nils-herrmann/clean_scirepeval huggingface dataset:
Resource Allocation for Error Resilient Video Coding Over AWGN Using Optimization Approach

The number of slices for error resilient video coding is jointly optimized with 802.11a-like media access control and the physical layers with automatic repeat request and rate compatible punctured convolutional code over additive white gaussian noise channel as well as channel times allocation for time division multiple access. For error resilient video coding, the relation between the number of slices and coding efficiency is analyzed and formulated as a mathematical model. It is applied for the joint optimization problem, and the problem is solved by a convex optimization method such as the primal-dual decomposition method. We compare the performance of a video communication system which uses the optimal number of slices with one that codes a picture as one slice. From numerical examples, end-to-end distortion of utility functions can be significantly reduced with the optimal slices of a picture especially at low signal-to-noise ratio.
Scalable phase extraction methods for phase plane motion estimation

To efficiently compute the phase difference (PD) between two complex numbers, two novel approaches are described. The problem of fast PD computation is central in many applications. As a case study, the main focus is on the phase correlation technique that is used for motion estimation. Starting from the problem statement, the system requirements are dealt with showing how PD requires a remarkable amount of computational resources. Reduced complexity techniques are then proposed and specifically tailored to suit the application needs. Each solution is completely implemented both in 0.25 µm as well as 0.13 µm CMOS. The so-called LUT-ROT exhibits noteworthy figures in terms of area occupation, delay and power dissipation, saving nearly 50% in terms of area and power when compared to recent work on this subject.
Polyphase interpretation of empirical image interpolation

We observe several characteristics of empirical image interpolating algorithms and contribute four novel concepts and claims. First, we interpret well-known classification-based filtering algorithms in terms of their polyphase components. We examine the underlying principles behind the various fixed-scale linear interpolating kernels. Second, we conceptually extend the properties of the multiple filters to two dimensions to analyze frequency domain characteristics common to all empirically-designed interpolating filters. Third, we propose a general linear filter for image interpolation, which uses a universal magnitude response and zero-phase. Finally, the proposed filter is further generalized to support arbitrary scaling factors. We claim that at any scaling factor, the proposed algorithm yields low-complexity at a minimal loss of high image-quality with the ability to interpolate diverse image content.
Filter-Bank Based Super-Resolution for Rotated and Blurry Undersampled Images

The ill-posed problem of image super-resolution (SR) is considered in the frequency domain using a filter bank approach. A low resolution (LR) imaging system is modeled in this paper using different fractional translation, rotation and blurs for each LR image. Synthesis filters are then found which provide a minimal time averaged mean squared error (TAMSE) reconstruction under a wide sense stationary (WSS) signal assumption. Presented simulations for many kinds of images demonstrate the effectiveness of the algorithm.
Wideband high gain circularly polarized Fabry-Perot Resonator antenna with asymmetric superstrates

A method of using multiple dielectric slabs with small-footprint to design wideband circularly polarized Fabry-Perot Resonator antenna is presented. This approach employed two thin truncated dielectric slabs as partially reflecting surface (PRS) with positive reflection phase gradient, which can significantly improve the 3-dB gain as well as axial ratio (AR) bandwidths of the antenna. The proposed design with the very compact size of 1.5 × 1.5 × 0.7 λo3 yielded impedance matching, and axial ratio bandwidths are approximately 63.3% and 48.1%, respectively. Additionally, the proposed antenna achieved a 3-dB gain bandwidth of 58.8% with a peak gain of 15 dBi.
On perfect-reconstruction allpass-based cosine-modulated IIR filter banks

In this paper, we consider the theory and design of the cosine-modulated infinite-impulse-response (IIR) perfect reconstruction (PR) filter banks. The analysis and synthesis filters are cosine-modulated versions of prototype filter. Moreover, the polyphase components of the prototype filter are allpass filters. Necessary and sufficient conditions on the allpass filters are derived such that the filter bank is a PR system. The proposed filter bank is the only known IIR cosine-modulated filter bank. A design procedure for the proposed filter bank is outlined and design examples are given to demonstrate the theory.<<ETX>>
Multiresolution Image Representation Using Combined 2-D and 1-D Directional Filter Banks

In this paper, effective multiresolution image representations using a combination of 2-D filter bank (FB) and directional wavelet transform (WT) are presented. The proposed methods yield simple implementation and low computation costs compared to previous 1-D and 2-D FB combinations or adaptive directional WT methods. Furthermore, they are nonredundant transforms and realize quad-tree like multiresolution representations. In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.
Quality Enhancement for Motion JPEG Using Temporal Redundancies

