nils-herrmann/clean_scirepeval huggingface dataset:
Design and orthonormality analysis of trijunctional spherical microphone array

Spherical microphone arrays have been widely studied for sound recording, beamforming, and sound-field analysis due to the advantage of its spherical symmetry. A trijunctional sampling scheme for spherical microphone array is proposed in this paper. All its samples are on the junctions of three great circles which are respectively chosen from three different series with mutually orthogonal rotation axes. So the trijunctional sampling scheme holds a rotational symmetry with respect to each rotation axis, which makes it easy to obtain the coordinates and the weights of samples. Besides, an analysis of spherical harmonics orthonormality indicates that the array redundancy of trijunctional sampling scheme is much lower than that of equiangular and Gaussian sampling schemes.
A 24–40GHz monolithic balanced low noise amplifier

LNA is one of the key components in the receiver system. With the development of millimeter wave (MMW) wireless communication, more and more high performance MMW LNAs are required for the MMW systems. Balanced LNA is widely used because of better return loss, stability and linearity, comparing to the single-ended LNA. Most of the balanced LNAs are structured by Lange couplers, and the performance of the balanced LNA is largely affected by the Lange coupler.
Continuous Monitoring of Sonomyography, Electromyography and Torque Generated by Normal Upper Arm Muscles During Isometric Contraction: Sonomyography Assessment for Arm Muscles

The aim of this study is to demonstrate the feasibility of using the continuous signals about the thickness and pennation angle changes of muscles detected in real-time from ultrasound images, named as sonomyography (SMG), to characterize muscles under isometric contraction, along with synchronized surface electromyography (EMG) and generated torque signals. The right biceps brachii muscles of seven normal young adult subjects were tested. We observed that exponential functions could well represent the relationships between the normalized EMG root-mean-square (RMS) and the torque, the RMS and the muscle deformation SMG, and the RMS and the pennation angle SMG for the data of the contraction phase, with exponent coefficients of (), , and , respectively. In addition, the preliminary results also demonstrated linear relationships between the normalized torque and the muscle deformation as well as the pennation angle with the ratios of and , respectively. The overall mean for the regressions was approximately 0.9 and the overall mean relative root mean square error (RRMSE) smaller than 15%. The potential values of SMG together with EMG to provide a more comprehensive assessment for the muscle functions should be further investigated with more subjects and more muscle groups.
Housing price increase and the number of newly-registered marriages

This paper takes housing price index (X) as the independent variable, brings marriage number (Y) as the dependent variable into regression equation, and adopts the method of panel data regression analysis, with the purpose of erasing the regional fixed effect that remains unchanged with the change of time in different provinces in China, and control the time fixed effect that remains unchanged with the change of time in different provinces in China, and meanwhile money supply amount is used as the instrumental variables to erase omitted variables' deviation influence caused by the variables which do not change with time or regions; then Hausman test is used to check whether the coefficients gained in random effect are obviously different from the coefficients gained in fixed effect. In the end, from the perspective of economics and sociology, the paper discusses and explores the causes why the core cities' housing price index of the provinces has influence on marriage number.
A MMIC distributed amplifier with bandwidth of 8–40GHz

A MMIC (Monolithic Microwave Integrated Circuit) distributed amplifier with bandwidth of 8-40GHz was successfully developed. We proposed a novel structure for the distributed amplifier, that is separating the amplifier into two sections and connecting the two sections by a capacitor. By this means, we can get higher gain than the traditional distributed amplifier. The amplifier was successfully designed and fabricated with 0.15¿m PHEMT technology and the chip area is 2.8mm*1mm. Measured results show that the gain is 11dB in the bandwidth of 8-40GHz, and the input and output return loss are more than 10dB in the whole band.
Accurate Image Registration Using SIFT for Extended-Field-of-View Sonography

Extended-field-of-view (EFOV) sonography has been proven to be capable of illustrating panoramic ultrasound images. In EFOV technology, accurate registration of original images is the basis. Conventional registration methods based on block matching cannot provide adequate accuracy when the relative rotations among the images are large. To register the images with improved accuracy, this paper proposes a novel methodological framework based on SIFT (Scale Invariant Feature Transform) and Levenberg-Marquardt (L-M) algorithms to obtain the transforming matrices. The SIFT is used to detect plenty of distinctive invariant features which can be used for reliable matching between the images with overlapping regions. After obtaining a series of pairwise matching points, we use the L-M algorithm, a non-linear optimization, to obtain the translation and rotation parameters between two adjacent images. Using the parameters, any pair of two adjacent images can be transformed into one coordinate system to generate a panorama. Preliminary experiments have been performed and the effectiveness of our approach is demonstrated.
MVP algorithm based prediction method for virulent proteins in bacterial pathogens

