nils-herrmann/clean_scirepeval huggingface dataset:
Diversity combining for DS/SS systems with time-varying, correlated fading branches

Diversity combining of multiple time varying and correlated fading branches is investigated for direct-sequence spread-spectrum systems. The correlated branches are modeled and estimated jointly as a vector autoregressive (VAR) process. The joint estimation is shown to provide performance gain over separate estimation of the fading branches. The parameter matrices of the VAR model are estimated via the method of expectation maximization (EM) with two algorithms. The first algorithm, using results from Kalman smoothing, provides a closed-form solution to the maximization problem in the iterative EM procedure. However, the iterative EM-Kalman algorithm operates repeatedly on a batch of training data and results in large storage requirements and long processing delays. To overcome these disadvantages, a new algorithm with only forward-time recursions is proposed that approximates the iterative EM solution and efficiently adapts to slowly changing Doppler spreads. As a result, the new algorithm significantly reduces memory and training sequence requirements. Through computer simulations, a near ideal bit-error rate performance is found for both algorithms, and the efficacy of the new adaptive algorithm for channels with changing Doppler spreads is demonstrated.
Improved Modeling and Control of a PEM Fuel Cell Power System for Vehicles

This paper presents an improved nonlinear dynamic modeling and control of a proton exchange membrane (PEM) fuel cell stack power system for vehicle applications. The PEM fuel cell system considered includes a fuel tank, supply manifold, cooler and humidifier, fuel cell stack, as well as return manifold. The hydrogen and oxygen have been considered as fuels. The dynamic behavior of the flow path and stack is mainly described with three fundamental equations of mass conservation, nozzle flow, and continuity of isentropic flow. An improved empirical model is employed for the fuel cell system and have been simulated using MATLAB/Simulink. Simulation result shows that the stack pressure and flow rate affects the stack performance in terms of output voltage and currents. Thus, it is crucial to find an optimal control strategy for the pressure that facilitates a control of the output power according to power demand of a load. A new control strategy has been proposed and simulated in conjunction with the fuel cell stack and system models for vehicle applications
A Compact Stereoscopic Video Representation for 3D Video Generation and Coding

We propose a novel compact representation for stereoscopic videos - a 2D video and its depth cues. Depth cues are derived from an interactive labeling process during 2D-to-3D video conversion, they are contour points of foreground objects and a background geometric model. By using such cues and image features of 2D video frames, depth maps of the frames can be recovered. Compared with traditional 3D video representation, the proposed one is more compact. We also design algorithms to encode and decode the depth cues. The representation benefits both 3D video generation and coding. Experimental results demonstrate that the bit rate can be saved about 10%-50% in coding 3D videos compared with multi-view video coding and 2D+depth methods. A system coupling 2D-to-3D video conversion and coding (CVCC) is proposed to verify advantages of the representation.
Boosted Sigma Set for Pedestrian Detection

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.
Hybrid powertrain design using a domain-specific modeling environment

State of the art design tools in automotive engineering still lack the power, sophistication, and automation of design tools that are used in the electronics industry. Widely accepted automotive powertrain design tools such as PSAT and ADVISOR still rely on manual manipulation of the design parameters for optimization. This paper presents a new methodology that merges model-based design, knowledge-based engineering, and physics-based modeling to realize large-scale design optimization. The extensible domain-specific design environment is capable of rapidly assimilating new knowledge from experts and design database. Further, it can be used to automate the management of design knowledge in a customizable manner. Introducing a design process that can handle the complexity of millions of competing constraints in an automated way will allow automotive manufacturers to reduce design time considerably.
Mode Dependent Coding Tools for Video Coding

This paper provides an overview of the mode dependent coding tools in the development of video coding technology. In video coding, the prediction mode is closely related with the local image statistical characteristics. For instance, the angular intra mode contains the oriented structure information and the interpartition mode can reveal the local texture properties. Characterized by this fact, the prediction mode can be employed to facilitate the further encoding process, such as transform and coefficient coding. Recently, many mode dependent coding tools were proposed to provide more flexibility and thereby improve the compression performance. In general, these coding tools can be classified into three categories: mode dependent transform, residual reorder before transformation and coefficient scan after transformation. In this paper, these advanced coding algorithms are detailed and experiments are conducted to demonstrate their superior compression performance.
Image classification using RBM to encode local descriptors with group sparse learning

