nils-herrmann/clean_scirepeval huggingface dataset:
A nonlinear model for studying synchronous machine dynamic behavior in phase coordinates

A new model for studying synchronous machine dynamic behavior is developed and implemented in the virtual test bed (VTB) simulation environment. The nonlinear synchronous machine model uses quantities in phase coordinates. The advantages of phase-domain modeling for electric machines are discussed. The new model was applied in the study of start-up transients of a synchronous motor. The simulation results are in agreement with those obtained from a standard d-q model, thus validating our model. This paper also demonstrates that symbolic modeling tool can greatly simplify the process of developing complex nonlinear device simulation models
AnalogCast: Full linear coding and pseudo analog transmission for satellite remote-sensing images

In this paper, we propose a novel image coding and transmission scheme called AnalogCast, which is a pseudo analog coding system for transmitting satellite remote-sensing images to large number of receivers. AnalogCast follows the idea originally developed for SoftCast [1-3] but with two special techniques, scale factor estimate based on L-shaped chunk division and scale factor fix curve-fitting model, so that there is no need to transmit side information as is required for SoftCast [1-3]. This improvement can get better mobility, less bandwidth and computational budget than SoftCast [1-3]. For robustness, AnalogCast adopts a mode of GOP (Group of Pictures) interweave structure to smooth image quality and improve perceptual quality when packet loss happens. Experimental results show that the proposed method can work well in low SNR condition in -7dB to 4dB where SoftCast system and JPEG2000 system fall into "cliff effect". And in high SNR condition, our proposed method works even better than SoftCast system at SNR 1.5~3dB and a gain of 1~10dB over JPEG2000 with forward error correction. For robustness, the AnalogCast works better than SoftCast at SNR 8dB when the burst packet loss rate is higher than 35%. And the scheme gets better perceptual quality under packet loss.
IEEE Standards for Advanced Audio and Video Coding in Emerging Applications

The IEEE Standards Association recently approved a comprehensive set of standards that cover advanced audio and video coding for storage and transmission, targeting emerging applications such as Internet media streaming and smart-camera surveillance. The standards' basic goal is to facilitate reliable and efficient exchange of audiovisual data streams between content creators and target users to provide high-quality, recognition friendly end presentations. They enable audiovisual content storage on a wide range of media and transmission over diverse wired and wireless networks, and offer tools for long-term picture reference, learning based picture generation, and object-based description and coding. In addition, they establish audio and video synchronization guarantees, guidelines for operating in error prone environments, and conventions for embedding metadata to describe content and make it discoverable. The standards conform to IEEE's patent policy, providing access to essential patent licensing at a reasonable cost.
Hardware architecture for AVS entropy encoder

In A VS-P2 video compression standard, similar to MPEG-2, entropy coding firstly assembles two dimensional coefficients of each block into a sequence of (Run, Level) combinations serially. As we know, such the serial run-length method is usually undesirable for hardware accelerator and thus, this paper proposes an efficient parallel algorithm to Run-Length Coding, which can determine the (Run, Level) combinations for one row of coefficients from a block in one clock cycle. In addition, Level-based multiple VLC tables switch mechanism (Context-based VLC) is further introduced in AVS-P2 entropy coding module to identify the big variation of probability distribution of (Run, Level) combinations. As a result, table selection for coding the current Level necessarily depends on the previously coded coefficients. Thus, we propose a parallel Looking-Up Table method, which can select the tables for one row of coefficients from a block in one clock cycle. On the other hand, at RDO stage, the calculation of rate term only needs to get the number of bits for each coded signal without the knowledge of its concrete value. Consequently, in hardware design, the Looking-Up Table in pre-coding can be mapped into a series of logic operations and thus much hardware memory can be saved. At the actual entropy coding, we only need to replace the logic operation of pre-coding with the actual 2D-VLC tables. Using our proposed hardware accelerator of AVS entropy coder, the results of simulation and synthesis demonstrate that the computing complexity and memory requirements are both reduced .
Interactive Stereoscopic Video Conversion

This paper presents a system of converting conventional monocular videos to stereoscopic ones. In the system, an input monocular video is firstly segmented into shots so as to reduce operations on similar frames. An automatic depth estimation method is proposed to compute the depth maps of the video frames utilizing three monocular depth cues - depth-from-defocus, aerial perspective, and motion. Foreground/background objects can be interactively segmented on selected key frames and their depth values can be adjusted by users. Such results are propagated from key frames to nonkey frames within each video shot. Equipped with a depth-to-disparity conversion module, the system synthesizes the counterpart (either left or right) view for stereoscopic display by warping the original frames according to their disparity maps. The quality of converted videos is evaluated by human mean opinion scores, and experiment results demonstrate that the proposed conversion method achieves encouraging performance.
Low-complexity and low-memory entropy coder for image compression

