Polynomial-rooting based fourth-order MUSIC for direction-of-arrival estimation of noncircular signals

A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival（DOA） estimation of second-order fully noncircular source signals, using a uniform linear array（ULA）. This algorithm inherits all merits of its spectralsearching counterpart except for the applicability to arbitrary array geometry, while reducing considerably the computation cost.Simulation results show that the proposed algorithm outperforms the previously developed closed-form second-order noncircular ESPRIT method, in terms of processing capacity and DOA estimation accuracy, especially in the presence of spatially colored noise.

Focused Widely Linear Beamforming

The problem addressed in this paper concerns the extension of widely linear beamforming to the wideband case,developing a wide-focused linear beamformer for the extraction of a wideband second-order(SO)noncircular signal-of-interest(SOI)contaminated by uncorrelated interferences and noise.In the proposed beamformer,the beamforming array observation is first focused to adopt a standard linear minimum variance distortionless response(MVDR)framework.The augmented SOI steering vector then is obtained by estimating the SOI noncircularity parameter with the newly proposed oblique projection with an augmented sparse representation scheme.The covariance matrix of the virtual interference,true interference and noise is further reconstructed using the newly presented complementary spatial spectrum technique.The wideband widely linear spatial filtering is finally realized via MVDR like beamforming.The performance of the proposed beamformer is verified by simulation.

Abnormal Crowd Behavior Detection Based on the Entropy of Optical Flow

To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.

Automated Segmentation of the Brainstem,Cranial Nerves and Vessels for Trigeminal Neuralgia and Hemifacial Spasm

Accurate localization of cranial nerves and responsible blood vessels is important for diagnosing trigeminal neuralgia(TN)and hemifacial spasm(HFS).Manual delineation of the nerves and vessels on medical images is time-consuming and labor-intensive.Due to the development of convolutional neural networks(CNNs),the performance of medical image segmentation has been improved.In this work,we investigate the plans for automated segmentation of cranial nerves and responsible vessels for TN and HFS,which has not been comprehensively studied before.Different inputs are given to the CNN to find the best training configuration of segmenting trigeminal nerves,facial nerves,responsible vessels and brainstem,including the image modality and the number of segmentation targets.According to multiple experiments with seven training plans,we suggest training with the combination of three-dimensional fast imaging employing steady-state acquisition(3D-FIESTA)and three-dimensional time-of-flight magnetic resonance angiography(3DTOF-MRA),and separate segmentation of cranial nerves and vessels.

Particle Filter Object Tracking Algorithm Based on Sparse Representation and Nonlinear Resampling

Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.

Tissue Microstructure Estimation of SANDI Based on Deep Network

Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size and density to biophysical model for the first time.In addition to neurite density,it can achieve their joint estimation non-invasively using dMRI.In the traditional method,parameters of the SANDI are estimated in a maximum likelihood frame-work,where the nonlinear model fitting is computationally intensive.Also,the present methods require a large number of diffusion gradients.Efficient and accurate algorithms for tissue microstructure estimation of SANDI is still a challenge currently.Consequently,we introduce deep learning method for tissue microstructure estimation of the SANDI model.The model comprises two functional components.The first component produces the sparse representation of diffusion sig-nals of input patches.The second component computes tissue microstructure from the sparse repre-sentation given by the first component.The deep network can produce not only tissue microstruc-ture estimates but also the uncertainty of the estimates with a reduced number of diffusion gradi-ents.Then,multiple deep networks are trained and their results are fused for the final prediction of tissue microstructure and uncertainty quantification.The deep network was evaluated on the MGH Connectome Diffusion Microstructure Dataset.Results indicate that our approach outperforms the traditional methods in terms of estimation accuracy.

Vegetable Tunnel House Technology in Tropical Island Countries

A complete set of techniques for the design,installation,maintenance and use of vegetable tunnel houses in tropical island countries was developed,which achieved the goals of rain prevention,wind protection,corrosion resistance,insect and bird prevention,water-saving irrigation,and economic efficiency,significantly improving the production capacity and technical level of vegetables in Samoa.The techniques have become a key vegetable production technology promoted nationwide in Samoa,with universal promotion value for tropical island countries.

Two-Dimensional Direction Finding via Sequential Sparse Representations

The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.

Polysaccharides-based nanohybrids: Promising candidates for biomedical materials

Natural polysaccharides are the polymers composed of monosaccharides through glycosidic bonds.Diverse polysaccharides could be readily obtained from algae(such as alginate),plants(such as pectin),microbes(such as dextran)and animals(such as chitosan(CS)).Carrying abundant functional groups including free carboxyl and hydroxyl groups,natural polysaccharides possess outstanding merits including biocompatibility,low toxicity,stability,low cost,and availability for versatile chemical modification[1,2].