This paper proposes to employ deep learning model to encode local descriptors for image classification. Previous works using deep architectures to obtain higher representations are often operated from pixel level, which lack the power to be generalized to large-size and complex images due to computational burdens and internal essence capture. Our method slips the leash of this limitation by starting from local descriptors to leverage more semantical inputs. We investigate to use two layers of Restricted Boltzmann Machines (RBMs) to encode different local descriptors with a novel group sparse learning (GSL) inspired by the recent success of sparse coding. Besides, unlike the most existing pure unsupervised feature coding strategies, we use another RBM corresponding to semantic labels to perform supervised fine-tuning which makes our model more suitable for classification task. Experimental results on Caltech-256 and Indoor-67 datasets demonstrate the effectiveness of our method.
A high-throughput low-latency arithmetic encoder design for HDTV

In this paper, we propose a high-throughput low-latency arithmetic encoder (AE) design suitable for high definition (HD) real-time applications employing advanced video coding standards such as H.264/AVC or AVS and using a macroblock (MB) level pipeline. First, in order to derive the performance requirement on the AE, a buffer model in connected with which it is designed is thoroughly analyzed. Then, using joint algorithm-architecture optimization and multi-bin processing techniques, we introduce a novel binary arithmetic coder (BAC) architecture with throughput of 2~4 bins per cycle sufficient for real-time encoding. Furthermore, a hybrid context memory scheme is presented to meet the throughput requirement on the BAC. Simulation result shows that our design can support 1080p at 60 fps for AVS HDTV real-time coding with a bin rate up to 107K per MB line. Synthesized with the TSMC 0.13μm technology, the AE can run at 200MHz and costs 47.3K gates. By operating at 130MHz, the design is also verified in an AVS HD encoder on a Xilinx Virtex-6 FPGA prototype board for 1080p at 30 fps.
Research on Evaluation Index System of Crisis Management about Sports Field

Crisis management is an important component of managements about sports field, and the quality of crisis management about sports field requires giving a scientific index system to evaluate. Through searching relevant literatures, this paper analyzes the crisis of sports field, crisis management of sports field, and the process about crisis management of sports field, On the basis of above, it sets up the evaluation index system of crisis management about sports field, and through using expert advice, mathematical statistics, AHP, fuzzy comprehensive evaluation and other methods, it determines the weights about corresponding evaluation index system.
Large-Vocabulary Continuous Sign Language Recognition Based on Transition-Movement Models

The major challenges that sign language recognition (SLR) now faces are developing methods that solve large-vocabulary continuous sign problems. In this paper, transition-movement models (TMMs) are proposed to handle transition parts between two adjacent signs in large-vocabulary continuous SLR. For tackling mass transition movements arisen from a large vocabulary size, a temporal clustering algorithm improved from k-means by using dynamic time warping as its distance measure is proposed to dynamically cluster them; then, an iterative segmentation algorithm for automatically segmenting transition parts from continuous sentences and training these TMMs through a bootstrap process is presented. The clustered TMMs due to their excellent generalization are very suitable for large-vocabulary continuous SLR. Lastly, TMMs together with sign models are viewed as candidates of the Viterbi search algorithm for recognizing continuous sign language. Experiments demonstrate that continuous SLR based on TMMs has good performance over a large vocabulary of 5113 Chinese signs and obtains an average accuracy of 91.9%
A Visual Perceiving and Eyeball-Motion Controlling Neural Network for Object Searching and Locating

This paper proposes a visual cognitive neural network for automatic object searching and locating. The model consists of two sub-networks. One is a visual perceiving network, which simulates human eyes to input image signals and recognize an object's direction and distance in terms of a high-level perceiving neuron's maximum response. The other one is an eyeball-motion controlling network, which simulates that human brain's high-level perceiving neurons transfer their responses to eyeball-motion controlling muscle cells to change eye's gaze to the position of the object that the perceiving system is attentive to or interested in. The system is applied to human face features searching and experiments show a promising result.
Performance-Complexity Analysis of High Resolution Video Encoder and its Memory Organization for DSP Implementation

