving SPSA. The final results suggest that today's signal timing solutions could be improved significantly by incorporating SPSA, genetic algorithms, and 'playbooks' of pre-optimized starting points.
Depth map super-resolution via iterative joint-trilateral-upsampling

In this paper, we propose a new approach to solve the depth map super-resolution (SR) and denoising problems simultaneously. Inspired by joint-bilateral-upsampling (JBU), we devised the joint-trilateral-upsampling (JTU), which takes edge of the initial depth map, texture of the corresponding high-resolution color image and the values of the surrounding depth pixels, into consideration during the process of SR. To preserve the sharp edge of the up-sampled depth map and remove the noise, we introduce an iterative implementation, where current up-sampled depth map is fed into the next iteration, to refine the filter coefficients of JTU. The iterative JTU presents a high performance at many aspects such as sharping edge, denoising and none texture copying, etc. To demonstrate the superiority of the proposed method, we carry out various experiments and show an across-the-board quality improvement by both of subjective and objective evaluations compared with previous state-of-art methods.
Application of fuzzy comprehensive evaluation model based on AHP in aircraft fire extinguishing assessment system

Aircraft fire accident has the characteristics of low probability and high severity. Therefore, it is necessary to design and study the aircraft fire extinguishing assessment system and evaluate its effectiveness. Firstly, this paper primarily selected the indexes of aircraft fire extinguishing assessment system based on the Kirkpatrick evaluation model. Then, in order to evaluate the fighting capacity of fire extinguishing, 5 first-grade indexes and 19 second-grade indexes of the multi-level fuzzy comprehensive evaluation model are established. Finally, the Analytic Hierarchy Process(AHP) is used to determine their weights, and the method of weight average principle is used to analysis the results. The research result shows that this method is systematic and logical, and its evaluation process is benefit to the application management of aircraft fire extinguishing assessment system. Meanwhile, it greatly improved the fire extinguishing skills of trainers. Therefore, the design and research of aircraft fire extinguishing evaluation system is of great practical significance.
Design and Analysis of Security Policy System in GSM/UMTS Hybrid Network

In this paper we propose a security policy system in GSM/UMTS hybrid system. Security policy requirement and framework is introduced. Design of security policy framework, description of security policy, usage of security policy and storage of security is discussed in detail. The security policy system provides a unified policy management and configuration mechanism which can manage network device, promote security feature of system, optimize system performance. The disadvantage of the system is also analyzed.
A 68.1-to-73.8GHz E-band low phase noise QPLL with amplifier feedback QVCO in 65-nm CMOS

A fully integrated 68.1–73.8GHz E-band low phase noise quadrature phase-locked loop (QPLL) is proposed. The quadrature voltage-controlled oscillator (QVCO) with a symmetrical coupling network formed by diode-connected transistors is used to reduce the phase error. In order to improve the oscillation frequency and reduced the phase noise, a feedback network composed of buffer amplifiers and capacitors is introduced to the QVCO. The locking-range divider chain of PLL consists of an injection-locked frequency divider (ILFD) with a 3-bit binary-weighted switch-capacitor bank, current mode logic (CML) dividers, and a multiple modulus divider (MMD). The proposed circuit was designed in a 65-nm CMOS process, and the QVCO achieves a tuning range of 8% from 68.1GHz to 73.8GHz with a phase noise of −95dBc/Hz at 1MHz offset while consuming only 12.5mW. The QPLL achieves an excellent phase noise of −93 dBc/Hz at 1MHz offset during the range, consuming 37.1mW of power and with a FoM of −174.1dBc/Hz.
Design of control system of microwave heating asphalt pavement maintenance truck

The design of control system of microwave heating asphalt pavement maintenance truck was presented in this paper, and the functional requirements of control system of the truck was put forward as well. Through the hardware design of IPC and the next-bit machine-based, the development and implementation of man-machine interface and image display based on the VB program, as well as the establishment of SCM control flow, the two intelligent control problems in the microwave heating of asphalt pavement repair aspects of renewable were solved. By controlling the scope of the heating wall heating and heating time, three heating methods of sustained heating, space heating, and scan heating were achieved. Experimental tests show that the heating control system is fully enough to satisfy the control requirements.
A Completed Modeling of Local Binary Pattern Operator for Texture Classification

