nils-herrmann/clean_scirepeval huggingface dataset:
A complete real-time 802.11a baseband receiver implemented on an array of programmable processors

This paper reports the design and software implementation of a real-time digital baseband receiver compliant with the IEEE 802.11a standard on the AsAP2 platform, a DSP chip multiprocessor. The computational platform consists of an array of programmable processors and configurable accelerators interconnected in a 2-D mesh network that are well matched for implementing complex DSP and embedded systems such as wireless and video applications. The receiver has full functionality including frame detection, timing synchronization, carrier frequency offset compensation, and channel equalization. It supports all eight operational modes defined in the standard: 6, 9, 12, 18, 24, 36, 48 and 54 Mbps. The implementation is optimized such that the receiver can obtain a full 54 Mbps rate while using an array of 29 small processors plus Viterbi and FFT accelerators configured to operate at 590 MHz.
An Efficient SDP Based Design for Prototype Filters of M-Channel Cosine-Modulated Filter Banks

The paper presents an efficient semidefinite programming (SDP) based design for prototype filters of cosine-modulated filter banks (CMFBs). We consider a class of near-perfect reconstruction CMFBs with the linear phase prototype filter, which structurally eliminates the amplitude overall distortion. The prototype filter design problem is then formulated into a convex semi-infinite programming problem. Furthermore, to handle the semi-infinite constraints, we use the linear matrix inequality (LMI) characterization of positive trigonometric polynomials to cast the semi-infinite programming problem into SDP one. Finally, convex duality is applied to transform the SDP into another SDP with the minimal number of additional variables, which is efficiently solved. An additional advantage of the proposed method is that we can precisely control the filter specifications.
Full-duplex analog WiFi transport over RSOA-based wavelength-reused digital passive optical networks

We experimentally demonstrated the transparent convergence of distributed antenna system in a digital wavelength division multiplexed passive optical network system employing reflective semiconductor optical amplifier (RSOA) at the remote antenna unit (RAU). The spurious free dynamic range and frequency response of the RSOA is measured to meet WiFi's requirements. RSOA's electrical-to-optical conversion efficiency improves significantly by down converting the WiFi signal to intermediate frequency band. By incorporating the up/down mixer in Central Office and RAU, we experimentally achieve full-duplex WiFi transmission along with 1 Gb/s downlink digital on-off-keying signal in single mode fiber up to 15 km (64QAM), 40 km (16QAM), and 60 km (QPSK) with 1 meter wireless path working in half-duplex mode. The bit-error-rate below the forward error correction threshold (2 × 10-3) is observed in all cases whereas the 1 Gb/s digital downlink signal is error-free up to 20 km of fiber.
Bounded characteristics of wavelet based SDP models

In our recent work, an efficient nonlinear system identification approach using wavelet based State Dependent Parameter (WSDP) models has been proposed and investigated. This model has some unique properties which are very useful for the investigation of its bounded characteristics. This paper presents results on the bounded characteristics of WSDP models. These results reveal the clear relationship between the model's parameters and the boundedness of a WSDP model, providing important insight to the analysis of the BIBO stability of this class of nonlinear models. To validate and illustrate the developed theoretical results, an extensive Monte Carlo (MC) simulation example is provided.
Rate-Distortion Optimized Bitstream Extractor for Motion Scalability in Wavelet-Based Scalable Video Coding

Motion scalability is designed to improve the coding efficiency of a scalable video coding framework, especially in the medium to low range of decoding bit rates and spatial resolutions. In order to fully benefit from the superiority of motion scalability, a rate-distortion optimized bitstream extractor, which determines the optimal motion quality layer for any specific decoding scenario, is required. In this paper, the determination process first starts off with a brute force searching algorithm. Although guaranteed by the optimal performance within the search domain, it suffers from high computational complexities. Two properties, i.e., the monotonically nondecreasing property and the unimodal property, are then derived to accurately describe the rate-distortion behavior of motion scalability. Based on these two properties, modified searching algorithms are proposed to reduce the complexity (up to five times faster) and to achieve the global optimality, even for those decoding scenarios outside the search domain.
Adaptive Fuzzy Filtering for Artifact Reduction in Compressed Images and Videos