The paper proposes a pixel-based post-processing algorithm to enhance the quality of motion JPEG (MJPEG) by exploiting the temporal redundancies of the decoded frames. The technique permits reconstruction of the high frequency coefficients lost during quantization, thereby reducing ringing artifacts. Based on the linearization of the quantization function, the error between the estimated and original coefficients is analyzed for both cases of ideal and real video sequences. Blocking artifact reduction is verified by a reduction in the variance of this coefficient error. The condition of valid motion vectors to get quality improvement is considered based on these errors. The algorithm is also extended to find the optimal filter for a general estimation scheme based on an arbitrary number of frames. Results in visual and peak signal-to-noise ratio improvement using both integer and subpixel motion vectors are verified by simulations on video sequences.
A quadratic-constrained least-squares approach to linear phase orthonormal filter bank design

The design of M-channel paraunitary linear-phase filter banks is considered. It is shown that the symmetry polarities of linear-phase filters must be a particular pattern if the filters have narrow transition band and high stopband attenuation. The general design problem is formulated as a quadratic-constrained optimization problem which offers fast convergence. An example is given to demonstrate the theory.<<ETX>>
Cable-Geometric Error-Prone Approach for Low-Latency Interconnection Networks

Interconnection network is a main concern in the architecture design of highly parallel systems such as high density data centers and supercomputers that reach millions of endpoints, e.g., 10M cores for Sunway TaihuLight system. As the number of endpoints of such systems has gradually increased to meet the higher computing and storage demand, the interconnection network is required to provide a low latency and high communication bandwidth, i.e., less than 1-microsecond latency across systems with a link bandwidth is greater than 100GB/s. In the low-latency design context, our primary aim is to provide a novel solution based on the technology-driven approaches: (1) cable-geometric small-world network topology with custom routing, and (2) the use of an FEC(forward error correction)-free error-prone mechanism for a high-speed low-reliable link. Both can present a logarithmic diameter low-radix network with low-latency and high-bandwidth switches, although approximate computing or ABFT (algorithm-based fault tolerance) design is required for parallel applications.
Design of Oversampled Filter Bank Based Widely Linear OQPSK Equalizer

In this paper, the design and analysis of multirate filter bank (FB) based OQPSK widely linear (WL) oversampled equalizers for frequency selectively channels (FSCs) are studied. We focus on OQPSK signal transmissions in FSCs with various amount of fractionally spaced equalization using WL processing under the framework of FB. We explicitly represent a OQPSK communications system as a FB which allows the study of conditions for perfect reconstruction using a finite length polynomial equalizer. Furthermore, WL FB based MMSE oversampled equalizer designs operating at various sampling rates are presented. Computer simulations are simulated to study the performance of various types of equalizers in FSCs.
Using Multi Decision Diagram in Model Checking

Model checking[1] is an automatic verification technique for finite concurrent systems. In this method, the assertion is verified by exhaustively searching over the state space. However, the number of states of the system will grow exponentially with the number of processes. It limits model checker to handle with complex systems. In explicit model checking[1], system states are explored one-by-one and stored in memory explicitly, so the verified system is restricted by the memory resource. Most of the memory is consumed by the hash table which contains the visited states and the queue of states whose successors are already generated. In this paper, we will present a new way of storing the visited states by using a tree. We show that our approach is memory efficient. Organization of the report: Section 1 is the introduction, and section 2 introduces PAT model checker. Section 3 describes how to implement the tree storing the visited states. Section 4 presents the heuristic to improve the performance for the tree. Section 5 is the experiment result. Lastly section 6 is the conclusion and future work.
Two-fold symmetric diamond-shaped 2-D filter design with quincunx sampling application

This paper presents the design of a two-fold symmetric, linear-phase, diamond-shaped FIR filter. The design is based on a Semi-Definite Program (SDP) of moderate dimension in which the passband and the stopband are expressed as intersecting regions of second order trigonometric polynomials. The two-fold symmetric filter is a more general representation when compared with its four-fold symmetric counterpart but it has much higher complexity and involve more complicated trigonometric functions. However the performance of the filter outperforms all the currently available filter designs and its effectiveness is emphasized by an example of quincunx sampling application presented at the end of this paper.
Using ANFIS to predict picking position of the fruits sorting system

