nils-herrmann/clean_scirepeval huggingface dataset:
Development of a remote access control laboratory using xPC target and Virtual Reality Modeling Language

In this paper, we present the development of a remote access control laboratory at RMIT University which allows users to perform experiments on real hardware through the internet. The web based system consists of an internal server linked to an xPC Target real time machine which communicates with a DC Motor control module via relevant DAQ interfaces. In this set up, we attempt to replace the expensive setup of IP camera or video server by a realistic virtual reality model. This enhances the experiment's flexibility and reduces the cost of implementation while providing more interactive visualization and reality feeling of the real system to the users. In addition, the software also features with multiple login database system allowing students to observe or perform experiments under supervision remotely from the admin server.
Yagi-Uda antennas for Terahertz photomixer

In this paper, we present THz Yagi-Uda antennas with high input impedance, achieving greatly improved impedance matching compared to existing THz antennas. We show designs employing both full-wavelength straight-line dipole and full-wavelength U-shaped dipole drivers. Current leakage into the bias line was minimized by applying photonic band gap structures at the bias line. By designing the antenna on a thin substrate, end-fire radiation patterns with high antenna resistance can be obtained even when the substrate has a high dielectric constant. We expect that the proposed Yagi-Uda antennas can achieve increased THz output power over the current state of the art.
Depth Reconstruction From Sparse Samples: Representation, Algorithm, and Sampling

The rapid development of 3D technology and computer vision applications has motivated a thrust of methodologies for depth acquisition and estimation. However, existing hardware and software acquisition methods have limited performance due to poor depth precision, low resolution, and high computational cost. In this paper, we present a computationally efficient method to estimate dense depth maps from sparse measurements. There are three main contributions. First, we provide empirical evidence that depth maps can be encoded much more sparsely than natural images using common dictionaries, such as wavelets and contourlets. We also show that a combined wavelet-contourlet dictionary achieves better performance than using either dictionary alone. Second, we propose an alternating direction method of multipliers (ADMM) for depth map reconstruction. A multiscale warm start procedure is proposed to speed up the convergence. Third, we propose a two-stage randomized sampling scheme to optimally choose the sampling locations, thus maximizing the reconstruction performance for a given sampling budget. Experimental results show that the proposed method produces high-quality dense depth estimates, and is robust to noisy measurements. Applications to real data in stereo matching are demonstrated.
Real-time non-contact breath detection from video using adaboost and Lucas-Kanade algorithm

A simple and economical system is developed to detect human breath in a real time fashion with a normal webcam. By using the optical flow method, a novel approach is proposed to detect the peak of inspiratory phase from a series of frames that are captured from a camera to define the time for trigging X-ray shooting. The issue of using images to detect breath is that the motions of respiratory features on chest are very small, since the camera is attached on the X-ray machine with a significant distance. Therefore, the Lucas-Kanade algorithm is considered to track small motions on chest. Adaboost and corner detection are developed to identify region of chest and to obtain useful feature points for the Lucas-Kanade algorithm. From the experimental conducted, by using the proposed approach, the motion of respiratory phase is easily observed and the predictive error of the peak time is about 0.488 second, approximately 9.75% of the averaging breath cycle. It is acceptable for the X-ray shooting purpose.
Architecture and Performance Analysis of Lossless FFT in OFDM Systems

The design of fast Fourier transform (FFT) architecture is one of the bottlenecks in the implementation of OFDM systems. With the recent progress on the development of lossless transform, its possible applications in communication systems have received more and more attention. In this paper, a lossless integer FFT (IntFFT) architecture based on radix-22 FFT algorithm is analyzed and implemented. By exploring the symmetric property, the overall memory usage is reduced by 27.4% for a 64-point FFT design. The variance of quantization loss for both IntFFT and conventional fixed-point FFT (FxpFFT) is derived. The signal to quantization loss ratio (SQNR) and the bit error rate (BER) performance in system level is also simulated to test the accuracy of IntFFT. Based on the simulation results, IntFFT can yield comparative performance while using less memory usage than FxpFFT designs
Photonic allpass filter: A versatile building block for all-optical signal processing

The allpass filter, since inception in the 1960s, has found its way into a wide range of digital signal processing applications. The structure of the filter also lends itself to robust analog lattice implementations using traditional passive electronic components. In the late 2000s, the versatility of the allpass translated into the photonic domain since it can be easily realized using a variety of basic photonic structures. The simplicity in the implementation makes the allpass structure ideal for serving as the basic building block for complex photonic integrated systems. The increasingly congested frequency spectrum has emphasized the necessity for Radio Frequency (RF) photonics, where the photonic allpass filters can serve as the foundation to all-optical signal processing backends. This paper reviews the concepts of allpass filter design, and discusses its role in photonic systems.
Texture image retrieval using complex directional filter bank

