nils-herrmann/clean_scirepeval huggingface dataset:
Period Estimation of the PN Sequence for Direct Sequence Spread Spectrum in Multipath Environment

In this paper, an algorithm reprocessing of the power spectrum density (RPSD) is proposed to detect and estimate the PN code's period of the multipath direct sequence spread spectrum (DS/SS) signal in low SNR, which develops the previous processing method based on the single path environment. It can solve the difficult problem of PN code's period detection and estimation in multipath environment. In the algorithm, the signals' power spectrum density (PSD) is firstly obtained, and then is treated as an input signal to compute its RPSD, the PSD secondly obtained appears peak pulses representing the signals' existence. Through detecting the interval of the peak pulses, we can obtain the period estimation of the DS/SS signals' PN code in multipath environment. Theoretical analysis and simulation results show that the proposed algorithm can effectively estimate the PN code's period of DS/SS signal in multipath environment, and that the estimation performance closely relates to the multipath environment.
Large scale partial-duplicate image retrieval with bi-space quantization and geometric consistency

The state-of-the-art image retrieval approaches represent image with a high dimensional vector of visual words by quantizing local features, such as SIFT, solely in descriptor space. The resulting visual words usually suffer from the dilemma of discrimination and ambiguity. Besides, geometric relationships among visual words are usually ignored or only used for post-processing such as re-ranking. In this paper, to improve the discriminative power and reduce the ambiguity of visual word, we propose a novel bispace quantization strategy. Local features are quantized to visual words first in descriptor space and then in orientation space. Moreover, geometric consistency constraints are embedded into the relevance formulation. Experiments in web image search with a database of one million images show that our approach achieves an improvement of 65.4% over the baseline bag-of-words approach.
Towards cross-category knowledge propagation for learning visual concepts

In recent years, knowledge transfer algorithms have become one of most the active research areas in learning visual concepts. Most of the existing learning algorithms focuses on leveraging the knowledge transfer process which is specific to a given category. However, in many cases, such a process may not be very effective when a particular target category has very few samples. In such cases, it is interesting to examine, whether it is feasible to use cross-category knowledge for improving the learning process by exploring the knowledge in correlated categories. Such a task can be quite challenging due to variations in semantic similarities and differences between categories, which could either help or hinder the cross-category learning process. In order to address this challenge, we develop a cross-category label propagation algorithm, which can directly propagate the inter-category knowledge at instance level between the source and the target categories. Furthermore, this algorithm can automatically detect conditions under which the transfer process can be detrimental to the learning process. This provides us a way to know when the transfer of cross-category knowledge is both useful and desirable. We present experimental results on real image and video data sets in order to demonstrate the effectiveness of our approach.
Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution

Organizing webpages into interesting topics is one of the key steps to understand the trends from multimodal Web data. The sparse, noisy, and less-constrained user-generated content results in inefficient feature representations. These descriptors unavoidably cause that a detected topic still contains a certain number of the false detected webpages, which further make a topic be less coherent, less interpretable, and less useful. In this paper, we address this problem from a viewpoint interpreting a topic by its prototypes, and present a two-step approach to achieve this goal. Following the detection-by-ranking approach, a sparse Poisson deconvolution is proposed to learn the intratopic similarities between webpages. To find the prototypes, leveraging the intratopic similarities, top- ${k}$  diverse yet representative prototype webpages are identified from a submodularity function. Experimental results not only show the improved accuracies for the Web topic detection task, but also increase the interpretation of a topic by its prototypes on two public datasets.
An image contrast fusion enhancement algorithm based on Retinex

In this paper, we proposed a contrast fusion enhancement algorithm based on center-surround Retinex model for a rendition of high dynamic range images. Firstly only luminance component obtained from YUV color space is processed by using the Retinex algorithm with different parameters, and then the different enhanced results are fused by the proposed nonlinear model in this paper, which can efficiently compress the dynamic range of the image. Compared to the conventional methods, the proposed method can compress the dynamic range of the image well to achieve a better enhancement effect without generating color distortion.
An Automatic Classification System Applied in Medical Images

