nils-herrmann/clean_scirepeval huggingface dataset:
Maximizing intra-individual correlations for face recognition across pose differences

The variations of pose lead to significant performance decline in face recognition systems, which is a bottleneck in face recognition. A key problem is how to measure the similarity between two image vectors of unequal length that viewed from different pose. In this paper, we propose a novel approach for pose robust face recognition, in which the similarity is measured by correlations in a media subspace between different poses on patch level. The media subspace is constructed by canonical correlation analysis, such that the intra-individual correlations are maximized. Based on the media subspace two recognition approaches are developed. In the first, we transform non-frontal face into frontal for recognition. And in the second, we perform recognition in the media subspace with probabilistic modeling. The experimental results on FERET database demonstrate the efficiency of our approach.
Object Recognition Based on Dependent Pachinko Allocation Model

Recently the "bag of words" model becomes popular in the approaches to object recognition. These approaches model an image as a collection of local patches called "visual words", and recognize objects in the image through inferring latent topics associated with the set of visual words. In this paper, we apply an extension version of Pachinko allocation model (PAM) to object recognition. Our PAM based approach models the correlation-ship of latent topics explicitly in a hierarchical structure. To relax the independent assumption for visual words and refine the topic inferring, we incorporate the prior knowledge of cooccurrence dependence among visual words into PAM. Highly competitive recognition results on both Caltech4 and Caltech101 datasets show the proposed approach is more expressive and discriminative than most existing methods of object recognition.
Advanced Modeling of Micro-Turbine Controls for System Analysis

With the increased penetration of distributed generation (DG) on power systems, there is much research to look into the impact of the DG on the operations of the power systems. While often there is a concern that the DG will have an adverse effect on the power system, the presence of an alternative source provides an opportunity to use the capabilities of the source to enhance and support the system. Through advanced control of the DG, power system personnel may investigate how changing the DG settings prior to planned switching or major load changes may reduce transient impacts on the system. Additionally the DG provides an opportunity to investigate the feature of intentional islanding during a major event. This paper covers some theory about distributed generation as the micro-turbine components and model are presented. Then an explanation about what is the universal controller architecture, and some advantages of its use are presented. The case of study and model implementation are also presented along with the preliminary results and conclusions
Teaching - Learning-based optimization approach for enhancing remanufacturability pre-evaluation system's reliability

Remanufacturability pre-evaluation is an important step at used products consolidation stage. In order to provide a quick sorting speed and reliable evaluation with a long lifespan, radio frequency identification (RFID) system is often employed in practice. A major factor that influences RFID system's reliability is the inaccuracy arising from missing data and reading errors, which are magnified to produce deleterious effects on reliability. A good yet simple solution is to add more redundant components (i.e., RFID readers) to smooth the RFID system's reliability. In this paper, we first formulate our focal scenario as a reliability-redundancy allocation problem (RRAP). Then, one of the recently developed swarm intelligence approach called teaching - learning-based optimization (TLBO), which is based on the effect of the influence of a teacher on the output of learners in a class, is employed to address our focal problem. Simulation results suggest that the proposed TLBO is a viable optimization technique in dealing with the optimization of RFID system's reliability.
Segmentation-based dense stereo matching algorithm for virtual view synthesis

This paper presents a new stereo matching algorithm to compute dense disparity map for virtual view synthesis. The reference view is segmented using mean-shift segmentation method. An adaptive support-weights window-based approach is adopted to obtain the initial disparity map per pixel. We utilize cross-checking technique to filter out reliable correspondences and detect occlusion regions. The disparity in each segment is represented as a 3D planar plane by a robust plane fitting process. With the plane model of each segment, the disparity of unreliable points and occlusion regions can be refined. Finally, pixel domain matches are defined by computed disparity map, and virtual view can be synthesized by interpolating. Experimental results with the Middlebury stereo testing images show that our stereo matching algorithm gives a good performance.
A review of inertia and frequency control technologies for variable speed wind turbines

With high penetration of wind energy into modern power systems, a gradual displacement of conventional thermal generator by wind farms and the fluctuating nature of wind speed will result in dynamic active power imbalance leading to corresponding frequency stability issues. Thus, this topic draws a growing attention from industry and academia all over the world devoted to the design, development and application of auxiliary frequency regulation function by variable speed wind turbine (VSWT) for the last couple of years. This paper mainly features a comprehensive overview on industry design and applications, ongoing research activities as well as various national technical requirements regarding the VSWT frequency regulation and spinning reserve. Further, the contribution of offshore HVDC wind farm to power system frequency regulation is discussed in depth.
Extending DACLIC for near-lossless compression with postprocessing of greyscale images