In this paper, the shift-invariant complex directional filter bank (CDFB) is proposed for texture image retrieval. By combining the Laplacian pyramid and the CDFB, a new image representation with an overcomplete ratio of less than 8/3 is obtained. The direction subbands' coefficients are used to form a feature vector for classification. Texture retrieval performance of the proposed representation is compared to those of the conventional transforms including the Gabor wavelet, the contourlet and the steerable pyramid. The overcomplete ratio of the proposed complex directional pyramid is about twice that of the contourlet, and is much lower than those of the other two transforms. An experiment shows that the new transform outperforms the steerable pyramid and the contourlet, and is comparable to the Gabor wavelet in texture image retrieval
A novel semi-automatic control strategy for multi-agent cooperative formation

A decentralized cooperative control strategy engaging offline and online computations for constrained multi-agent vehicle systems is presented in this paper. Intermittent data dropouts may incur concurrently to all vehicle agents in this formulation. Demands on communication links and online data are limited with a perturbed state-feedback strategy, which consists of a static cooperative-state feedback and an online-optimizing perturbation. The former compensates for the interagent communication-coupled dynamics. The latter attends to constraints and data losses, adopting only decoupled objective functions. The state feedback gain is determined in accordance with the interconnection topology of the formation, which is described by a directed graph in this paper. The ADC - asymptotically dissipative constraint, is employed in the overriding control strategy of stabilizing agents such that the inter-operability between remote manoeuvres and local autonomous commands becomes practically realizable. The equilibrium-independent supply rate and storage function are used in the stabilizability condition in this development for the final-state tracking of the cooperative formation problem.
Hand segmentation for hand-object interaction from depth map

Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.
Multiscale edge detection for medical image enhancement

A general framework is presented in this paper for edge detection and enhancement of medical images. The method is based on multiscale analysis using filter banks, and it is adaptive to a large number of features. Initially, an optimal one-scale filter is designed for the required detection. This one-scale filter is further extended to a set of multiscale filters, which in turn are used in designing the filter bank that would provide the desired multiscale responses. Subsequently, the scale space information is optimally combined in a maximum-a-posteriori (MAP) classifier, whose design depends on the desired feature and the resulting filter bank. The method is robust to noisy conditions which are common to medical images in angiography, echocardiography, blood vessels, and others based on ultrasonic imaging, X-ray, and tomography.
Integer- and rational-coefficient M-band wavelet

This paper introduces a family of symmetric biorthogonal M-band wavelets with rational and integer coefficients and up to 2 degrees of regularity. Unlike previous approaches, we construct M-band wavelets by adding simple pre/post-filtering modules along the block boundaries of the traditional block DCT framework. Integer- and rational-coefficient solutions are then obtained from placing further restrictions on each involved component. In other words, we address the design and implementation of fast, efficient pre/post-filters that help improve the polynomial-representing and capturing capability of traditional block transforms such as the DCT. Fast symmetric integer-mapping M-band wavelets can be easily generated by iterating our decomposition scheme on the lowpass subband. Several design examples are presented to demonstrate the validity of the proposed theory.
Adaptive patch-based image denoising by EM-adaptation

Effective image prior is a key factor for successful image denoising. Existing learning-based priors require a large collection of images for training. Besides being computationally expensive, these training images do not necessarily correspond to the noisy image of interest. In this paper, we propose an adaptive learning procedure for learning image patch priors. The new algorithm, called the Expectation-Maximization (EM) adaptation, maps a generic prior to a targeted image to create a specific prior. EM adaptation requires significantly less amount of training data compared to the standard EM, and can be applied to pre-filtered images in the absence of clean databases. Experimental results show that the adapted prior is consistently better than the originally un-adapted prior, and has superior performance than some state-of-the-art algorithms.
Adaptable K-nearest neighbor for image interpolation

A variant of the k-nearest neighbor algorithm is proposed for image interpolation. Instead of using a static volume or static k, the proposed algorithm determines a dynamic k that is small for inputs whose neighbors are very similar and large for inputs whose neighbors are dissimilar. Then, based on the neighbors that the adaptable k provides and their corresponding similarity measures, a weighted MMSE solution defines filters specific to intrinsic content of a low-resolution input image patch without yielding to the limitations of a non-uniformly distributed training set. Finally, global optimization through a single pass Markovian-like network further imposes on filter weights. The approach is justified by a sufficient quantity of relevant training pairs per test input and compared to current state of the art nearest neighbor interpolation techniques.
Integer FFT with Optimized Coefficient Sets