In this paper, a multi-class classification system is developed for medical images. We have mainly explored ways to use different image features, and compared two classifiers: principle component analysis (PCA) and supporting vector machines (SVM) with RBF (radial basis functions) kernels. Experimental results showed that SVM with a combination of the middle-level blob feature and low-level features (down-scaled images and their texture maps) achieved the highest recognition accuracy. Using the 9000 given training images from ImageCLEFOS, our proposed method has achieved a recognition rate of 88.9% in a simulation experiment. And according to the evaluation result from the ImageCLEFOS organizer, our method has achieved a recognition rate of 82% over its 1000 testing images
Study of quantity characteristics of double valves fuel injection system with electronically controlled

Double valves system simulation model is established in AMESim environment, By comparing with experiment, The simulation model can accurately predict injection characteristics of the double valves system. The influence of the parameters such as piston diameter, cam velocity, piston matching clearance, SV valve lift, NCV valve lift, needle valve lift and injector diameter on the main fuel injection quantity fluctuation is analyzed in details with the simulation model in the whole working conditions The change rule of the main injection quantity fluctuation with speed and injection pulse is obtained under different parameters, The results show that piston diameter, cam velocity and injector diameter are the key characteristic parameters on the main fuel injection quantity fluctuation.
ObjectBook construction for large-scale semantic-aware image retrieval

Automatic image annotation assigns semantic labels to images thus presents great potential to achieve semantic-aware image retrieval. However, existing annotation algorithms are not scalable to this emerging need, both in terms of computational efficiency and the number of tags they can deal with. Facilitated by recent development of the large-scale image category recognition data such as ImageNet, we extrapolate from it a model for scalable image annotation and semantic-aware image retrieval, namely ObjectBook. The element in the ObjectBook, which is called an ObjectWord, is defined as a collection of discriminative image patches annotated with the corresponding objects. We take ObjectBook as a high-level semantic preserving visual vocabulary, and hence are able to easily develop efficient image annotation and inverted file indexing strategies for large-scale image collections. The proposed retrieval strategy is compared with state-of-the-art algorithms. Experimental results manifest that the ObjectBook is both discriminative and scalable for large-scale semantic-aware image retrieval.
Ensemble Diffusion for Retrieval

As a postprocessing procedure, diffusion process has demonstrated its ability of substantially improving the performance of various visual retrieval systems. Whereas, great efforts are also devoted to similarity (or metric) fusion, seeing that only one individual type of similarity cannot fully reveal the intrinsic relationship between objects. This stimulates a great research interest of considering similarity fusion in the framework of diffusion process (i.e., fusion with diffusion) for robust retrieval.,,In this paper, we firstly revisit representative methods about fusion with diffusion, and provide new insights which are ignored by previous researchers. Then, observing that existing algorithms are susceptible to noisy similarities, the proposed Regularized Ensemble Diffusion (RED) is bundled with an automatic weight learning paradigm, so that the negative impacts of noisy similarities are suppressed. At last, we integrate several recently-proposed similarities with the proposed framework. The experimental results suggest that we can achieve new state-of-the-art performances on various retrieval tasks, including 3D shape retrieval on ModelNet dataset, and image retrieval on Holidays and Ukbench dataset.
Non-Lambertian photometric stereo by color and noise analysis

This paper proposes a novel photometric stereo method for reliable reconstruction of non-Lambertian object surfaces based on color and noise analysis. Unlike previous techniques that treat highlight pixels as outliers, the proposed method utilizes the intrinsic shape cue in those pixels by robust reflection separation. Image noise is estimated from two photometric images instead of a single one, thus the common uniform block assumption can be eliminated. The highlight pixels are identified by analyzing intensity and chromaticity uncertainty, and the specularity components are removed based on robust estimation of diffuse chromaticities. The normals of deficient positions, whose pixels are of extremely low intensities, are computed from individual channels. The obtained surface normal map is further refined by adaptive lateral filtering. Experimental results show that, compared with the state-of-the-art techniques, the proposed method produces improved reconstruction accuracy, which indicates the effectiveness of the proposed method for non-Lambertian object surfaces.
Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks.