This paper first analyses the relationship between performance and complexity of several state-of-the-art coding algorithms for high resolution videos. Based on the coding efficiency comparison under different config parameters and the intra mode usage in P/B frame, this paper presents a practical scheme to improve the coding speed with slight quality loss. And a DSP-oriented two-level internal memory organization is also proposed to keep pipeline processing. In such organization, block correlation caused by motion vector predictions is lightened while keeping almost the same performance as the original
Pruning tree-structured vector quantizer towards low bit rate mobile visual search

Coming with the proliferation of mobile devices, mobile visual search emerges. One fundamental issue here is the query transmission latency, especially in a bandwidth constraint wireless link. Towards low bit rate retrieval, recent works have proposed to extract compact visual descriptors directly on the mobile end, where the vocabulary tree based bag-of-words representation has shown superior performance in producing compact descriptors [2][9]. However, the corresponding tree-structure vector quantizer is extremely large against a mobile end implementation. In this paper, we propose two alternatives to prune this tree structure based on the subtree discriminability analysis, where either information gain based or ranking based pruning are investigated. Furthermore, we have unveiled that the tree structure can be even discarded while retaining only the discriminative leaves together with their radii in practice. We evaluate our tree pruning on Android HTC Desire G7, with application to low bit rate mobile landmark search in a 10-million landmark photo collection, where over 10 scale memory reduction with almost identical search accuracy is reported.
Compact Descriptors for Visual Search

To ensure application interoperability in visual object search technologies, the MPEG Working Group has made great efforts in standardizing visual search technologies. Moreover, extraction and transmission of compact descriptors are valuable for next-generation, mobile, visual search applications. This article reviews the significant progress of MPEG Compact Descriptors for Visual Search (CDVS) in standardizing technologies that will enable efficient and interoperable design of visual search applications. In addition, the article presents the location search and recognition oriented data collection and benchmark under the MPEG CDVS evaluation framework.
Code-Matched Interleaver Design over Surrogate Channels

Recently, the universality of a well-designed LDPC code ensemble has been recognized. Take the DVB standards for example. The structured LDPC codes originally developed for satellite communications have been reused in terrestrial and cable channels. To this end, a bit interleaver can be employed to adapt the profile of the given code to the variation of channel conditions. In this paper, we focus on the design of code-dependent bit interleavers for parallel non-uniform channels. Since the channel-dependence of a given code ensemble is dominated by the mutual information between the channel input and output, we propose to simplify the analysis about the decoding behavior by using a set of surrogate binary erasure channels (BEC). The approximation of the actual channel by the surrogate BEC is established on the equivalence of bitwise capacities, which represent the mutual information between the uniformly-distributed binary input and the likelihood ratios of the effective parallel AWGN channels. Moreover, the transition of the erasure probabilities is modeled by a linear difference equation around the decoding threshold SNR, from which we can derive a necessary condition on the convergence of decoding iterations and achieve a useful guideline for the configuration of the bit interleavers.
An integrated method of ANFIS and Dempster-Shafer theory for fault diagnosis of power transformer

Dissolved gas analysis (DGA) approach is extensively applied to detect incipient faults of power transformers. This paper presents a novel DGA method for power transformer incipient fault diagnosis based on integrated adaptive neuro fuzzy inference system (ANFIS) and Dempster-Shafer Theory (DST). Four out of seven common conventional methods which are studied and compared for better consistency and accuracy, are used to develop new ANFIS based fault diagnosis models. To promote fault diagnostic performance further and make fault decision process more reliable and reasonable, an improved DST is introduced to integrate outputs of each ANFIS based model, and to provide comprehensive and convincing fault diagnosis results. The fault diagnosis capability of the proposed method is validated by a reported fault dataset and 10-fold cross validation experiment. The performance of the proposed method is compared with conventional approaches and ANFIS based models which demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy and consistency.
Entropy of primitive: A top-down methodology for evaluating the perceptual visual information

In this paper, we aim at evaluating the perceptual visual information based on a novel top-down methodology: entropy of primitive (EoP). The EoP is determined by the distribution of the atoms in describing an image, and is demonstrated to exhibit closely correlation with the perceptual image quality. Based on the visual information evaluation, we further demonstrate that the EoP is effective in predicting the perceptual lossless of natural images. Inspired by this observation, in order to distinguish whether the loss of input signal is visual noticeable to human visual system (HVS), we introduce the EoP based perceptual lossless profile (PLP). Extensive experiments verify that, the proposed EoP based perceptual lossless profile can efficiently measure the minimum noticeable visual information distortion and achieve better performance compared to the-state-of-the-art just-noticeable difference (JND) profile.
Robust frontal face detection in complex environment