A fuzzy filter adaptive to both sample's activity and the relative position between samples is proposed to reduce the artifacts in compressed multidimensional signals. For JPEG images, the fuzzy spatial filter is based on the directional characteristics of ringing artifacts along the strong edges. For compressed video sequences, the motion compensated spatiotemporal filter (MCSTF) is applied to intraframe and interframe pixels to deal with both spatial and temporal artifacts. A new metric which considers the tracking characteristic of human eyes is proposed to evaluate the flickering artifacts. Simulations on compressed images and videos show improvement in artifact reduction of the proposed adaptive fuzzy filter over other conventional spatial or temporal filtering approaches.
Mechanical parameter estimation of motion control systems

Variation of relevant mechanical parameters (i.e. moment of inertia, viscous coefficient) highly influences the performance of servo motor drive systems as it affects the motor's mechanical dynamics. This paper, on one hand, reviews relevant approaches to the estimation of such parameters of motion control systems, identifying their strengths and weaknesses. On the other hand, an efficient alternative to such an estimation problem is presented. This approach is relatively simple, thus computationally advantageous to implement, yet very robust to external disturbances such as measurement noise, mechanical resonance within the drive system.
Adaptive PD networks tracking control with full-state constraints for redundant parallel manipulators

In this paper, a detailed study to apply an adaptive proportional-derivative networks (APDNs) tracking control is proposed for redundant parallel manipulators with full-state constraints. APDNs is designed — based on the combination of the nonlinear compensation, the PD nonlinear sliding part, and feedforward radial basic function neural networks (RBFNs) with online learning. By using the Lyapunov method, the stability of the closed-loop system in the case of full-state constraints is proven. The proposed control strategy has better dynamic performance and higher robustness in comparison with PD-sliding mode control and augmented nonlinear PD control. The effectiveness proposed control is illustrated by simulation.
Signal-adapted binary tree-structured paraunitary filter bank design

Signal adapted filter bank design that uses energy compaction as the adaptation criterion has been applied in the past to quadrature mirror filter (QMF) banks, as well as to M-channel FIR paraunitary filter banks. This analysis was extended to tree-structured filter banks having signal adapted, FIR QMF banks at each level. It showed that fewer parameter allocations to represent filter coefficients are required for tree structured filter banks with similar energy compaction performance, compared to an equivalent structure filter bank with M channels.
Markov Random Field Model-Based Edge-Directed Image Interpolation

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

Deep Convolutional Neural Networks (CNN) are the state-of-the-art performers for the object detection task. It is well known that object detection requires more com- putation and memory than image classification. In this work, we propose LCDet, a fully-convolutional neural net- work for generic object detection that aims to work in em- bedded systems. We design and develop an end-to-end TensorFlow(TF)-based model. The detection works by a single forward pass through the network. Additionally, we employ 8-bit quantization on the learned weights. As a use case, we choose face detection and train the proposed model on images containing a varying number of faces of different sizes. We evaluate the face detection perfor- mance on publicly available dataset FDDB and Widerface. Our experimental results show that the proposed method achieves comparative accuracy comparing with state-of- the-art CNN-based face detection methods while reducing the model size by 3× and memory-BW by 3 - 4× compar- ing with one of the best real-time CNN-based object de- tector YOLO . Our 8-bit fixed-point TF-model pro- vides additional 4× memory reduction while keeping the accuracy nearly as good as the floating point model and achieves 20× performance gain compared to the floating point model. Thus the proposed model is amenable for em- bedded implementations and is generic to be extended to any number of categories of objects.
An FPGA implementation of a time-to-digital converter with a ring oscillator and buffers

A Time-to-Digital Converter (TDC) is widely used in applications that need to measure the time interval between events. Previous designs based on a feedback loop and an extended delay line suffer from poor accuracy caused by the non-linearity of a delay line. To overcome the poor accuracy in the previous TDC, this paper proposes a novel TDC that uses a ring oscillator to measure the interval by counting the number of cycles in the interval. To reduce the precision drop by the measurement with the ring oscillator, an additional delay line is used in the TDC. The proposed TDC is implemented by using Xilinx Spartan-6 LX9 FPGA with 20MHz oscillator.
Adaptive scanning methods for wavelet difference reduction in lossy image compression