This paper tries to separate fine-grained images by jointly learning the encoding parameters and codebooks through low-rank sparse coding (LRSC) with general and class-specific codebook generation. Instead of treating each local feature independently, we encode the local features within a spatial region jointly by LRSC. This ensures that the spatially nearby local features with similar visual characters are encoded by correlated parameters. In this way, we can make the encoded parameters more consistent for fine-grained image representation. Besides, we also learn a general codebook and a number of class-specific codebooks in combination with the encoding scheme. Since images of fine-grained classes are visually similar, the difference is relatively small between the general codebook and each class-specific codebook. We impose sparsity constraints to model this relationship. Moreover, the incoherences with different codebooks and class-specific codebooks are jointly considered. We evaluate the proposed method on several public image data sets. The experimental results show that by learning general and class-specific codebooks with the joint encoding of local features, we are able to model the differences among different fine-grained classes than many other fine-grained image classification methods.
Periodicity Detection of Local Motion

Periodicity is useful for compact representation of periodic motion and a reasonable selection of a proper temporal scale for periodic motion analysis. In this paper, we concern the periodicity detection of local motion within an interesting region and present an approach to automatically detect the motion periodicity inherent to local motion under complex condition. The task is challenging as local motion is usually buried in clutters with global motion and noises. Most existing methods have assumed a static camera and a labeled moving object region. We instead apply robust local motion estimation and an object localization method to extract the object motion. The object motion is characterized by the confidence based motion probability map and the motion vectors obtained by global motion compensation. The autocorrelation series of motion energy is then carried out to locate local maximum points. With the set of indices of local maximum points, we can estimate the basic periodicity through a least-square fitting. This method has been applied to swimming videos and got encouraging results
Person Re-identification in the Wild

This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different detectors for re-ID. We make three distinct contributions. First, a new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities. Extensive benchmarking results are presented on this dataset. Second, we show that pedestrian detection aids re-ID through two simple yet effective improvements: a cascaded fine-tuning strategy that trains a detection model first and then the classification model, and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement. Third, we derive insights in evaluating detector performance for the particular scenario of accurate person re-ID.
Parallelized TSM-RT Method for the Fast RCS Prediction of the 3-D Large-Scale Sea Surface by CUDA

In this paper, a parallelized two-scale model-ray tracing (TSM-RT) method is developed for the fast radar cross-section prediction of the 3-D large-scale sea surface by the compute unified device architecture (CUDA). The TSM-RT method is an accelerated RT method, where the sea surface is constructed into a great deal of large triangles, which consist of many small triangles; this is called the two-scale model. For the TSM-RT method, the large triangles are utilized to determine the asymptotical ray paths, while the small triangles are utilized to calculate the scattered far fields. In addition, the CUDA of NVIDIA takes advantage of the graphics processing units (GPUs) for parallel computing, and greatly improves the speed of computation. The scattering field of each ray in the TSM-RT method is calculated independently. Therefore, a parallelization concept, which is based on the CUDA technology, is presented to improve the computational efficiency. The asynchronous transfer and shared memory are used to further improve the speedup factors. Our method is validated by comparing the numerical results with those obtained using a CPU. At the same time, significant speedup factors are achieved compared with the CPU-based TSM-RT.
Tracking performance limitation of a linear MISO unstable system with quantized control signals

This paper studies the tracking performance for linear time-invariant multi-input single-output (MISO) discrete-time feedback systems with quantized control signals. The specific problem under consideration is that the quantized control signals transmitted to a given plant by two different communication channels which satisfy some constraint. The tracking performance is measured by the energy of the error response between the output of the plant and the reference signal. Logarithmic quantization law is used in the quantizers. To obtain the best attainable tracking performance of the feedback system, two parameter controller is adopted. By using stochastic dynamic programming approach, the best attainable tracking performance is in terms of the state equation of the plant and the largest relative quantization errors of the logarithmic quantization laws. Furthermore, a sufficient condition for asymptotical tracking is given for the feedback system in an average sense.
Uniting Keypoints: Local Visual Information Fusion for Large-Scale Image Search

In this paper, we propose a novel approach to address the problem of the huge amount of local features for a large-scale database. First, in each image the local features are organized into dozens of groups by performing the standard k-means clustering algorithm on their spatial positions. Second, a compact descriptor is generated to describe the visual information of each group of local features. Since, in each image, thousands of local features are reorganized into only dozens of groups and each group is described by a single descriptor, the total amount of descriptors in a large-scale database will be greatly reduced. Therefore, we can reduce the complexity of the searching procedure significantly. Further, the generated group descriptors are encoded into binary format to achieve the storage and computation efficiency. The experiments on two benchmark datasets, i.e., UKBench and Holidays, with the Flickr1M distractor database demonstrate the effectiveness of the proposed approach.
High-contrast RF correlation imaging of defects in food package seals

