nils-herrmann/clean_scirepeval huggingface dataset:
Multi-pitch Estimation for Speech Mixture Based on Multi-length Windows Harmonic Model

Harmonics of two concurrent speech signals may be overlapped in their mixing spectrum from the short time window analysis. We propose a multi-length windows (MLW) method based on harmonic model to estimate the multiple pitches of the single-channel mixed speech. The longest window is used to distinguish all the potential harmonic peaks and initially estimate the other pitch. And the other windows rectify the initial estimation. The advantages of our method include: i) it can obtain more accurate prominent pitch with autocorrelation; ii) it overcomes the low frequency resolution of the traditional harmonic model for estimating the other pitch. The simulation results show that the proposed algorithm outperforms the minimum mean square error (MMSE) method and short window (SW) harmonic method.
A Compact All-Solid-State Repetitive Pulsed Power Modulator Based on Marx Generator and Pulse Transformer

In this paper, a compact all-solid-state pulsed power modulator was designed based on Marx generator and pulse transformer. It is consisted of a six-stage Marx generator using power MOSFETs as switches to generate primary voltage pulses and a pulse transformer through a closed magnetic core to boost and convert the polarity of the output high-voltage (HV) pulse. A MOSFET driver circuit based on optical–electrical isolation was designed for controlling the Marx generator. It shows that the output pulse shapes, especially the rising and falling phases, are determined by the design of the pulse transformer such as its leakage inductance, distributed capacitance, magnetization inductance, and step-up ratio. The designed modulator is able to generate HV pulses with amplitude ranging from −35 to 35 kV, repetition frequency up to 10 kHz, minimum pulsewidth 250 ns. Various types of plasma discharges, including air microdischarge for local treatments, plate-to-plate air dielectric-barrier discharge, underwater discharge, are able to be driven with good reproducibility by this compact pulse modulator.
3D reconstruction of human enamel Ex vivo using high frequency ultrasound

This paper introduces a new scanning system which collects structural information of tooth surface and enamel-dentine junction. A 3D motorized scanning stage is used to control the moving trajectories of an A-mode transducer so as to acquire the echoes from the surface of a tooth. The enamel in a 3D form will be reconstructed with this system. Despite some discontinuous areas appearing in the 3D image, the 3D representations of human molars well duplicate the real samples and the thickness of enamel can be measured.
Audio personalization using head related transfer function in 3DTV

In 3DTV, head related transfer function (HRTFs) can promote immersive feeling of listeners because it contains spatial information on sound source. Audio can be customized through using personalized HRTF. So, listening distortions are caused if HRTFs do not match anthropometric parameters concerning different listeners. In this paper, personalized method is proposed to customize individual HRTF based on non-negative matrix factorization (NMF) and support vector regression (SVR). The anthropometric parameters are selected and high dimensional HRTFs are decomposed into low dimensional matrix using NMF. Nonlinear regression model is derived between the selected anthropometric parameters and low dimensional matrix by SVR. Experimental results demonstrated that personalized HRTF has better performance than using the same HRTF for different listeners in 3DTV audio.
A Novel Graph-Based Segmentation Method for Breast Ultrasound Images

Breast cancer occurs to 8% women during their lifetime, and is a leading cause of death among women. Breast ultrasound (BUS) image segmentation which is the essential process for further analysis, is a very challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information based on the robust graph-based (RGB) segmentation method and the particle swarm optimization (PSO) algorithm. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of the PSO algorithm, the RGB segmentation method is performed to segment the filtered image. To validate our method, experiments have been conducted on datasets. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that our method can accurately segment BUS images.
A miniaturized high-efficiency GaAs HBT power amplifier used in TD-SCDMA handset application

This paper is the first to report a monolithic HBT power amplifier for TD-SCDMA cellular phones which achieves a high efficiency and linearity. The two-stage MMIC integrates input and interstage matching circuits, and active bias circuits in a single chip with a chip size as small as 0.91×0.98 mm2. The amplifier obtains a power-added efficiency (PAE) of 43% (16%) and 28.5 dB (23.8 dB) of the gain at the high and low operation mode under the 3.4 V supply. In addition, the adjacent channel leakage power is below -39 dBc and -48 dBc at 1.6 MHz and 3.2 MHz offset respectively with QPSK modulation. The MMIC offers the potential both for low cost production due to small chip size, stable voltage supply, and high performance at the same time.
Automated trading based on biclustering mining and fuzzy modeling