A low-complexity and low-memory entropy coder (LLEC) is proposed for image compression. The two key elements in the LLEC are zerotree coding and Golomb-Rice (1966, 1991) codes. Zerotree coding exploits the zerotree structure of transformed coefficients for higher compression efficiency. G-R codes are used to code the remaining coefficients in a variable-length codes/variable-length integer manner resulting in JPEG similar computational complexity. The proposed LLEC does not use any Huffman table, significant/insignificant list, or arithmetic coding, and therefore its memory requirement is minimized with respect to any known image entropy coder. In terms of compression efficiency, the experimental results show that discrete cosine transform (DCT)- and discrete wavelet transform (DWT)-based LLEC outperforms baseline JPEG and embedded zerotree wavelet coding (EZW) at the given bit rates, respectively. For example, LLEC outperforms baseline JPEG by an average of 2.2 dB on the Barbara image and is superior to EZW by an average of 0.2 dB on the Lena image. When compared with set partition in hierarchical trees, LLEC is inferior by 0.3 dB, on average, for both Lena and Barbara. In addition, LLEC has other desirable features, such as parallel processing support, region of interest coding, and as a universal entropy coder for DCT and DWT.
A high-voltage interface circuit for MEMS vibratory gyroscope

In this paper, a high-voltage interface circuit has been designed and implemented for MEMS gyroscope. Continuous-time charge sensitive amplifier with chopping stabilization technique is used in the conversion stage to achieve low noise performance. A closed driving loop for MEMS gyroscope which applying multiple supply voltages is presented. The chip is fabricated in a 0.35um 5V/12V high-voltage process. The test of the chip is performed with a MEMS vibratory gyroscope. The result shows that the driving output of the ASIC can ensure a stable oscillation in the driving axis.
Single underwater image enhancement with a new optical model

As light is attenuated when disseminating in water, the clarity of images or videos captured under water is usually degraded to varying degrees. By exploring the difference in light attenuation between in atmosphere and in water, we derive a new underwater optical model to describe the formation of an underwater image in the true physical process, and then propose an effective enhancement algorithm with the derived optical model to improve the perception of underwater images or video frames. In our algorithm, a new underwater dark channel is derived to estimate the scattering rate, and an effective method is also presented to estimate the background light in the underwater optical model. Experimental results show that our algorithm can well handle underwater images, especially for deep-sea images and those captured from turbid waters.
Multi-innovation observer based fault detection for pitch system of wind turbines

Considering the pitch sensor fault of pitch systems in wind turbines, a multi-innovation observer based fault detection method is proposed. The pitch system can be modeled as a second order differential equation. And then the equation is transformed into a state space model. According to the state space model, a multi-innovation observer is designed to estimate the system state. The pitch sensor fault can be detected through the residual which is generated by comparing the state estimation with the output of an actual system. The simulation results show that the proposed approach is able to detect the pitch sensor fault effectively.
Optimizing JPEG quantization table for low bit rate mobile visual search

Smart phones is bringing about emerging potentials in mobile visual search. Extensive research efforts have been made in compact visual descriptors. However, directly extracting visual descriptors on a mobile device is computationally intensive and time consuming. Towards low bit rate visual search, we propose to deeply compress query images by learning a customized JPEG quantization table in the context of visual search. Distinct from traditional image compression, by incorporating pair-wise image matching precision into distortion measure, we optimize quantization table to seek a better trade-off between image compression rate and visual search performance. An evolutionary algorithm is employed to learn an optimal quantization table. Under MPEG CDVS evaluation framework, extensive evaluation has been done including image retrieval and pair-wise matching over 1 million database images. Experimental results have demonstrated that our optimized quantization table works much better than JPEG default one in terms of retrieval/matching performance vs. a set of different operating points. The proposed low bit rate solution may be easily deployed to smart phones without hardware support, as a useful complement to the ongoing MPEG CDVS standardization efforts.
Manifold-Manifold Distance with application to face recognition based on image set