Identifying whether the uncharacterized protein belongs to a virulent protein or not is important. If it is virulent protein, it is very useful for studying its virulence mechanisms in pathogens as well as designing antiviral drugs. Particularly, with a large number of virulent protein sequences discovered in recent years, it is urgent to develop an automated method to predict the bacterial virulent proteins. In this work, a sequence encoding scheme based on combing DC (Dipeptide Composition) and PseAA (Pseudo Amino Acid) is introduced to represent protein samples. However, this sequence encoding scheme would correspond to a very high dimensional feature vector. A DR (Dimensionality Reduction) algorithm, the so-called MVP (Maximum variance projection) is introduced to extract the key features from the high-dimensional space and reduce the original high-dimensional vector to a lowerdimensional one. Finally, our jackknife test results thus obtained are quite encouraging, which indicate that the above method is used effectively to deal with this complicated problem of predicting virulent proteins in bacterial pathogens.
A multi-objectively-optimized graph-based segmentation method for breast ultrasound image

Segmentation of medical image, as the most essential and important step in the computer-aided diagnosis system, can greatly influence the system performance. Better segmentation to a great extent means better performance. Among many proposed segmentation algorithms, graph-based segmentation has become a hot one in the past few years because of the simple structure and rich theories. After the robust graph-based segmentation method (RGB) was introduced in 2010, a parameter-automatically-optimized robust graph-based segmentation method (PAORGB) was presented in 2013 as well, to optimize the two key parameters of RGB utilizing the particle swarm optimization algorithm (PSO). However, single-objectively-optimized PAORGB cannot well guarantee the global optimization. Therefore, this paper continues the work of PAORGB and proposes a multi-objectively-optimized robust graph-based segmentation method (MOORGB) to further improve the performance of RGB. Experimental results have shown that MOORGB can get better segmentation results from breast ultrasound images compared to PAORGB.
The decomposition and compression of HRTF based on adaptive fourier decomposition

Head-Related Transfer Function (HRTFS) is the key to many applications in spatial audio. Its large amount of data makes it difficult to make real-time implementation. Reducing HRTF data is necessary and important. In this paper, we apply a new developed signal decomposition theory, named Adaptive Fourier Decomposition (AFD), to decompose and compress HRTF data, comparing with traditional Fourier's convergence property and PCA's compression property. Simulation results show that the proposed AFD-based decomposition and compression method enables evident performance improvement for HRTF.
Improving Decoupled Spherical Harmonics ESPRIT Using Structured Least Squares

Spherical arrays have been widely used in direction-of-arrival estimation in recent years, and most algorithms in the spherical harmonics domain typically simultaneously obtain the 2-D angles. In this paper, we first use the structured least squares (SLS) to improve the performance of the decoupled spherical harmonics estimation of the signal parameter via rotational invariance technique (D-SHESPRIT) and propose D-SHESPRIT-SLS. Second, we derive the mean square error of the proposed algorithm and compare estimated results with theoretical results. The simulation results demonstrate the satisfactory performance of the proposed method.
A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition

Recognizing smiles is of much importance for detecting happy moods. Gabor features are conventionally widely applied to facial expression recognition, but the number of Gabor features is usually too large. We proposed to use Pyramid Histogram of Oriented Gradients (PHOG) as the features extracted for smile recognition in this paper. The comparisons between the PHOG and Gabor features using a publicly available dataset demonstrated that the PHOG with a significantly shorter vector length could achieve as high a recognition rate as the Gabor features did. Furthermore, the feature selection conducted by an AdaBoost algorithm was not needed when using the PHOG features. To further improve the recognition performance, we combined these two feature extraction methods and achieved the best smile recognition rate, indicating a good value of the PHOG features for smile recognitions.
An unsupervised feature ranking scheme by discovering biclusters

In this paper, we aim to propose an unsupervised feature ranking algorithm for evaluating features using discovered biclusters which are local patterns extracted from a data matrix. The biclusters can be expressed as sub-matrices which are used for scoring relevant features from two aspects, i.e. the interdependence of features and the separability of instances. The features are thereby ranked with respect to their accumulated scores from the total discovered biclusters before the pattern classification. Experimental results show that this proposed algorithm can yield comparable or even better performance in comparison with the well-known Fisher Score, Laplacian Score and Variance Score using several UCI data sets.
Low intensity pulsed ultrasound increases the mechanical properties of the healing tissues at bone-tendon junction