In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT). The center pixels represent the image gray level and they are converted into a binary code, namely CLBP-Center (CLBP_C), by global thresholding. LDSMT decomposes the image local differences into two complementary components: the signs and the magnitudes, and two operators, namely CLBP-Sign (CLBP_S) and CLBP-Magnitude (CLBP_M), are proposed to code them. The traditional LBP is equivalent to the CLBP_S part of CLBP, and we show that CLBP_S preserves more information of the local structure than CLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well. By combining CLBP_S, CLBP_M, and CLBP_C features into joint or hybrid distributions, significant improvement can be made for rotation invariant texture classification.
Decoding analysis accounting for mis-corrections for spatially-coupled split-component codes

We consider an asymptotic iterative decoding analysis of spatially-coupled split-component codes used for communication over binary symmetric channel (BSC) with hard-decision decoding at the receiver. The proposed analysis takes into account the impact of mis-corrections that occur in component code decoding. The analysis technique models flows of corrections and mis-corrections that occur throughout the decoding process in the entire coupled code chain. The results for spatially-coupled split-component codes with BCH component codes demonstrate that the analysis provides significantly more accurate estimates of the iterative decoding threshold values.
A CBIR method based on color-spatial feature

Along with the analysis of color features in the hue, saturation and value (HSV) space, a new dividing method to quantize the color space into 36 non-uniform bins is introduced in this paper. Based on this quantization method we propose a color-spatial method to include several spatial features of the colors in an image for retrieval. These features are area and position, which mean the zero-order and the first-order moments, respectively. Experiments on an image database of 838 images show that the algorithm performs well in precision and adaptability.
An adaptive PID control for cement grinding particle size based on simultaneous perturbation stochastic approximation

Considering the problem of the highly dependence on the accuracy model information of the industrial control process, an online PID parameter tuning method is proposed based on the simultaneous perturbation stochastic approximation (SPSA) algorithm and the traditional PID control only by the input and output data of the system. Interfering with each component of the PID controller parameters simultaneously, and the gradient information of the control performance loss function is estimated via the output value after disturbance by the global optimization method of stochastic approximation algorithm. The PID controller parameter is iterative updated according to the gradient estimation value. Finally, we applied this method to the control process of cement grinding particle size, and the simulation results by MATLAB shown it can lead a high control precision which proves the feasibility and effectiveness of this adaptive PID control method.
A new baseband digital predistortion system

Digital predistortion technique has become more and more important in the modern mobile communications due to its excellent IMD suppression. In this paper, a new baseband digital predistortion system based on a complex gain look-up table and a simplified linear convergence algorithm is developed, simulated and tested using a hardware-EDA software combined testing solution. Trustful result got from a real test PA shows as much as 15dB reduction in the ACPR can be achieved with our system.
An ultra-low current mode amplifier aiming at biosensor applications

Recently, along with the booming of research and production of CMOS integrated bio-sensing system (IBS), ultra-low current mode amplifier (UCMA) which servers for biosensor signal pre-amplification implemented in modern CMOS technology is in high demand in areas like ultra-low bio-current analysis and detection, DNA probing and surveying system and etc. In this paper, a novel UCMA module has been presented and verified by SMIC 0.18 mum CMOS process by elaborately considering gain, bandwidth, stability, noise, DC operating point optimization, as well as offset cancellation. Theoretical calculation, HSPICE simulation, and xCalibre post simulation show that the proposed UCMA can stably provide a current gain of 57.6 dB and 3 dB bandwidth of 4.38 MHz with only InA of DC quiescent current, while the input offset current and noise current are less than 22.8 fA and 66.9 fA /radicHz, respectively.
Cement curing time influence to the accuracy of EDTA titration

EDTA titration method is widely used to detect the cement dose of the Cement Stabilized Soil in highway, generally before final setting of cement, with little influence from cement curing time. However, owing to project requirements testing must be done after a specified period. Cement curing time which is longer than the prescribed period has great influence on the experimental results. The laws of the hydrolysis and hardening process of cement and its microstructure with the cement curing time changing were studied. EDTA consumptions with different cement curing time were obtained in laboratory. Improved EDTA titration method considering cement curing time was proposed.
Cycle-based variable speed limit methodology for improved freeway merging