In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD, we also propose a novel manifold learning approach, which expresses a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between pair of subspaces respectively from one of the involved manifolds. The proposed MMD method is evaluated on the task of Face Recognition based on Image Set (FRIS). In FRIS, each known subject is enrolled with a set of facial images and modeled as a gallery manifold, while a testing subject is modeled as a probe manifold, which is then matched against all the gallery manifolds by MMD. Identification is achieved by seeking the minimum MMD. Experimental results on two public face databases, Honda/UCSD and CMU MoBo, demonstrate that the proposed MMD method outperforms the competing methods.
Spectrum Sensing for Wireless Microphone Signals

Spectrum sensing in the context of wireless microphone (WM) signals is considered in this paper. The spectrum sensing algorithm developed in this study makes use of the property that the bandwidth of the WM signal is narrow compared with the bandwidth of a digital television (DTV) channel. As a result, the power of the WM signal is highly concentrated in the frequency domain and there are apparent peaks contained in the power spectrum density (PSD) of the WM signal even when the signal-to-noise power ratio (SNR) is as low as -20 dB. Based on this property, a simple spectrum sensing algorithm for the WM signal is evaluated using the maximum peak of the estimated PSD as the decision statistic. The probability of false alarm (PFA) for the proposed WM detector is analytically derived. The operating characteristic curves for the spectrum sensing algorithm is determined from computer simulations. Simulation results show that the proposed spectrum sensing algorithm can easily achieve a probability of misdetection (PMD) of 0.1 corresponding to a PFA of 0.01 when the SNR is less than -20 dB and the sensing time equals 10 ms.
Improved low delay distributed video coding

This paper proposes an image partition based approach to enhance side information quality in low delay distributed video coding (DVC). The proposed method employs a checkerboard pattern to group blocks of the Wyner-Ziv frame into two sets, where one set is DPCM encoded and the other set is DVC encoded. These two sets are encoded independently and decoded successively. At decoder, DPCM set will be first reconstructed. Then the temporal concealment tool, such as boundary matching algorithm, is performed to conceal blocks in the DVC set. An improved side information is subsequently obtained for DVC set, based on which a higher compression can be achieved. Simulation results indicate that a more promising performance can be achieved when compared with existing motion extrapolated approach.
Efficient and flexible drift-free video bitstream switching at predictive frames

We propose an efficient and flexible coding scheme inspired by the SP picture technique in H.26L TML; it can achieve drift-free bitstream switching at predictive frames. Firstly, the proposed scheme improves the coding efficiency of the SP frames in H.26L TML by (1) reducing the number of quantization modules in the encoding path; (2) eliminating the mismatch between references for the prediction and the reconstruction; (3) outputting a high quality image for display purpose before the quantization step in the reconstruction loop. Secondly, the proposed scheme allows independent quantization parameters for up-switching and down-switching bitstreams. It can further reduce the switching bitstream size while keeping the coding efficiency of the normal bitstreams. It allows more rapid and frequent down-switching than up-switching. Furthermore, the size of the down-switching bitstream can be much smaller than that of the up-switching one. This is a very desirable feature for any TCP-friendly protocols currently used in most existing streaming systems.
Monocular Tracking 3D People By Gaussian Process Spatio-Temporal Variable Model

Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temporal variable model (GPSTVM), a novel dynamical system modeling method is proposed for learning human pose and motion priors. The GPSTVM provides a low dimensional embedding of human motion data, with a smooth density function that provides higher probability to the poses and motions close to the training data. The low dimensional latent space is optimized directly to retain the spatio-temporal structure of the high dimensional pose space. After the prior on human pose is learned, the particle filtering can be used tracking articulated human pose; particle filtering propagates over time in the embedding space, avoiding the curse of dimensionality. Experiments demonstrate that our approach tracks 3D people accurately.
Concealment of Whole-Picture Loss in Hierarchical B-Picture Scalable Video Coding

H.264/AVC scalable video coding (H.264/AVC SVC), as the scalable extension of H.264/AVC, offers the flexible adaptivity in terms of spatial, temporal and SNR scalabilities for the generated bitstream. However, such compressed video still suffers from the bad playback quality when packet loss occurs over unreliable networks. In this paper, we present an error concealment algorithm to tackle the whole-picture loss problem in H.264/AVC SVC when hierarchical B-picture coding is used to support temporal scalability. In the proposed algorithm, by taking advantage of the temporal relationship among the adjacent video pictures, the motion information of the lost picture is derived simply and efficiently based on the principle of temporal direct mode. Utilizing the derived motion information, the lost picture is concealed by performing motion compensation on the correctly received temporally previous and future video pictures. The experimental results demonstrate that as a post-processing tool, the proposed error concealment algorithm is able to significantly improve both the objective and subjective qualities of the decoded video pictures in the presence of packet losses when compared to the error concealment algorithm used in H.264/AVC SVC reference software. The proposed method can also be applied to H.264/AVC with hierarchical B-picture coding for error concealment.
A low-light image enhancement method for both denoising and contrast enlarging