The re-establishment of bone-tendon junction (BTJ) tissues is involved in many trauma and reconstructive surgeries. A direct BTJ repair requires a long period of immobilization which may be associated with a postoperative weak knee. In this study, we investigated if low-intensity pulsed ultrasound treatment increases the material properties of healing tissues at bone-tendon junction (BTJ) after partial patellectomy using rabbit models. Standard partial patellectomy was conducted on one knee of twenty four rabbits which were randomly divided into an ultrasound group and a control group. The bony changes of BTJ complexes around the BTJ healing interface were measured by anteroposterior x-ray radiographs; then the volumetric bone-mineral density (BMD) of the new bone was assessed using a peripheral computed tomography scanner (pQCT). The stiffness of patellar cartilage, fibrocartilage at the healing interface and the tendon were measured in situ using a novel noncontact ultrasound water jet indentation system. Not only significantly more newly formed bone at the BTJ healing interface but also increased stiffness of the junction tissues were found in the ultrasound group compared with the controls at week 18. In addition, the ultrasound group also showed significantly 44% higher BMD at week 6 than controls.
Ultrasound elastography based on the normalized cross-correlation and the PSO algorithm

Ultrasound elastography is a common medical imaging applied in medical applications since it can provide the tissue hardness information. Most ultrasound elastography techniques are window based methods. The key of the quasi-static ultrasound elastography is to do the similarity measure between two windows from pre- and post-compression to compute the displacement. In view of this situation, the window size has an important influence on the strain images quality. In this paper, a reasonable method utilizing PSO algorithm to search for the optimal window length for different data is brought out to solve this problem. The displacement map can be estimated with the optimal window length using the normalized cross correlation method. And a spatial derivative operator is applied to estimate the strain map. The strain images with the fixed window length 12 and 50 and the optimal window length using PSO algorithm are compared in this paper. Results show that using PSO algorithm to search for the optimal window length can improve the SNR and CNR of strain images.
Development of an Optical Elastomicroscopy for Imaging Tissue Elasticity in High Resolution

Articular cartilage is a thin but crucial tissue layer which plays an important role in diarthrodial joints. During the last decade, the quantitative ultrasound technique, especially the high frequency ultrasound elasticity measurement technique, called as ultrasound elastomicroscopy (UEM), has been widely employed as a versatile modality to investigate the articular cartilage. Ultrasound elastomicroscopy can measure the deformation of different layers of articular cartilage under compression, but whether the propagation of ultrasound in tissues was changed or not with the imposed load is still unknown. In this study, we developed a new optical method to validate the accuracy and reliability of the ultrasound elastomicroscopy. Layered sponge phantoms were prepared for the experiments. During the compression, both the ultrasound and optical elastomicroscopy systems were employed to record the ultrasound signals and optical images from inside and the font of the phantoms. The deformations of the phantoms from different layers were tracked from both ultrasound and optical signals and were then compared. The preliminary results suggest that the measurements of the ultrasound elastomicroscopy system could be validated.
An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer

The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.
Imaging of Discharge Plasma Channel Evolution Process of Microsecond Wire Explosion in Air

The discharge plasma channel (DPC) evolution process is observed by taking time-series self-emission intensified charge-coupled device (iCCD) photos and simultaneous shadow images. By adding neutral density filters before iCCD, the subtle structure of the DPC self-emission is observed. It is interesting that a treelike structure is observed soon after the voltage breakdown. Long exposure time photos demonstrate that this structure existed for hundreds of nanoseconds. An inner homogeneously luminous column arises latterly, forming a two-layer self-emission structure. Then, this column gradually occupies the whole DPC. Shadow images show that the expanding DPC maintains good axial uniformity, manifesting the long-standing uneven treelike structure only has limited impact on the energy distribution and the expansion process. The calculated expansion rate reaches its maximum value (4.75 km/s) not at the power peak, but 0.24  $\mu \text{s}$  after that. Then, the expansion rate undergoes fast drop and slow decline. In addition, it seems that the distributions of related plasma parameters experience an evolution from nonuniform to uniform.
Two-Stage Decoupled DOA Estimation Based on Real Spherical Harmonics for Spherical Arrays