We constructed a simple and fast system to detect frontal human faces in complex environment. There are two main contributions of our work: 1) we use a fast image segmentation method based on connected components labelling to select candidate face areas; 2) we propose a positive-negative attractor template to examine face areas. A valley detector is used to search the valley-like points of eyes and mouths. We test the system on images in complex environment and with confusing objects. The experiment shows a robust detection result with few false detected faces.
Efficient bit allocation and CTU level rate control for High Efficiency Video Coding

In this work, a CTU level rate control algorithm is proposed for High Efficiency Video Coding (HEVC). On top of the CTU level rate control algorithm, an efficient bit allocation method considering the HEVC hierarchical coding structure is specifically designed. Instead of directly applying the rate distortion model at the CTU level, it's proposed to derive the CTU level quantization parameter (QP) based on the frame level QP with feedback of the coding status of CTUs. To further improve the coding performance of the CTU level rate control, a QP adjustment strategy is proposed and incorporated into the rate control algorithm. Experimental results show that the proposed CTU level rate control algorithm can achieve better coding performance than the state-of-the-art rate control scheme for HEVC (1.9% for RA, 3.2% for LD). Moreover, the coding quality become much smoother with the proposed algorithm.
Spatial error concealment via model based coupled sparse representation

In this paper, we propose a novel spatial error concealment algorithm through model-based coupled sparse representation. According to the non-local self-similarity property of natural images, we first collect two set of samples by template matching: one is called the latent set corresponding to the current missing patch and the other one is called the template set corresponding to the current template. Using these two sets of samples as the training data, we learn a dictionary pair and a linear prediction model simultaneously. The pair of dictionaries aims to characterize the two structural domains of the two sets, and the linear model is to reveal the intrinsic relationship between the sparse representations of the current missing patches and its template. Finally, we cast the non-local dictionary learning and local correlation model into a unified coupled sparse coding framework to obtain optimal sparse representation and further accurate estimation of the current missing patch. Experimental results demonstrate that the proposed method remarkably outperforms previous approaches.
Novel observation model for probabilistic object tracking

Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force search to perform object tracking on the generated probability map, which is obtained from the classification results; however, performing probabilistic object tracking on the probability map is almost unexplored. This paper proposes a novel observation model which is suitable to perform this task. The observation model considers both region and boundary cues on the probability map, and can be computed very efficiently by using the integral image data structure. Extensive experiments are carried out on several challenging image sequences, which include abrupt motion change, background clutter, partial occlusion, and significant appearance change. Quantitative experiments are further performed with several related trackers on a public benchmark dataset. The experimental results demonstrate the effectiveness of the proposed approach.
Fuzzy neural network compound control and its applications for dust trap of electric arc furnace

The dust produced by steel-making in an electric arc furnace is one of the most serious pollution sources. A kind of intelligent control strategy is presented and some presently-existing problems are discussed based on the complexity of the dust trapping process of an arc furnace. The tendency of the quantity of smoke, produced in different smelting steps, to change can be predicted with a neural network. A basic output value can be obtained from the basic controller, and then the operating points of the system can be changed easily and the revolution rate of the dust-trap draught fan can be adjusted in time. A good effect has been obtained and the energy cost has been reduced in this dust trap process by means of fuzzy inference to compensate for the effect of the bag-house on the system.
A New Just-Noticeable-Distortion Model Combined with the Depth Information and its Application in Multi-view Video Coding

Traditional video compression methods remove spatial and temporal redundancy based on the statistical correlation. However the final receptor is the human, we can remove the perception redundancy to get higher compression efficiency, by taking use of the properties of human visual system (HVS). Research has simulated the sensitivity of HVS to luminance contrast and spatial and temporal masking effects with the just-noticeable-distortion (JND) model, which describes the perception redundancy quantitatively. This paper proposes a new model named MJND (JND in Multi-view), which explores the property of HVS to stereoscopic masking effect. The proposed model not only contains the spatial and temporal JND, but also includes the JND in depth. The MJND model is then used in macroblock (MB) quantization adjustment and rate-distortion optimization in multi-view video coding (MVC). Compared with the standard MVC scheme without JND, our model can get better visual quality in the case of the same bit rate.