More and more records or charts of historical financial data are used for technical analysis, hoping to identify patterns that can be exploited to achieve excess profits. Technical analysis has been widely used in the real stock market to forecast stock price or stock trading points. The good association of technical indicators can obtain good prediction results in stock markets. But the selection of technical indicators is also a tough problem. In this paper, we introduce a forecasting model incorporating biclustering algorithm with a new fuzzy inference method. Biclustering algorithm discover biclusters which are regarded as trading patterns. And a new fuzzy inference method is used for determining trading points. The proposed forecasting model (called BM-FM) was used for predicting three real-world stock data. The experiment is designed by comparing the profit ratio in TPP-based strategy, IPLR and IPLR-ANN with the profit ratio in our forecasting model. According to experimental results, it is indicated that our model obtains more earnings and higher profit ratio than other comparative methods.
Rapid Image Registration for Extended-Field-of-View Ultrasound

Extended field-of-view (EFOV) can acquire a full field of vision, which can help doctors to make more objective and accurate diagnosis. Current EFOV techniques suffer from the low computation speed due to the large amount of ultrasound data of image blocks to be registered. This paper describes a novel technique to register 2D ultrasound images and form EFOV images with significantly reduced computation time by using a less number of image frames. With an assumption that the moving direction and speed for the probe are nearly identical during the data collection, we are able to estimate the moving speed of the probe by registering the frames one by one at the beginning of data collection for roughly determining the frame interval for the whole image sequence. Using the estimated frame interval, a smaller number of frames can be selected. The overlapping area and searching range for the registration of the selected frames can be pre-determined. To further improve the computational efficiency and robustness of the registration, we innovatively propose to select fewer blocks which are regarded as the most valid blocks based on the importance of image content in each block. The experimental results show that our method can produce as good expanded images as the traditional method does. Moreover, the computation time is significantly reduced.
A novel method for ultrasound elastography using the mutual information and the phase information

Ultrasound elastography is a technique used for clinical imaging of soft tissue by deformation estimation. This paper introduces a 2-D strain imaging technique using the mutual information and the phase information. This paper introduces a novel 2D strain imaging technique based on the mutual information between pre and post compressed radio frequency (RF) signals to define time delays. The mutual information and the phase information between two corresponding windows around the samples of the pre- and the post-compressed signals are employed to generate a similarity measurement. The time delay is considered to be the time shift between two windows for which the similarity degree between pre- and the post-compressed signals reaches a maximum. Another two methods used to estimate displacements are compared to our method in this paper. The effectiveness of the proposed method is investigated through phantom experiments. The experimental results show that the proposed method can estimate displacement map effectively.
Fully distributed acoustic source localization in wireless sensor network

Each node in wireless sensor network has limited energy for communication and computation, so how to reduce the energy consumption efficiently is an important research problem. In this paper, we propose a fully distributed expectation maximization algorithm (FDEM) for acoustic source localization to reduce communication cost significantly. It just requires local data transmission and simple computation. Each sensor node transmits energy observations to its nearest neighbors (nodes within one-hop communication range) and also receives energy observations from its nearest neighbors. Long range wireless communication is avoided and the energy is saved. Simulation results for a static source localization and a moving source tracking demonstrate that the FDEM algorithm can provide a good tradeoff between localization accuracy and communication cost.
Local spectral feature extraction and compaction for HRTFs by nonnegative tensor factorization

Head-related transfer functions (HRTFs) depend on frequencies, sound directions, and individuals with high dimensionality and complicated structure. In practical applications, it is difficult to utilize the original HRTFs. Nonnegative matrix factorization (NMF) has been used to reduce the dimension of original HRTFs by vectorization and might lose the information of their natural structure. In this paper, to keep the inherent multi-dimensional structure, a tensor is firstly used to describe HRTFs. Then local spectral features in a low-dimensional space are extracted from HRTFs tensor by nonnegative tensor factorization (NTF). Due to their nonnegativity, the high-dimensional HRTFs tensor can be explained by an additive linear combination of these local spectral features. The simulations demonstrate that NTF achieves higher compression ratio with lower reconstruction error than NMF for HRTFs.
A Kinect-based scan path planning method for ultrasound imaging

In hospitals, the doctors at sonography department often need to examine many patients every day. To alleviate the working burden, an automatic examination using some machines is needed. In this paper, by using the depth data captured by Kinect, the 3-D scene could be reconstructed with Visualization Toolkit, then the scan path could be automatically acquired with our method based on the 3-D scene. At last, the 3-D translation device, based on the scan path, is adopted to control the movement of ultrasound probe to scan the tissue surface. And the experimental results show that the method is available for planning an appropriate scan path and making the ultrasound probe touch the tissue surface accurately in the mechanical scanning.
Two-Step Spherical Harmonics ESPRIT-Type Algorithms and Performance Analysis