Sorting is one of the important tasks in production line and it has an appreciable effect to the homogeneous of products. Using vision system to increase productivity in automatic sorting system has attracted many researchers. In this paper, a vision sorting system based on computer that can identify the position and properties of fruits has been recommended. The sorting system uses the high resolution camera placed on the top of the body on conveyor belt. With the images are captured by the camera, the software will perform the algorithms to identify the characteristics of the object (for example tomatoes, passion fruits) to sort and give the location of objects to database, then the data of objects can be used by pick and place process that synchronize with controller of the actuators. The sorting process including some steps as detecting the object, determining the object properties like color, size, and shape,…, locating of the object, and using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to calculate the actual gripping position. A five layer neural network of ANFIS is used to adjust input and output parameters of membership function in a fuzzy logic controller. The hybrid learning algorithm is used for training this network. In this algorithm, the least square estimation method is applied for the tuning of linear output membership function parameters and the error hybrid method is used to tune the nonlinear input membership function parameters, it is possible to predict position exactly for actuators and reduce interpolation errors of the system. The algorithms will be experimented on tomatoes and passion fruits, results will be analyzed and evaluated to calculate the stable of the vision system, so that the productivity and accurate efficiency is appropriate.
From Personal Experience to Global Reputation for Trust Evaluation in the Social Internet of Things

Trust has been exploring in the era of Internet of Things (IoT) as an extension of the traditional triad of security, privacy and reliability for offering secure, reliable and seamless communications and services. It plays a crucial role in supporting IoT entities to reduce possible risks before making decisions. However, despite a large amount of trust-related research in IoT, a prevailing trust evaluation model has been still debatable and under development. In this article, we clarify the concept of trust in the Social Internet of Things (SIoT) ecosystems and propose a comprehensive trust model called REK that incorporates third-party opinions, experience and direct observation as the three Trust Indicators. As the convergence of the IoT and social network, the SIoT enables any types of entities (physical devices, smart agents and services) to establish their own social networks based on their owners' relationships. We leverage this characteristic for inaugurating Experience and Reputation, which are originally two concepts from social networks, as the two paramount indicators for trust. The Experience and Reputation are characterized and modeled using mathematical analysis along with simulation experiments and analytical results. We believe our contributions offer better understandings of trust models and evaluation mechanisms in the SIoT environment, particularly the two Experience and Reputation models. This paper also opens important trust-related research directions in near future.
Lapped transforms of arbitrary block size

This paper presents the theory, structure, design, and implementation of lapped transforms (LTs) with arbitrary block size. These new transforms are basically M-channel linear phase perfect reconstruction filter banks (LPPRFBs) with all filters having the same length L=KM+/spl beta/, where K and /spl beta/ are any integers, K/spl ges/1,/spl beta//spl ges/0. This is the truly general lapped transform where the amount of overlap is not constrained to be a multiple of the number of channels. The design approach is based on the transform's parameterization by a lattice structure that can be proven to be complete, minimal, robust and modular. Design examples show that the new family of LTs is comparable to the ones in previous works while providing more flexibility in the trade-off between transform performance and complexity.
Robust and reduced-order filtering: new LMI-based characterizations and methods

This paper addresses several challenging problems of robust filtering. We derive new linear matrix inequality (LMI) characterizations of minimum variance or H/sub 2/ performance and demonstrate that they allow the use of parameter-dependent Lyapunov functions while preserving tractability of the problem. The resulting conditions are less conservative than earlier techniques, which are restricted to fixed (not parameter-dependent) Lyapunov functions. The remainder of the paper discusses reduced-order filter problems. New LMI-based nonconvex optimization formulations are introduced for the existence of reduced-order filters, and several efficient optimization algorithms of local and global optimization are proposed. Nontrivial and less conservative relaxation techniques are presented as well. The viability and efficiency of the proposed approaches are then illustrated through computational experiments and comparisons with existing methods.
An Investigation of Dehazing Effects on Image and Video Coding

This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation. It operates at a faster speed than current methods and can avoid halo effects by using the median operation. We then consider the dehazing effects in compression by investigating the coding artifacts and motion estimation in cases of applying any dehazing method before or after compression. We conclude that better dehazing performance with fewer artifacts and better coding efficiency is achieved when the dehazing is applied before compression. Simulations for Joint Photographers Expert Group images in addition to subjective and objective tests with H.264 compressed sequences validate our conclusion.
Depth-Adaptive Deep Neural Network for Semantic Segmentation