In this paper, a novel united low-light image enhancement framework for both contrast enhancement and denoising is proposed. First, the low-light image is segmented into superpixels, and the ratio between the local standard deviation and the local gradients is utilized to estimate the noise-texture level of each superpixel. Then the image is inverted to be processed in the following steps. Based on the noise-texture level, a smooth base layer is adaptively extracted by the BM3D filter, and another detail layer is extracted by the first order differential of the inverted image and smoothed with the structural filter. These two layers are adaptively combined to get a noise-free and detail-preserved image. At last, an adaptive enhancement parameter is adopt into the dark channel prior dehazing process to enlarge contrast and prevent over/under enhancement. Experimental results demonstrate that our proposed method outperforms traditional methods in both subjective and objective assessments.
Automatic text segmentation from complex background

In this paper, we proposed an automatic method to segment text from complex background for recognition task. First, a rule-based sampling method is proposed to get portion of the text pixels. Then, the sampled pixels are used for training Gaussian mixture models of intensity and hue components in HSI color space. Finally, the trained GMMs together with the spatial connectivity information are used for segment all of text pixels form their background. We used the word recognition rate to evaluate the segmentation result. Experiments results show that the proposed algorithm can work fully automatically and performs much better than the traditional methods.
Extended Fisherface for face recognition from a single example image per person

We extend Fisherface for face recognition from one example image per person. Fisherface is one of the most successful face recognition methods. However, Fisherface requires several training images for each face, so it cannot be applied to face recognition applications where only one example image per person is available for training. To tackle this problem, we extended the Fisherface method by proposing a method to derive multiple images of a face from one single image. Fisherface is then trained on these derived images. Experimental results on the Bern face database and our 350 subjects database show that our method makes impressive performance improvement compared with the conventional eigenfaces and template matching techniques.
Application of energy storage in high penetration renewable energy system

The application of energy storage technology in high penetration renewable energy systems are reviewed in this paper. Firstly, the characteristics of power system with high penetration renewable energy are analyzed. Secondly, the research direction and research status of domestic and international energy storage technology in high penetration renewable energy system are introduced. Then the application of energy storage technology in various fields is introduced in detail, and the advantages and disadvantages of energy storage in these fields are analyzed. At last, the problems and future development trend of energy storage technology are pointed out.
Stereoscopic video coding based on global displacement compensated prediction

In this paper, we propose a novel algorithm for coding the stereoscopic video sequence based on the global displacement information between the left- and right-view images. MPEG-4 temporal scalable video coding scheme is employed at the baseline, in which the left- and right-view images are compressed at the base and enhancement layer, respectively. To further improve the coding efficiency, some intuitive properties of stereoscopic videos are utilized. One of the major properties lies in the global displacement between the left- and right-view images. In this paper, we propose coding the right-view image with global motion compensated prediction from the left-view image. A further main property of stereoscopic video arises from the fact that, objects simultaneously existing in the left- and right-view images usually result in the very close motion vectors. Based on the global motion information, the motion vectors of the right-view sequence can be easily predicted from that of the left-view sequence. Rate-distortion optimization is also used to select the coding mode. Experimental results show that, compared to the MPEG-4 temporal scalability algorithm for low bitrate stereoscopic video coding, the proposed algorithm can save the bitrate up to 20%.
Isomap Based on the Image Euclidean Distance

Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
A 14-bit differential-ramp single-slope column-level ADC for 640×512 uncooled infrared imager

This paper presents a low-power 14-bit column-level ADC for 640×512 size uncooled infrared imager. A novel differential-ramp single-slope (DRSS) structure is proposed in this work, which achieves 2x faster conversion speed and 3dB higher SNR performance compared with classical single-slope scheme. Moreover, a novel low-power area-saving result-consistent coarse-fine TDC scheme is proposed. The sensor with 17μm pixel pitch has been realized in 0.5 μm 2P3M CMOS process and applied in the thermal imaging system. Power consumption of per ADC is 120 μW. Measurement results demonstrate an average output RMS noise of 0.5LSB and a maximum nonlinearity of 3LSB.