Studies and field implementations demonstrate that variable speed limit (VSL) systems help freeway traffic congestion mitigations. This study presents a cycle-based variable speed limit (CVSL) strategy that could variate speed limits in the upstream segment of freeway merging areas. CVSL reduces speed limits in a fraction of a cycle. This variation creates artificial gaps on freeway mainlines, which in turn increases merging opportunities for incoming on-ramp vehicles. CVSL retains VSL's benefit in reducing delays while overcoming its shortcoming of no significant throughput increase. The authors establish analytical CVSL delay models, including freeway mainline and ramp delays with CVSL speed as a decision variable, and propose a robotic solution method to minimise the delays. The delay model and solutions are implemented and independently evaluated in microscopic traffic simulation. Particularly, the CVSL system is implemented and interfaced with the transportation flow open-source microscopic model (ETFOMM). Detector information from ETFOMM is fed into the delay model, and the optimised speed limit is fed back to ETFOMM. Two performance measures, total travel delay and throughput, are used to evaluate the CVSL system. Their simulation evaluation indicated up to 16% delay reduction and 6% throughput increase. This CVSL strategy is ready for field evaluation.
A 6.15GHz balanced linear power amplifier with digital predistortion linearization

This paper presents the design of a balanced power amplifier at 6.15GHz for wireless communication systems in the higher frequency band. Two final stage amplifiers are connected in parallel through two branch line hybrid couplers, to obtain good input and output VSWR performance. The digital predistortion technique is used to further improve the linearity. The measured results show that the developed PA has excellent performance. S11 and S22 of the Balanced power amplifier are both less than -15dBc. The two-tone IMD3 is less than -47dBc at output power of 27dBm. Tested by 20MHz 64-QAM, ACPR arrives at 47.1dBc and EVM is 0.6%. The digital predistortion technique is used to further improve the linear performance of the power amplifier
An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover

This paper proposes an multi-objective evolutionary algorithm. The algorithm is based on OMOEA-II[2]. A new linear breeding operator with lower-dimensional crossover and copy operation is used. By using the lower-dimensional crossover, the complexity of searching is decreased so the algorithm converges faster. The orthogonal crossover increase probability of producing potential superior solutions, which helps the algorithm get better results. Ten unconstrained problems in  are used to test the algorithm. For three problems, the obtained solutions are very close to the true Pareo Front, and for one problem, the obtained solutions distribute on part of the true Pareo Front.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
A Mobile Middleware Approach for the Convergence of Wide Area and Personal Area Networks

Evolving device capabilities and network technologies make the seamless combination of personal area and wide area networks capable and desirable to exploit advantages of each of the environments. By this, the vision of adaptive and context-aware service provision becomes possible and offers new opportunities for pervasive computing. 
 
In this paper, we describe an approach that addresses the convergence of personal area and wide area networks on the service level. We extend a middleware for mobile ad hoc networks to enable the remote access to personal multimedia content at home via a proper infrastructure and gateway. In a second step, we use this concept to enable the context-aware download of multimedia content to the mobile phone based on rules and a semantic blackboard approach.
A Survey on Latest Botnet Attack and Defense

A botnet is a group of compromised computers, which are remotely controlled by hackers to launch various network attacks, such as DDoS attack and information phishing. Botnet has become a popular and productive tool behind many cyber attacks. Recently, the owners of some botnets, such as storm worm, torpig and conflicker, are employing fluxing techniques to evade detection. Therefore, the understanding of their fluxing tricks is critical to the success of defending from botnet attacks. Motivated by this, we survey the latest botnet attacks and defenses in this paper. We begin with introducing the principles of fast fluxing (FF) and domain fluxing (DF), and explain how these techniques were employed by botnet owners to fly under the radar. Furthermore, we investigate the state-of-art research on fluxing detection. We also compare and evaluate those fluxing detection methods by multiple criteria. Finally, we discuss future directions on fighting against botnet based attacks.
An Accurate dB-Linear Programmable-Gain Amplifier with Temperature-Robust Characteristic