Polymeric biomaterials for biophotonic applications

With the growing importance of optical techniques in medical diagnosis and treatment,there exists a pressing need to develop and optimize materials platform for biophotonic applications.Particularly,the design of biocompatible and biodegradable materials with desired optical,mechanical,chemical,and biological properties is required to enable clinically relevant biophotonic devices for translating in vitro optical techniques into in situ and in vivo use.This technological trend propels the development of natural and synthetic polymeric biomaterials to replace traditional brittle,nondegradable silica glass based optical materials.In this review,we present an overview of the advances in polymeric optical material development,optical device design and fabrication techniques,and the accompanying applications to imaging,sensing and phototherapy.

Surface-enhanced Raman scattering on dielectric microspheres with whispering gallery mode resonance

Conventionally, metallic nanostructures are used for surface-enhanced Raman spectroscopy(SERS), but recently there has been increasing interest in the enhancement of Raman scattering from dielectric substrates due to their improved stability and biocompatibility compared with metallic substrates. Here, we report the observation of enhanced Raman scattering from rhodamine 6 G molecules coated on silica microspheres. We excite the whispering gallery modes(WGMs) supported in the microspheres with a tapered fiber coupler for efficient WGM excitation, and the Raman enhancement can be attributed to the WGM mechanism. Strong resonance enhancement in pump laser intensity and modified Raman emission from the Purcell effect in the microsphere resonator are observed from the experiment and compared with theoretical results. A total Raman enhancement factor of 1.4 × 10~4 is observed, with contribution mostly from the enhancement in pump laser intensity. Our results show that, with an efficient pumping scheme, dielectric microspheres are a viable alternative to metallic SERS substrates.

A whispering-gallery scanning microprobe for Raman spectroscopy and imaging

Optical whispering-gallery-mode microsensors are a promising platform for many applications,such as biomedical monitoring,magnetic sensing,and vibration detection.However,like many other micro/nanosensors,they cannot simultaneously have two critical properties–ultrahigh sensitivity and large detection area,which are desired for most sensing applications.Here,we report a novel scanning whispering-gallery-mode microprobe optimized for both features and demonstrate enhanced Raman spectroscopy,providing high-specificity information on molecular fingerprints that are important for numerous sensing applications.Combining the superiorities of whispering-gallery modes and nanoplasmonics,the microprobe exhibits a two-orders-of-magnitude sensitivity improvement over traditional plasmonics-only enhancement;this leads to molecular detection demonstrated with stronger target signals but less optical power required than surface-enhanced-Raman-spectroscopy substrates.Furthermore,the scanning microprobe greatly expands the effective detection area and realizes two-dimensional micron-resolution Raman imaging of molecular distribution.The versatile and ultrasensitive scanning microprobe configuration will thus benefit material characterization,chemical imaging,and quantum-enhanced sensing.

Sensitivity of N400 Effect During Speech Comprehension Under the Uni-and Bi-Modality Conditions

N400 is an objective electrophysiological index in semantic processing for brain.This study focuses on the sensitivity of N400 effect during speech comprehension under the uni-and bi-modality conditions.Varying the Signal-to-Noise Ratio(SNR) of speech signal under the conditions of Audio-only(A),Visual-only(V,i.e.,lip-reading),and Audio-Visual(AV),the semantic priming paradigm is used to evoke N400 effect and measure the speech recognition rate.For the conditions A and high SNR AV,the N400 amplitudes in the central region are larger;for the conditions of V and low SNR AV,the N400 amplitudes in the left-frontal region are larger.The N400 amplitudes of frontal and central regions under the conditions of A,AV,and V are consistent with speech recognition rate of behavioral results.These results indicate that audio-cognition is better than visual-cognition at high SNR,and visual-cognition is better than audio-cognition at low SNR.

Experimental study of aerodynamic interference effects on aerostatic coefficients of twin deck bridges

The aerodynamic interference effects on aero-static coefficients of twin deck bridges with large span were investigated in detail by means of wind tunnel test.The distances between the twin decks and wind attack angles were changed during the wind tunnel test to study the effects on aerodynamic interferences of aerostatic coefficients of twin decks.The research results have shown that the drag coefficients of the leeward deck are much smaller than that of a single leeward deck.The drag coefficients of a windward deck decrease slightly com-pared with that of a single deck.The lift and torque coefficients of windward and leeward decks are also affected slightly by the aerodynamic interference of twin decks.And the aerodynamic interference effects on lift and torque coefficients of twin decks can be neglected.

Citric Acid-Based Intrinsic Band-Shifting Photoluminescent Materials

Citric acid,an important metabolite with abundant reactive groups,has been demonstrated as a promising starting material to synthesize diverse photoluminescent materials including small molecules,polymers,and carbon dots.The unique citrate chemistry enables the development of a series of citric acid-based molecules and nanomaterials with intriguing intrinsic band-shifting behavior,where the emission wavelength shifts as the excitation wavelength increases,ideal for chromatic imaging and many other applications.In this review,we discuss the concept of“intrinsic band-shifting photoluminescent materials”,introduce the recent advances in citric acid-based intrinsic band-shifting materials,and discuss their potential applications such as chromatic imaging and multimodal sensing.It is our hope that the insightful and forward-thinking discussion in this review will spur the innovation and applications of the unique band-shifting photoluminescent materials.