A lossless/near lossless coding scheme, DACLIC, is presented. The proposed scheme attempts to remove redundancy in a given image in the spatial domain. The redundancy removal is achieved by block direction prediction and context-based error modeling. The block direction operation in DACLIC first partitions an image into blocks. Pixels within each incoming block are analyzed resulting in a best directional prediction for that block. The best direction is chosen from a given set that results in the minimum prediction error. Removal of redundancy by the block direction technique is not possible for removing all possible redundancy in a given image. Another decorrelation part of the DACLIC scheme is the context-based error modeling which exploits context-dependent DPCM error structures. The DACLIC scheme is primarily used as a lossless image compression technique. However, the scheme can be easily extended to near-lossless compression applications by introducing a small quantization loss. This small quantization loss is restricted to an absolute error not exceeding a prescribed value n for all pixels in a given image. Application of block direction and context modeling reduces a given image into residuals. This residual typically has a lower entropy than the given image. A quadtree Rice coder (QRC) is proposed as an entropy coder of DACLIC. An arithmetic coder is also given as an option. The QRC operates on residual blocks with low computing complexity that compares favorably with the residual coding method used by LOCO-I as the proposed QRC is two-dimensional in nature. For near-lossless compression with a larger value of n, banding artifacts are visible in the decoded image. In the DACLIC system, a postprocessing technique is proposed to remove the banding artifacts.
A fast intra coding algorithm for HEVC

High Efficiency Video Coding (HEVC) is the emerging video coding standard, which provides equivalent subjective quality with about 50% bit rate reduction compared to H.264/AVC High Profile. However, the improvement of coding efficiency is obtained at the expense of increased computational complexity. In this paper, a fast algorithm for HEVC intra coding is proposed. Firstly, a depth range prediction method by exploiting the correlation between the neighboring Coding Tree Units (CTUs) is proposed. Secondly, the rate distortion costs and Sum of Absolute Difference with Hadamard transform (HSAD) of recently encoded CUs are used to decide whether current CU will be further divided or not. Finally, only the intra prediction modes (IPMs) with lower precision are employed in the rough mode decision (RMD) and IPMs for rate distortion optimization (RDO) are reduced based on the correlation between neighboring CUs. Experimental results show that our proposed algorithm can achieve average 54% (up to 57%) encoding time saving while causing negligible RD performance loss (1.0% BD-rate increase on average) in All Intra High Efficiency test condition compared with HM 10.0.
Intelligent travel route planning for bridge crane type of material handling equipment in cellular manufacturing

In this work, we propose the bridge crane travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas in manufacturing systems. We then investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the bridge crane operating performance measure index is employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem for bridge crane.
MPEG transcoding for DVD recording

In this paper a system level MPEG transcoding scheme is proposed. The MPEG-2 system standard specifies two kinds of streams: transport stream (TS) and program stream (PS). Each is optimized for a different set of applications. The transcoding between TS and PS has an important application, which can be applied to convert the digital television (DTV) broadcasting bitstreams to the bitstreams for digital video disc (DVD) application. An important technical issue for transcoding between PS and TS is the rate control algorithm. Usually, for DVD application, the video is encoded by variable bit rate (VBR) in order to provide constant quality over whole sequence, while for DTV application due to buffer requirement the video should be encoded by constant bit rate (CBR). hi this paper, a novel VBR rate control algorithm is proposed for the on-line video transcoding process. In this algorithm, the bit allocation strategy at picture level, which is based on a bit producing model and hyperbolic R-D function, is first discussed. Then, the rate control algorithm at macroblock level with R-D optimization is proposed. Experiment results show that the proposed VBR transcoding algorithm achieved the better image quality compared with CBR transcoding at the same bit rate.
A self-adaptive digital calibration technique for multi-channel high resolution capacitive SAR ADCs

This paper introduces a self-adaptive digital technique to calibrate multi-channel SAR ADCs. Rather than achieving higher resolution by adjusting the analog component values, this new digital method infers the capacitor weights of a SAR ADC with the help of a channel-shared high resolution sigma-delta ADC, which results in remarkable improvements in the capacitive SAR ADCs' property. ENOB increases from 11.06 bits to 12.67 bits. The digital calibration logic is based on LMS (least mean squares) and gradient descent.
A single-pass based adaptive interpolation filtering algorithm for video coding