This paper presents an accurate dB-linear programmable-gain amplifier with temperature-robust characteristic. A novel Gm-cell feedback topology is proposed to provide linear gain control as well as bandwidth extension without any extra power consumption. Moreover, a PTAT bias is employed to further promote the temperature robustness while AC coupling stages are embedded in the adjacent stages for DC-offset cancellation. Implemented in TSMC 65-nm CMOS technology, the proposed PGA exhibits a programmable gain range of 0 dB to 42 dB with 1 dB per step and 0.17 dB error. The gain variation across −40∼125 °C is lower than 0.3 dB at all gain settings. The core circuit consumes a DC power of 3.8mV from a 1.2 V supply and occupies a layout area of 0.048 mm2.
Constructing Concept Lexica With Small Semantic Gaps

In recent years, constructing mathematical models for visual concepts by using content features, i.e., color, texture, shape, or local features, has led to the fast development of concept-based multimedia retrieval. In concept-based multimedia retrieval, defining a good lexicon of high-level concepts is the first and important step. However, which concepts should be used for data collection and model construction is still an open question. People agree that concepts that can be easily described by low-level visual features can construct a good lexicon. These concepts are called concepts with small semantic gaps. Unfortunately, there is very little research found on semantic gap analysis and on automatically choosing multimedia concepts with small semantic gaps, even though differences of semantic gaps among concepts are well worth investigating. In this paper, we propose a method to quantitatively analyze semantic gaps and develop a novel framework to identify high-level concepts with small semantic gaps from a large-scale web image dataset. Images with small semantic gaps are selected and clustered first by defining a confidence score and a content-context similarity matrix in visual space and textual space. Then, from the surrounding descriptions (titles, categories, and comments) of these images, concepts with small semantic gaps are automatically mined. In addition, considering that semantic gap analysis depends on both features and content-contextual consistency, we construct a lexicon family of high-level concepts with small semantic gaps (LCSS) based on different low-level features and different consistency measurements. This set of lexica is both independent to each other and mutually complimentary. LCSS is very helpful for data collection, feature selection, annotation, and modeling for large-scale image retrieval. It also shows a promising application potential for image annotation refinement and rejection. The experimental results demonstrate the validity of the developed concept lexica.
Calculation and influence analysis for outlet water temperature of natural draft cooling tower

Cooling tower outlet-water temperature concerns greatly economic and safety operation of unit. A calculation model of cooling tower outlet water temperature is discussed in the paper. The results are given in the paper. The difference between calculated value and measured value is very little. The factors, which concern the inlet and outlet water temperature of cooling tower, are analyzed from the view of cooling tower and condenser influencing each other. The result indicates that the influence of all kinds of factors on inlet water temperature is greater than on outlet water temperature. It is helpful for cooling tower and condenser to calculate exactly in off-design condition.
Classifier fusion framework using genetic algorithms

In this work a hierarchical fusion framework for melding multiple classifiers has been introduced, to obtain improved performance for classification problems. The fusion framework is hybrid in nature which allows for feature and decision level fusion while also being application and data agnostic. With a set of data features and a pool of trainable classifiers as input, the fusion framework utilizes a Genetic Algorithm (GA) with a modified chromosome structure to identify the appropriate choice of classifiers, select feature inputs for each classifier, and to determine the suitable hierarchical structure for a three layered hybrid classifier fusion scheme. The paper describes the workings of the framework and shows results of improved performance over individual classifiers and the majority voting scheme when applied to physiological condition classification.
A Unitary Precoder for Optimizing Spectrum and PAPR Characteristic of OFDMA Signal

In this paper, a unitary precoder is proposed to suppress the out-of-subband (OOSB) spectral leakage and reduce the peak-to-average-power-ratio (PAPR) for DFT-based orthogonal frequency division multiple access uplink signal. The optimum precoder is developed by solving a multi-objective optimization problem and a method which includes two steps is presented. First, employ eigenvalue decomposition to design the precoder with minimizing OOSB power radiation. Then search for the optimum permutation of columns to reduce the PAPR sufficiently. Besides, an iterative decoding algorithm for the proposed precoder is designed to improve the system performance on bit-error-rate (BER). Simulations show that the proposed precoding and decoding method offers significant performance advantages of spectral leakage suppression, PAPR reduction, and BER improvement at the same time.