Spherical arrays have been widely used in direction-of-arrival (DOA) estimation in recent years, and the high-resolution estimation of signal parameter via rotational invariance technique (ESPRIT) was developed in the spherical harmonics domain. However, the spherical harmonics ESPRIT (SHESPRIT) cannot estimate the DOA when the elevation approaches 90°. To solve this problem, we present a two-step SHESPRIT (TS-SHESPRIT) based on two new recurrence relations of complex spherical harmonics. Furthermore, we develop a real-valued two-step SHESPRIT (RTS-SHESPRIT) that exploits a unitary matrix to obtain a real-valued relation between the signal subspace and the steering matrix to further reduce the computational complexity. However, the number of sources that are estimated by RTS-SHESPRIT is limited. Therefore, we propose the semi-RTS-SHESPRIT method, which reduces the computational complexity associated with eigenvalue decomposition (EVD) and avoids the limitations of RTS-SHESPRIT. Relative to SHESPRIT and TS-SHESPRIT, RTS-SHESPRIT and semi-RTS-SHESPRIT reduce the computational burden by 75% during EVD. Furthermore, we derive the mean square errors (MSEs) of the above algorithms and significantly simplify the MSE expressions. Different expressions for the MSEs are due to different recurrence relations used by different SHESPRIT-type algorithms. All proposed two-step SHESPRIT-type algorithms have higher accuracy than traditional SHESPRIT. The simulation results demonstrate the satisfactory performance of our methods.
HRTF personalization modeling based on RBF neural network

A tensor is used to describe head-related transfer functions (HRTFs) dependent on frequencies, sound directions and anthropometric parameters. It can represent the multi-dimensional structure of measured HRTFs. To construct a personalization model, high-order singular value decomposition (HOSVD) is firstly applied to extract individual core tensor as the outputs of the model. Some important anthropometric parameters are selected by Laplacian score and correlation analysis between all measured parameters and the individual core tensor. They act as the inputs of the personalization model. Then a nonlinear model is constructed based on radial basis function (RBF) neural network to predict individual HRTFs according to the measured anthropometric parameters. Compared with back-propagation (BP) neural network method, simulation results demonstrate the better performance for predicting individual HRTFs in the midsaggital plane at high elevations.
CT to ultrasound registration for non-invasive kinematic analysis of knee joints

Accurate measurement of the motion of individual bones in the knee joint is extremely important for the recovery of patients with motor dysfunction. The most recognized clinical method for measuring is inserting tantalum beads into the bone act as landmark prior to imaging using X-ray equipment at present. Nevertheless, the approach is invasive and the exposure to ionizing radiation is inevitable. This paper puts forward a new non-invasive, precise 3D kinematic analysis system based on registration of CT-scans and B-mode ultrasonic images which is able to capture the flexion of the bones in the knee joint accurately. The experimental results in this paper indicate that the system is capable of measuring the motion of the bones in the knee joint with high precision which is able to meet the need of 3D kinematic analysis for the knee joint.
Median Filters Used for Volume Reconstruction in Freehand 3-D Ultrasound

Freehand 3-D ultrasound imaging has been playing an important role in obtaining entire 3-D impression of tissues and organs. Reconstructing a sequence of irregularly located B-scan images into a volume data set with regular voxel arrays is one of key procedures for visualization and data analysis. In this study, we investigated the feasibility of median filters for volume reconstruction in freehand systems. Three median filters were proposed to filter noises and obtain voxel intensities. Qualitative and quantitative comparison results were presented to demonstrate the performance of median filters for volume reconstruction. Compared with conventional distance weighted (DW) interpolation, the three median filters were able to preserve more image details and reconstruct volume data with better quality
Single channel music source separation based on harmonic structure estimation

Single channel music separation is a useful but difficult problem in audio signal processing field. In this paper a new method is proposed. The method consists of three stages: estimating the harmonic structure of each source in every frame based on iteration of the mixed spectral peaks, clustering the estimated harmonics into the signals they belong to with pitch and formant information, and synthesizing the music source in time domain. Moreover, the method can solve the octave overlapping problem which is a tough one in the single channel source separation area. The experimental results show that our algorithm can separate the mixed signal and obtains a good subjective audio quality.
Automated correction of spin-history related motion artefacts in fMRI: Simulated and phantom data