In this paper, the principle of finding the optimized coefficient set of integer fast Fourier transform (IntFFT) is introduced. IntFFT has been regarded as an approximation of original FFT since it utilizes lifting scheme (LS) and decomposes the complex multiplication of twiddle factor into three lifting steps. Based on the observation of the quantization loss model of lifting operations, we can select an optimized coefficient set and achieve better signal-to-quantization-noise ratio (SQNR). A mixed-radix 128-point FFT is used to compare the SQNR performance between IntFFT and other FFT implementations. A fixed-point simulation environment with the presence of additive white Gaussian noise (AWGN) channel is also constructed for comparison purposes.
Theory and lattice structure of complex paraunitary filterbanks with filters of (Hermitian-)symmetry/antisymmetry properties

The theory of the real-coefficient linear-phase filterbank (LPFB) is extended to the complex case in two ways, leading to two generalized classes of M-channel filterbanks. One is the symmetric/antisymmetric filterbank (SAFB), where all filters are symmetric or antisymmetric. The other is the complex linear phase filterbank (CLPFB), where all filters are Hermitian symmetric or Hermitian antisymmetric and, hence, have the linear-phase property. Necessary conditions on the filter symmetry polarity and lengths for the existence of permissible solutions are investigated. Complete and minimal lattice structures are developed for the paraunitary SAFB and paraunitary CLPFB, where the channel number M is arbitrary (even or odd), and the subband filters could have different lengths. With the elementary unitary matrices in the structure of the paraunitary SAFB constrained to be real and orthogonal, the structure covers the most general real-coefficient paraunitary LPFBs. Compared with the existing results, the number of parameters is reduced significantly.
Display dependent coding for 3D video on automultiscopic displays

A method to adaptively code multiview videos has been proposed which uses the depth characteristics of automultiscopic multiview displays. It is found that for the 3D scene seen on multiview displays, regions appearing at large depths are rendered blurry. The proposed method identifies such regions and uses fewer bits to code them. Also, greater number of bits are used for regions which appear sharp on the 3D displays. The overall quality is better than regular AVC/H.264. For compatibility with the framework of scalable multiview coding, we introduce depth scalability. This ensures that the (hierarchical layerwise) encoded video bitstream can be optimally displayed on various displays with different depth properties.
Spatially consistent view synthesis with coordinate alignment

In this paper, we propose a novel method that uses coordinate alignment and background pixel extraction to synthesize highly accurate and spatially consistent intermediate views from a pair of stereo images and disparity maps. In contrast to the traditional depth image-based rendering (DIBR) method, where useful background pixels are discarded in the warping process, the proposed method extracts these background pixels and uses them as candidates for an exemplar-based image in-painting technique (EBIIT) to synthesize realistic content in disocclusion regions. Our second contribution is a coordinate alignment algorithm that aligns disocclusion regions in each view together and simultaneously synthesizes disocclusion regions to enhance spatial consistency across all virtual views. The proposed method compares favorably in quantitative measures to those obtained by existing techniques and has superior potential for stereo-to-multiview conversion.
Statistical image modeling using von Mises distribution in the complex directional wavelet domain

In this paper, a new statistical model is proposed for modeling the nature images in the transform domain. We demonstrate that the von Mises distribution (VM) fits accurately the behaviors of relative phases in the complex directional wavelet subband from different nature images. Moreover, a new image feature based on the VM model is proposed for texture image retrieval application. The VM based feature yields higher retrieval accuracy compared to the energy features and the relative phase features. In addition to magnitude information typically used in many other feature extraction methods, the VM based phase information is also incorporated to further improve the performance.
Hybrid Terminal Sliding-Mode Observer Design Method for a Permanent-Magnet Synchronous Motor Control System

This paper proposes a hybrid terminal sliding-mode observer based on the nonsingular terminal sliding-mode (NTSM) and the high-order sliding-mode (HOSM) for the rotor position and speed estimation in the permanent-magnet synchronous motor control system. An NTSM manifold is utilized to realize both fast convergence and better tracking precision. In addition, a derivative estimator is used to obtain the derivative of the sliding-mode function. Meanwhile, an HOSM control law is designed to guarantee the stability of the observer and eliminate the chattering, so that smooth back-electromotive-force (EMF) signals can be obtained without a low-pass filter. According to the back-EMF equations, the rotor position and speed of the motor can be calculated. Simulation and experimental results are presented to validate the proposed method.
A method for design of Mth-band filters