In this paper, we present the depth-adaptive deep neural network using a depth map for semantic segmentation. Typical deep neural networks receive inputs at the predetermined locations regardless of the distance from the camera. This fixed receptive field presents a challenge to generalize the features of objects at various distances in neural networks. Specifically, the predetermined receptive fields are too small at a short distance, and vice versa. To overcome this challenge, we develop a neural network that is able to adapt the receptive field not only for each layer but also for each neuron at the spatial location. To adjust the receptive field, we propose the depth-adaptive multiscale (DaM) convolution layer consisting of the adaptive perception neuron and the in-layer multiscale neuron. The adaptive perception neuron is to adjust the receptive field at each spatial location using the corresponding depth information. The in-layer multiscale neuron is to apply the different size of the receptive field at each feature space to learn features at multiple scales. The proposed DaM convolution is applied to two fully convolutional neural networks. We demonstrate the effectiveness of the proposed neural networks on the publicly available RGB-D dataset for semantic segmentation and the novel hand segmentation dataset for hand-object interaction. The experimental results show that the proposed method outperforms the state-of-the-art methods without any additional layers or preprocessing/postprocessing.
General synthesis procedures for FIR lossless transfer matrices, for perfect-reconstruction multirate filter bank applications

A recently reported procedure (see ibid., vol.ASSP-35, p.476-492, Apr. 1987 and Proc.  1987, p.2169-2172) for the design of M-channel perfect-reconstruction quadrature mirror filter (QMF) bands uses the concept of lossless alias-component matrices. The synthesis of such QMF banks centers around the generation of a lossless M*M finite-impulse response (FIR) transfer matrix E(z). Recent results for such generation have been somewhat ad hoc, i.e. not sufficiently general. A general procedure is outlined for the generation of such transfer matrices. The procedure is based on a cascaded-lattice structure, derived from a state-space viewpoint. The structure is such that it generates only M*M lossless FIR transfer matrices (regardless of parameter values), and conversely, any M*M lossless FIR transfer matrix can be obtained by a suitable choice of parameters. These parameters turn out to be angles theta /sub k/, and the structure is such that the number of angles is minimal. A design example is presented to demonstrate the main results.
Experimental Verification of Multilevel Coded Modulation for 16-Ary Constellations

We present an experimental demonstration of multilevel coded modulation (MLCM) for 16-ary constellations. Performance of MLCM coding is compared for two 16 QAM constellations: 1) a phase noise optimized and 2) a square constellation. We quantify experimentally the phase noise regimes where the optimized constellation outperforms square 16 QAM. While MLCM coding is designed for one linewidth at a given symbol time, for the same overhead we show it outperforms uniform rate coding over a wide range of linewidths. The MLCM coding strategy minimizes block error rate under some simplifying assumptions. We show that under realistic conditions, the MLCM also gives good post forward error correction and quantify the advantage over uniform rate coding experimentally.
A Wideband Dual-Cavity-Backed Circularly Polarized Crossed Dipole Antenna

A novel wideband dual-cavity-backed circularly polarized (CP) crossed dipole antenna is presented in this letter. The exciter of the antenna comprises two classical orthogonal straight dipoles for a simple design. Dual-cavity structure is employed to achieve unidirectional radiation and improve the broadside gain. In particular, the rim edges of the cavity act as secondary radiators, which contribute to significantly enhance the overall CP performance of the antenna. The final design with an overall size of 0.57λ<sub>o</sub> × 0.57λ<sub>o</sub> × 0.24λ<sub>o</sub> where λ<sub>o</sub> is the free-space wavelength at the lowest CP operating frequency of 2.0 GHzb yields a measured –10 dB impedance bandwidth (BW) of 79.4% and 3 dB axial-ratio BW of 66.7%. The proposed antenna exhibits right-handed circular polarization with a maximum broadside gain of about 9.7 dBic.
Appearance model based face-to-face transform

In this paper, a novel approach to the face-to-face transform is presented. The face-to-face transform is a technique which transforms one person's facial actions to the others. In general, 3D models of faces are used for such transformation. Therefore, the facial action parameters must be estimated from the 2D input images, which is not an easy task. On the contrary, our proposed approach is based on the 2D appearance model, instead of the 3D model, so that the model is acquired by learning directly from training images. To achieve this, we investigate making use of the hidden Markov model (HMM) framework, which models the correspondence between an input face and the other one as well as the appearances of both faces. The experimental results show the effectiveness of the proposed method.