This paper describes methods for adapting the scanning order through wavelet transform values used in the wavelet difference reduction (WDR) algorithm of Tian and Wells (1996). These new methods are called adaptively scanned wavelet difference reduction (ASWDR). ASWDR adapts the scanning procedure used by WDR in order to predict locations of significant transform values at half thresholds. These methods retain all of the important features of WDR: low-complexity, region of interest, embeddedness, and progressive SNR. They improve the rate-distortion performance of WDR so that it is essentially equal to that of the SPIHT algorithm of Said and Pearlman (1996) when arithmetic compression is not employed. When arithmetic compression is used, then the rate-distortion performance of the ASWDR algorithms is only slightly worse than SPIHT. The perceptual quality of ASWDR images is clearly superior to SPIHT.
Design of Cosine-Modulated Pseudo-QMF Banks Using Semidefinite Programming Relaxation

The paper proposes a new approach for the design of M-channel pseudo-quadrature mirror filter (QMF) banks. First, the convex hull of 2Mth band linear phase filters admitting linear phase spectral factors is analytically described by semidefinite programming (SDP). Then, the prototype filter design is cast into an SDP problem, which is efficiently solved. Design examples are presented to illustrate the effectiveness of the proposed method and to evaluate the design performance in comparison with the existing designs.
Extended C-Band Tunable Multi-Channel InP-Based Coherent Transmitter PICs

We demonstrate a fully integrated multi-channel InP-based coherent transmitter photonic integrated circuits (PICs) with extended C-band tunability, operating at 33 and 44 Gbaud per channel under 16-QAM dual-polarization modulation. PICs are demonstrated integrating up to 14-channels enabling multi-Tb/s total PIC capacities.
Semantic video segmentation: Exploring inference efficiency

We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames. The key idea is to combine best of two worlds: semantic co-labeling and more expressive models. Our formulation enables us to perform inference over ten thousand images within seconds and makes the system amenable to perform video semantic segmentation most effectively. On CamVid dataset, with TextonBoost unaries, our proposed method achieves up to 8% improvement in accuracy over individual semantic image segmentation without additional time overhead. The source code is available at https: //github. com/subtri/video inference.
Optoelectronic properties of CdSe nanoparticles and their application to bulk hetero-junction solar cells

The CdSe nanoparticles have been synthesized by hot-injection method, and thermal annealing was used to improve the crystallinity of synthesized nanoparticles and reconstruct the surface of nanoparticles. The synthesized and annealed CdSe nanoparticles were characterized by TEM, XRD and PL spectra. After thermal annealing followed by a solvent exchange process, the CdSe nanoparticles were used to fabricate bulk hetero-junction solar cells of CdSe/P3HT active layer. The morphology control of nanoparticle-polymer composite films was also investigated. By varying only the concentration of the binary solvent mixture, the phase separation behavior between the nanoparticles and blended polymer could well be controlled from micrometer scale to nanometer scale, yielding uniform and reproducible active layer films.
Motion vector processing using the color information

In this paper, we investigate color distribution around object edges and further exploit this information for motion vector reliability analysis. Since the motion vectors are often estimated only based on luminance component, the resulting motion vectors may become unreliable for the areas with similar intensity. By making use of the color information for the received residual energy calculation, these unreliable motion vectors can effectively be identified. Moreover, the motion boundaries can be easily detected due to stronger residual energy distribution on the object boundaries. This characteristic can also be used to assist the motion vector processing in motion-compensated frame interpolation so that the interpolated object structures can be well maintained. Experimental results show that using same motion vector processing approach but with additional consideration on color information outperforms other methods.
An effective example-based learning method for denoising of medical images corrupted by heavy Gaussian noise and poisson noise

Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.
Robust Bayesian method for simultaneous block sparse signal recovery with applications to face recognition

In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic data experiments as well as the multiple measurement face recognition problem.
Single Image Super-Resolution Using Local Geometric Duality and Non-Local Similarity