An adaptive interpolation filtering (AIF) algorithm has been proposed to improve the conventional hybrid video coding scheme recently. Although such an algorithm does improve coding efficiency significantly, its encoding complexity, in terms of computational complexity and memory access, increases dramatically due to its inherent two-pass encoding structure. In this paper, we propose a novel single-pass based algorithm which can reduce the complexity of AIF significantly. In our method, a competitive filter set which includes optimal filters trained from several previous frames and the fixed H.264 filters is considered for the current coding frame. A rate-distortion optimization (RDO) criterion is then used to select the best one at macro-block (MB) level. In order to reduce overhead, a predictive coding method is used to compress the filter type for each MB. Experimental results show that, by using the proposed algorithm, the encoding complexity can be significantly reduced without sacrifice of coding gain.
An Efficient FGS Coding Scheme for Interlaced Scalable Video Coding

JVT scalable video coding (SVC) provides high coding efficiency for progressive video sequences with combined scalability. However, interlaced SVC is only the straightforward extension of H.264/AVC interlaced coding with the similar FGS coding technique for progressive coding. Based on the particular temporal correlation of interlaced video sequences, this paper presents a novel and efficient FGS coding scheme for interlaced SVC, which is able to achieve higher compression efficiency and further temporal scalability by introducing additional temporal decomposition stage when coding key pictures. The advantages of our proposed interlaced FGS coding scheme are verified by integrating it into JVT-SVC reference software.
Network Adapted Selective Frame-Dropping Algorithm for Streaming Media

This paper presents a network adapted selective frame-dropping algorithm for streaming media over the Internet, which aims to address the problem of random packet losses resulted from network bandwidth mismatching. The basic idea is to first determine a sending window for each GOP according to its rendering time interval, and then selectively drop some frames with low priority to ensure that other more important frames in this GOP can be reliably delivered to the receiver within the time limitation of the GOP's sending window. For each GOP, the frame-dropping policies are determined in advance according to the transmitting results of its preceding GOP, and then slightly re-adjusted after each frame has been sent according to the current available network bandwidth. Experimental results show that the proposed algorithm can achieve free-error and fluent rendering effects even in the case of worse network conditions.
An Effective Local Invariant Descriptor Combining Luminance and Color Information

Extraction of stable local invariant features is very important in many computer vision applications, such as image matching, object recognition and image retrieval. Most existing local invariant features mainly characterize luminance information, and neglect color information. In this paper, we present a new local invariant descriptor characterizing both of them, which combines three photometric invariant color descriptors with the famous SIFT descriptor. To reduce the dimension of the combined high-dimensional invariant feature the principal component analysis (PCA) is used. Our experiments show the proposed local descriptor through combining luminance and color information outperforms the descriptors that only utilize a single category of information, and combining the three color feature representations is more effective than only one.
Multi-hypothesis based multi-view distributed video coding

This paper proposes a multi-hypothesis based Wyner-Ziv (WZ) decoder for the multi-view distributed video coding (MDVC). Two hypotheses, the intra-view SI and the interview SI, are fed together into the WZ decoder in the proposed scheme. A multi-hypothesis based correlation model (MHBCM) is presented to fully exploit the redundancy between these two SI frames and the original frame. The MHBCM is also applied on the optimal minimum mean-square error reconstruction of the quantized samples. The simulation results show that the proposed algorithms are able to significantly improve the coding efficiency of the MDVC system.
High Resolution Local Structure-Constrained Image Upsampling

With the development of ultra-high-resolution display devices, the visual perception of fine texture details is becoming more and more important. A method of high-quality image upsampling with a low cost is greatly needed. In this paper, we propose a fast and efficient image upsampling method that makes use of high-resolution local structure constraints. The average local difference is used to divide a bicubic-interpolated image into a sharp edge area and a texture area, and these two areas are reconstructed separately with specific constraints. For reconstruction of the sharp edge area, a high-resolution gradient map is estimated as an extra constraint for the recovery of sharp and natural edges; for the reconstruction of the texture area, a high-resolution local texture structure map is estimated as an extra constraint to recover fine texture details. These two reconstructed areas are then combined to obtain the final high-resolution image. The experimental results demonstrated that the proposed method recovered finer pixel-level texture details and obtained top-level objective performance with a low time cost compared with state-of-the-art methods.
Nonconvex Lp Nuclear Norm based ADMM Framework for Compressed Sensing