Previous research work to detect channel defect in package seals using pulse-echo ultrasound inspired the Backscattered Amplitude Integral (BAI) imaging and RF sample (RFS) imaging techniques. The two former image formation techniques, as well as the newly proposed technique, are evaluated from the same acquired pulse-echo RF data set. All images are formed with a 17.3-MHz 6.35-mm-diameter focused ultrasound transducer (f/2, 173-μm-6 dB pulse-echo lateral beamwidth at the focus, λ-86 μm) scanned over a rectangular grid, keeping the package material in the focal region. All techniques are evaluated with the same set of laboratory-made channel defects: plastic and aluminum foil trilaminate film with 6-, 10-, 15-, 38- and 50-μm-diameter channels filled with water or air. The new RF correlation image technique is formed from the correlation coefficient of each RF echo signal relative to a reference signal that does not pass through a channel defect. Prior to processing, the acquired RF echo signals are first windowed to match within the sample the range where the two materials are bonded, that is, the range where channel defects occur. The statistical study on the laboratory-made channel defects shows that RF correlation technique has the highest detection rate relative to BAI-mode and RFS-mode image for 15-, 10- and 6-μm channel defects. It also is the most effective at smoothing the background, leading to the greatest CNR enhancement.
A Neural Network Method to Adaptive Feature Extraction of Weak DS-CDMA Signals

This paper introduces an on-line unsupervised LEArning neural network (NN) for adaptive feature extraction via Principal component analysis (LEAP) of lower signal to noise ratios (SNR) direct sequence code-division multiple-access (DS-CDMA) signals. The proposed method is based on eigen-analysis of DS-CDMA signals, and exploits the cyclo-stationarity of communication signals adequately. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a period of pseudo noise (PN) sequence (signature waveform). Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Since the synchronous point between the symbol waveform and observation window is a randomize determination point, therefore, each vector must contain all information of a whole period of PN sequence. In the end, the PN sequence and its strength can be extracted by the principal eigenvectors and their associated eigenvalues of autocorrelation matrix blindly. But, the eigen-analysis method is belongs to a batch method, it is difficult to real-time implementation. We can use the LEAP NN method to realize on-line adaptive principal feature extraction and tracking of the weak input DS-CDMA signals effectively.
Dynamic Analysis of China Logistics Agglomeration Based on Clustering Analysis

Base on the theory of location quotient (LQ)and the characteristics of regional logistics, four quotients are constructed. This paper describes the regional distribution of China logistic agglomeration and its formation mechanism by the clustering analysis of statistical data of China Logistic Industry from 1999 to 2004. We conclude that spatial distribution in regional logistics agglomeration shows "one strip-shaped and three regions " pattern, and it is characterized by remarkable orientation of production and consumption, traffic hub, energy production, and export-oriented economy.
A Secure and Robust Authentication Scheme for Video Transcoding

In this paper, we describe a configurable content-based MPEG video authentication scheme, which is robust to typical video transcoding approaches, namely frame resizing, frame dropping and requantization. By exploiting the synergy between cryptographic signature, forward error correction (FEC) and digital watermarking, the generated content-based message authentication code (MAC or keyed crypto hash) is embedded back into the video to reduce the transmission cost. The proposed scheme is secure against malicious attacks such as video frame insertion and alteration. System robustness and security are balanced in a configurable way (i.e., more robust the system is, less secure the system will be). Compressed-domain process makes the scheme computationally efficient. Furthermore, the proposed scheme is compliant with state-of-the-art public key infrastructure. Experimental results demonstrate the validity of the proposed scheme
An uplink burst signal detection method with midamble sequence in TD-SCDMA system

For the TD-SCDMA uplink burst signal detection in low SNR, this paper proposed a method, which uses the highdegreed correlation of the same midamble sequence. As the local midamble sequence matched with the received midamble sequence, there would be a residual carrier or direct current signal. By analyzing its power spectrum, the certain user's uplink burst signal can be detected, and the carrier's frequency can be estimated accurately, too. Studies have shown that the approach can effectively overcome the offset of the residual frequency.