The objective of this paper is to present a theory, constraints, and a design method for nonlinear-phase halfband and Mth-band filters. Based on a time-domain property, the constraints and properties in the frequency domain are derived. They are the generalization of the well-known conditions for linear-phase Mth-band filters. Having found all necessary conditions, we present the design method based on an eigenfilter that minimizes the mean-squared errors. The design method is also extended to the design of nonlinear-phase Mth-band filters with properties of R-regularity, or equiripple stopband attenuation, or impulse responses that have complex coefficients. Design examples of various Mth-band filters with different properties are presented, discussed, and compared with the linear-phase case.
Subpixel motion estimation without interpolation

We propose a fast subpixel motion estimation method for motion deblurring, where conventional motion estimation algorithms used in video codings are too complex. The new algorithm is a combination of block matching and optical flow. It does not require any interpolation and it does not provide motion compensated frames. Thus it is much faster than conventional methods. Statistical results show that the new algorithm performs quickly and accurately. It also demonstrates compatible performance with the benchmarking full search algorithm, yet uses significantly less amount of time.
Designing allpass filters using the eigenfilter method

The eigenfilter method is used in the design of allpass functions that approximate a given phase response. For a prescribed phase response, it is not possible to express the ideal least-squares error in a quadratic form. Several alternative least-squares error measures are introduced as means to approximate the ideal least-squares error. For each of these alternative errors, a quadratic error is formulated and the allpass coefficients are the elements of the eigenvector corresponding to the minimum eigenvalue of a real, symmetric, and positive definite matrix. Using iterative weighting functions, both the true least-squares and the equiripple phase error solutions can be approximated very fast and accurately with 15-20 iteration steps. Design examples are analysed. Consideration is given to the design of phase equalizers and the approximately linear-phase IIR (infinite impulse response) filter using the allpass eigenfilter method.<<ETX>>
On the eigenfilter design method and its applications: a tutorial

The eigenfilter method for digital filter design involves the computation of filter coefficients as the eigenvector of an appropriate Hermitian matrix. Because of its low complexity as compared to other methods as well as its ability to incorporate various time and frequency-domain constraints easily, the eigenfilter method has been found to be very useful. In this paper, we present a review of the eigenfilter design method for a wide variety of filters, including linear-phase finite impulse response (FIR) filters, nonlinear-phase FIR filters, all-pass infinite impulse response (IIR) filters, arbitrary response IIR filters, and multidimensional filters. Also, we focus on applications of the eigenfilter method in multistage filter design, spectral/spacial beamforming, and in the design of channel-shortening equalizers for communications applications.
Robust mixed /spl Hscr//sub 2///spl Hscr//sub /spl infin// filtering of 2-D systems

This paper deals with several challenging problems of robust filtering for two-dimensional (2-D) systems. First of all, new linear matrix inequality (LMI) characterizations for the /spl Hscr//sub /spl infin// and /spl Hscr/2 norms of 2-D systems are introduced and thoroughly established. Based on these preparatory results, convex (LMI) characterizations for robust /spl Hscr//sub /spl infin//, /spl Hscr//sub 2/, and robust mixed /spl Hscr//sub 2///spl Hscr//sub /spl infin// filtering are derived. The efficiency and viability of the proposed techniques and tools are demonstrated through a set of numerical examples.
An Adaptable $k$ -Nearest Neighbors Algorithm for MMSE Image Interpolation

We propose an image interpolation algorithm that is nonparametric and learning-based, primarily using an adaptive k-nearest neighbor algorithm with global considerations through Markov random fields. The empirical nature of the proposed algorithm ensures image results that are data-driven and, hence, reflect ldquoreal-worldrdquo images well, given enough training data. The proposed algorithm operates on a local window using a dynamic k -nearest neighbor algorithm, where k differs from pixel to pixel: small for test points with highly relevant neighbors and large otherwise. Based on the neighbors that the adaptable k provides and their corresponding relevance measures, a weighted minimum mean squared error solution determines implicitly defined filters specific to low-resolution image content without yielding to the limitations of insufficient training. Additionally, global optimization via single pass Markov approximations, similar to cited nearest neighbor algorithms, provides additional weighting for filter generation. The approach is justified in using a sufficient quantity of training per test point and takes advantage of image properties. For in-depth analysis, we compare to existing methods and draw parallels between intuitive concepts including classification and ideas introduced by other nearest neighbor algorithms by explaining manifolds in low and high dimensions.