Compressed Sensing (CS) has drawn quite an amount of attention as a joint sampling and compression methodology. Recent studies further show that image prior models play an important role in image CS recovery. By exploiting the non-local self-similarity of natural images and clustering similar patches, low-rank prior model is adopted in this paper. Different from traditional nuclear norm, we extend the lp (0 <; p <; 1) penalty function on singular values of a matrix to characterize low-rank prior model, and propose a new non-nonvex lp nuclear norm prior model for image CS recovery, which is able to more accurately enforce image structural sparsity and self-similarity at the same time. The proposed optimization problem is efficiently solved within the alternative direction multiplier method (ADMM) framework. Experimental results demonstrate that the proposed lp nuclear norm based ADMM framework for image CS recovery framework exhibits good convergence and achieves significant performance improvements over the current state-of-the-art methods.
Detecting adult image using multiple features

This paper presents a new method to detect naked people in an image using multiple features. The skin color model is firstly used to detect naked skin areas roughly. The Sobel edge operator and Gabor filter are used to weed those that are not really human skin pixels. Images that have many naked skin areas are thought maybe to have naked people. The color coherence vector and color histogram of these images are calculated and the SVM (support vector machine) is used to determine which of these images contain images of naked people.
Multiple modes intra-prediction in intra coding

Intra-prediction is a widely used technique in intra coding. In H.264, nine directional prediction modes are used, and in AVS (audio-video coding standard of China), six modes are used. The paper proposes a multiple mode, intra-prediction scheme to achieve a better balance between prediction precision and side information cost. Three kinds of additional modes are introduced to increase prediction precision from different aspects. A significant performance gain can be achieved using this multiple mode intra-prediction, with little complexity increase.
Human reappearance detection based on on-line learning

Many video surveillance applications require detecting human reappearances in a scene monitored by a camera or over a network of cameras. This is the human reappearance detection (HRD) problem. Studying this problem is important for analyzing a surveillance scenario at semantic level. In this paper, we propose a novel online learning framework for solving HRD problem. Both generative model and discriminative model are employed in this framework and a voting scheme is presented to fuse the decisions of both models for determining whether a just entered person is one of those who have shown up, i.e. whether a reappearance happens. Both models will be updated based on mistake-driven online learning strategy. Our experimental results show that the adopted online learning framework not only improves the reappearance detection accuracy but also achieves high robustness in various surveillance scenes.
A design of high-power based on Direct Torque and Torque-limited Control electric wrench driver

In this paper the author designed a high-power and torque-limited electric wrench driver based on direct torque control, and hope that when the input is single-phase power frequency alternating current, the electric wrench can output limited torque by DTC according to the pre-set speed. And this electric wrench is able to limit the maximum torque and prevents the damage to the device. Also the electric wrench diver has a function of over-current and over-voltage protection, these functions protect itself from voltage surges. The paper verifies Direct Torque Control feasibility applied to the electric wrench driver, and finds out disadvantages of u-i FluxLinkage Observer which provides us guidance for further study.
Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition

Subspace-based face representation can be looked as a regression problem. From this viewpoint, we first revisited the problem of recognizing faces across pose differences, which is a bottleneck in face recognition. Then, we propose a new approach for cross-pose face recognition using a regressor with a coupled bias-variance tradeoff. We found that striking a coupled balance between bias and variance in regression for different poses could improve the regressor-based cross-pose face representation, i.e., the regressor can be more stable against a pose difference. With the basic idea, ridge regression and lasso regression are explored. Experimental results on CMU PIE, the FERET, and the Multi-PIE face databases show that the proposed bias-variance tradeoff can achieve considerable reinforcement in recognition performance.
A low-power auto-zeroed comparator for column-paralleled 14b SAR ADCs of 384×288 IRFPA ROIC

This paper presents a low power comparator with auto-zeroed technique for the readout chain of a 384×288 infrared focal plane array (IRFPA). To overcome the high power consumption of column-paralleled application, a novel inverter=based pre-amplifier is introduced. The performances of the proposed comparator are verified by a 14-bit column paralleled Successive-Approximation-Register (SAR) A/D converter which is developed in a 0.35um CMOS-based process technology. The SFDR of the A/D converter is up to 93dB at a sampling clock of 31.25 KHz with an input signal of 1.009 KHz. The overall static power of the 384 column-paralleled ADCs is less than 50mW.
A representation of context in design thinking process modeling

One way to reduce potentially serious problems caused by modifications to the original design is to find out design thinking process of the original designer. The knowledge structure of design thinking process is represented, which is composed of design intent, design decision, design rationale and design operation. Semi-formal structure is adopted by the presentation, which means both the expressiveness and computability of the representation can be improved. Context in the design thinking process is defined, which can reduce complexity of the design thinking process. Independence between contexts that share an intent allows the designer to propose more assumptions about the design without necessarily affecting previous design thinking process. A graphical modeling system is established for capturing and formalizing the design thinking process.