Super-resolution (SR) from a single image plays an important role in many computer vision applications. It aims to estimate a high-resolution (HR) image from an input low- resolution (LR) image. To ensure a reliable and robust estimation of the HR image, we propose a novel single image SR method that exploits both the local geometric duality (GD) and the non-local similarity of images. The main principle is to formulate these two typically existing features of images as effective priors to constrain the super-resolved results. In consideration of this principle, the robust soft-decision interpolation method is generalized as an outstanding adaptive GD (AGD)-based local prior. To adaptively design weights for the AGD prior, a local non-smoothness detection method and a directional standard-deviation-based weights selection method are proposed. After that, the AGD prior is combined with a variational-framework-based non-local prior. Furthermore, the proposed algorithm is speeded up by a fast GD matrices construction method, which primarily relies on the selective pixel processing. The extensive experimental results verify the effectiveness of the proposed method compared with several state-of-the-art SR algorithms.
A study of two-channel complex-valued filterbanks and wavelets with orthogonality and symmetry properties

We investigate two-channel complex-valued filterbanks and wavelets that simultaneously have orthogonality and symmetry properties. First, the conditions for the filterbank to be orthogonal, symmetric, and regular (for generating smooth wavelets) are presented. Then, a complete and minimal lattice structure is developed, which enables a general design approach for filterbanks and wavelets with arbitrary length and arbitrary order of regularity. Finally, two integer implementation methods that preserve the perfect reconstruction property of the filterbank are proposed. Their performances are evaluated via experimental results.
Efficient stereo-to-multiview synthesis

From a rectified stereo image pair, the task of view synthesis is to generate images from any viewpoint along the baseline. The main difficulty of the problem is how to fill occluded regions. In this paper, we present a new method for view synthesis that is both fast and accurate. Occlusions are filled using color and disparity information to produce consistent pixel estimates. Results are comparable to current state-of-the-art methods in terms of objective measures while computation time is drastically reduced. This work has applications in free-viewpoint television, angular scalability for 3D video coding/decoding, and stereo-to-multiview conversion.
Combined image plus depth seam carving for multiview 3D images

Multiview 3D displays have to multiplex a set of views on a single LCD panel. Due to this, each view has to be downsampled by a considerable amount leading to loss of details. In this paper, we extend the seam carving technique for adaptive resizing of images. It is proposed that the depth information be used along with the image pixel intensity values for resizing. This results in better resized multiview images. It is clear from the results presented that the object structure is maintained when the proposed method is used as compared to vanilla seam carving.
Curvature Scale Space Application to Distorted Object Recognition and Classification

Contour classification methods which operate directly on an image are greatly affected by small magnitude transformations to the image. In this paper, a contour classification method is developed which takes advantage of curvature scale space (CS2) and a linear support vector machine (SVM) classifier. The CS2 representation boasts invariance to transformations including: scaling, rotation, translation and noise. In addition, the linear SVM is a robust tool for classification problems involving multiple labels. The combination of these tools produces a classifier well suited for object recognition in photographs where distortion is present.
Tally: A web-based subjective testing tool

Collecting data from human subjects is a critical and time-consuming part of video quality assessment. In this work, we introduce Tally, a modern web-based tool to help automate data collection for subjective video experiments. It is designed with decoupled voting and viewing interfaces, allowing Tally to be used for both 2D and 3D displays. Its web-based design provides ubiquitous data access, the ability to share and collaborate on projects, and compatibility with mobile devices. Standard ITU test methods are supported, and we are releasing the software as open source for continued development.
Efficient Bit Allocation and Rate Control Algorithms for Hierarchical Video Coding

Hierarchical structure is a useful tool for providing the necessary scalability in adapting to the variety of channel environments. For schemes involving hierarchical picture structures, bit allocation, and rate control algorithms are vital components for improving video codec performance. Since conventional bit allocation schemes do not efficiently consider the hierarchical structure characteristics, it is difficult to optimize the video quality at an arbitrary bitrate. Similarly, conventional quantization parameter decision methods are not appropriate for controlling the bitrate generated by a codec using a hierarchical encoding structure. In this paper, we propose an effective bit allocation scheme that assigns the target number of bits to pictures or macroblocks (MBs) and improves the overall quality of images encoded by a hierarchical-based encoder. A rate control scheme is also proposed to ensure that the generated bitrate is equal to the assigned target bitrate. From the simulation results, the proposed schemes outperformed conventional methods from a rate-distortion perspective, by efficiently controlling the bitrate of the MB unit. The algorithms regulated the generated bits to achieve the target bits by using the proposed linear R-Q model.