This paper concerns the problem of correcting spin-history artefacts in fMRI data. We focus on the influence of through-plane motion on the history of magnetization. A change in object position will disrupt the tissue's steady-state magnetization. The disruption will propagate to the next few acquired volumes until a new steady state is reached. In this paper we present a simulation of spin-history effects, experimental data, and an automatic two-step algorithm for detecting and correcting spin-history artefacts. The algorithm determines the steady-state distribution of all voxels in a given slice and indicates which voxels need a spin-history correction. The spin-history correction is meant to be applied before standard realignment procedures. To obtain experimental data a special phantom and an MRI compatible motion system were designed. The effect of motion on spin-history is presented for data obtained using this phantom inside a 1.5-T MRI scanner. We show that the presented algorithm is capable of detecting the occurrence of a displacement, and it determines which voxels need a spin-history correction. The results of the phantom study show good agreement with the simulations.
Visualizing white matter structure of the brain using Dijkstra's algorithm

An undirected weighted graph may be constructed from diffusion weighted magnetic resonance imaging data. Every node represents a voxel and the edge weights between nodes represent the white matter connectivity between neighboring voxels. In this paper we propose and test a new method for calculating trajectories of fiber bundles in the brain by applying Dijkstra's shortest path algorithm to the weighted graph. Subsequently, the resulting tree structure is pruned, showing the main white matter structures of the brain. The time consumption of this method is in the order of seconds.
Interactive Segmentation and Visualization of DTI Data Using a Hierarchical Watershed Representation

Magnetic resonance diffusion tensor imaging (DTI) measures diffusion of water molecules and is used to characterize orientation of white matter fibers and connectivity of neurological structures. Segmentation and visualization of DT images is challenging, because of low data quality and complexity of anatomical structures. In this paper, we propose an interactive segmentation approach, based on a hierarchical representation of the input DT image through a tree structure. The tree is obtained by successively merging watershed regions, based on the morphological waterfall approach, hence the name watershed tree. Region merging is done according to a combined similarity and homogeneity criterion. We introduce filters that work on the proposed tree representation, and that enable region-based attribute filtering of DTI data. Linked views between the visualizations of the simplified DT image and the tree enable a user to visually explore both data and tree at interactive rates. The coupling of filtering, semiautomatic segmentation by labeling nodes in the tree, and various interaction mechanisms support the segmentation task. Our method is robust against noise, which we demonstrate on synthetic and real DTI data.
Frequency domain volume rendering by the wavelet X-ray transform

We describe a wavelet based X-ray rendering method in the frequency domain with a smaller time complexity than wavelet splatting. Standard Fourier volume rendering is summarized and interpolation and accuracy issues are briefly discussed. We review the implementation of the fast wavelet transform in the frequency domain. The wavelet X-ray transform is derived, and the corresponding Fourier-wavelet volume rendering algorithm (FWVR) is introduced, FWVR uses Haar or B-spline wavelets and linear or cubic spline interpolation. Various combinations are tested and compared with wavelet splatting (WS). We use medical MR and CT scan data, as well as a 3-D analytical phantom to assess the accuracy, time complexity, and memory cost of both FWVR and WS. The differences between both methods are enumerated.
Diatom contour analysis using morphological curvature scale spaces

A method for shape analysis of diatoms (single-cell algae with silica shells) based on extraction of features on the contour of the cells by multi-scale mathematical morphology is presented. After building a morphological contour curvature scale space, we present a method for extracting the most prominent features by unsupervised cluster analysis. The number of extracted features matches well with those found visually in 92% of the 350 diatom images examined.
GPU-ASIFT: A fast fully affine-invariant feature extraction algorithm

This paper presents a method that takes advantage of powerful graphics hardware to obtain fully affine-invariant image feature detection and matching. The chosen approach is the accurate, but also very computationally expensive, ASIFT algorithm. We have created a CUDA version of this algorithm that is up to 70 times faster than the original implementation, while keeping the algorithm's accuracy close to that of ASIFT. It's matching performance is therefore much better than that of other non-fully affine-invariant algorithms. Also, this approach was adapted to fit the multi-GPU paradigm in order to assess the acceleration potential from modern GPU clusters.
Accelerating Wavelet Lifting on Graphics Hardware Using CUDA

The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as massively parallel coprocessors through NVidia's CUDA compute paradigm. The three main hardware architectures for the 2D DWT (row-column, line-based, block-based) are shown to be unsuitable for a CUDA implementation. Our CUDA-specific design can be regarded as a hybrid method between the row-column and block-based methods. We achieve considerable speedups compared to an optimized CPU implementation and earlier non-CUDA-based GPU DWT methods, both for 2D images and 3D volume data. Additionally, memory usage can be reduced significantly compared to previous GPU DWT methods. The method is scalable and the fastest GPU implementation among the methods considered. A performance analysis shows that the results of our CUDA-specific design are in close agreement with our theoretical complexity analysis.