Spherical arrays have been widely used in direction-of-arrival (DOA) estimation in recent years. In this paper, we develop a unitary matrix to transform the complex spherical harmonics into real ones and obtain a real-valued covariance matrix after forward–backward (FB) averaging. Based on this transformation, a two-stage decoupled approach (TSDA) is proposed to decouple the estimation of the elevation and the azimuth. First, we propose a unitary spherical harmonics estimation of signal parameter via rotational invariance technique using one recurrence relation of real spherical harmonics to obtain the elevation estimation. Because of the limitation of using another recurrence relation to estimate the azimuth; second, we propose a unitary spherical harmonics root multiple signal classification (U-SHRMUSIC) to obtain the azimuth. Moreover, using the phase shift characteristic of real spherical harmonics, we also propose a low-complexity U-SHRMUSIC that can further decrease the computational load in the second stage. The proposed TSDA can not only achieve more accurate DOA estimation via FB averaging and two-stage estimation after decoupling but also reduce the computational complexity by exploiting real operations. Computer simulations, especially simulations of the real environment, validate the effectiveness of the proposed method.
PEBB-based shunt active power filter for shipboard power systems

The future vision of naval electric distribution systems describes a system capable of supplying an electric propulsion system as well as other demanding loads such as high power pulsed loads. The system must also be flexible and reconfigurable. Such a system, containing numerous power electronic devices, will require appropriate compensation systems to maintain some minimum acceptable level of power quality. In this paper, we propose and implement a power electronics building block (PEBB) based shunt active power filter with digital control for future naval electric distribution systems. Various configurations of the software phase locked loop (SPLL), as well as various harmonic extraction strategies are analyzed by comparing them with respect to their steady state and transient performance.
A reduced order model of head-related impulse responses based on independent spatial feature extraction

A reduced order model for large numbers of head-related impulse responses (HRIRs) is proposed for real-time three-dimensional (3D) sound rendering. Independent spatial features are firstly extracted from measured HRIRs using independent component analysis (ICA). These spatial feature vectors are not only mutually statistical independent but independent from all the measured azimuths. Therefore filtering sound sources with numerous HRIRs is transformed into filtering them using the extracted lower-dimensional feature vectors. Furthermore balanced model truncation (BMT) method in a state space is adopted to reduce the order of each independent feature vector. Simulation results demonstrate that our proposed algorithm not only acquires better approximated accuracy but has significantly lower computational complexity.
Near-Field Source Localization Using Spherical Microphone Arrays

A new method is proposed for joint range and bearing (azimuth and elevation) estimation of multiple near-field acoustic sources using observations collected by a spherical microphone array. First, Spherical Fourier transform (SFT) is used to construct the array signal model in the spherical harmonics domain to decouple range and bearing information. Then the relation among the spherical harmonics of three adjacent degrees is exploited to build the recursive relationship of the signal subspace. Using Eigenvalue decomposition (EVD), bearings are estimated based on the eigenvalues and simultaneously the steering matrix can be represented by the signal subspace. Finally, range is estimated using the energy ratios of the elements of the steering matrix in the spherical harmonics domain. The algorithm can avoid parameter pairing and multi-dimensional searching. It has lower computational complexity than that of the Multiple signal classification (MUSIC) method. The performance is evaluated by Monte-Carlo simulations and the estimation root meansquare errors are compared to the corresponding Cramer-Rao bounds (CRBs) and those of MUSIC range estimates, which demonstrate the validity of the proposed algorithm.
Tensor modeling and interpolation for distance-dependent head-related transfer function

Head-related transfer functions (HRTFs) are multi-dimensional functions of source position, frequency, and anthropometric parameters. In this paper, a tensor is adopted to represent HRTFs dependent on elevation, azimuth, and frequency. The core tensor dependent on a source distance is extracted to capture most of the variations in the original tensor space. Interpolation between the low dimensional core tensors can obtain the desired HRTFs at an arbitrary distance according to the measurements. The tensor model compresses the original high dimensional data and realizes simple interpolation with high accuracy. Simulation results demonstrate the performance of the tensor modeling and interpolation for distance-dependent HRTFs.
Sparsity-based higher order ambisonics reproduction via LASSO

The under-determined problem in Higher Order Ambisonics (HOA) makes it hard for larger accurate reproduction area especially at high frequencies. In this paper, the least absolute shrinkage and selection operator (LASSO) algorithm is used to solve the problem by obtaining the sparse representation of the original plane wave field. The algorithm minimizes the squared error between the original and reproduced microphone signals with constraint on plane wave gains, so as to select a few nonzero plane wave gains for reproduction. Simulation results demonstrate that sparsity-based plane wave field reproduction via LASSO achieves small reproduction errors and outperforms basic decoding. In addition, fewer loudspeakers are active to reproduce the original field and have better performance.