MAPOD Analysis in Eddy Current Testing of Flaws Considering Multiple Response Signals and Multiple Flaw Parameters

The reliability of the eddy current testing (ECT) in flaw detection is quantitatively evaluated by theprobability of detection (POD). Precise and efficient modeling of POD gives direction for the implement of ECTon sites to avoid false or missing flaw detection. Traditional POD analysis focuses on single uncertain factor orsingle response signal with limited credibility in engineering. This paper considers multiple response signals andmultiple flaw parameters to perform POD. The flaw length, the flaw depth, the coil impedance, and the magneticflux density are comprehensively studied under various lift-off distances. A finite element model (FEM) of ECT isestablished and verified with experiments to obtain sufficient simulation data for discrete POD modeling. Thecontinuous POD function is then fitted based on the discrete values to show the superiority of integrating multiplefactors. A comparison with conventional POD analysis further demonstrates the higher reliability of ECT flawdetection considering multiple flaw parameters and multiple response signals, especially for small flaws.

Blind channel estimation for multiple antenna OFDM system subject to unknown carrier frequency offset

The problem of channel estimation for multiple an- tenna orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） is addressed. Multiple signal classification （MUSIC）-Iike algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional （RD）-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cramer-Rao bound （CRB） of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.

Frankincense myrrh attenuates hepatocellular carcinoma by regulating tumor blood vessel development through multiple epidermal growth factor receptor-mediated signaling pathways

BACKGROUND In traditional Chinese medicine(TCM),frankincense and myrrh are the main components of the antitumor drug Xihuang Pill.These compounds show anticancer activity in other biological systems.However,whether frankincense and/or myrrh can inhibit the occurrence of hepatocellular carcinoma(HCC)is unknown,and the potential molecular mechanism(s)has not yet been determined.AIM To predict and determine latent anti-HCC therapeutic targets and molecular mechanisms of frankincense and myrrh in vivo.METHODS In the present study,which was based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(http://tcmspw.com/tcmsp.php),Universal Protein database(http://www.uniprot.org),GeneCards:The Human Gene Database(http://www.genecards.org/)and Comparative Toxicogenomics Database(http://www.ctdbase.org/),the efficacy of and mechanism by which frankincense and myrrh act as anti-HCC compounds were predicted.The core prediction targets were screened by molecular docking.In vivo,SMMC-7721 human liver cancer cells were transplanted as xenografts into nude mice to establish a subcutaneous tumor model,and two doses of frankincense plus myrrh or one dose of an EGFR inhibitor was administered to these mice continuously for 14 d.The tumors were collected and evaluated:the tumor volume and growth rate were gauged to evaluate tumor growth;hematoxylineosin staining was performed to estimate histopathological changes;immunofluorescence(IF)was performed to detect the expression of CD31,α-SMA and collagen IV;transmission electron microscopy(TEM)was conducted to observe the morphological structure of vascular cells;enzyme-linked immunosorbent assay(ELISA)was performed to measure the levels of secreted HIF-1αand TNF-α;reverse transcription-polymerase chain reaction(RT-qPCR)was performed to measure the mRNA expression of HIF-1α,TNF-α,VEGF and MMP-9;and Western blot(WB)was performed to determine the levels of proteins expressed in the EGFR-mediated PI3K/Akt and MAPK signaling pathways.RESULTS The results of the network pharmacology analysis showed that there were 35 active components in the frankincense and myrrh extracts targeting 151 key targets.The molecular docking analysis showed that both boswellic acid and stigmasterol showed strong affinity for the targets,with the greatest affinity for EGFR.Frankincense and myrrh treatment may play a role in the treatment of HCC by regulating hypoxia responses and vascular system-related pathological processes,such as cytokine-receptor binding,and pathways,such as those involving serine/threonine protein kinase complexes and MAPK,HIF-1 and ErbB signaling cascades.The animal experiment results were verified.First,we found that,through frankincense and/or myrrh treatment,the volume of subcutaneously transplanted HCC tumors was significantly reduced,and the pathological morphology was attenuated.Then,IF and TEM showed that frankincense and/or myrrh treatment reduced CD31 and collagen IV expression,increased the coverage of perivascular cells,tightened the connection between cells,and improved the shape of blood vessels.In addition,ELISA,RT-qPCR and WB analyses showed that frankincense and/or myrrh treatment inhibited the levels of hypoxia-inducible factors,inflammatory factors and angiogenesis-related factors,namely,HIF-1α,TNF-α,VEGF and MMP-9.Furthermore,mechanistic experiments illustrated that the effect of frankincense plus myrrh treatment was similar to that of an EGFR inhibitor with regard to controlling EGFR activation,thereby inhibiting the phosphorylation activity of its downstream targets:the PI3K/Akt and MAPK(ERK,p38 and JNK)pathways.CONCLUSION In summary,frankincense and myrrh treatment targets tumor blood vessels to exert anti-HCC effects via EGFR-activated PI3K/Akt and MAPK signaling pathways,highlighting the potential of this dual TCM compound as an anti-HCC candidate.

Modified MUSIC estimation for correlated signals with compressive sampling arrays

This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array （CSA-MMUSIC）, is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.

An effective array beamforming scheme based on branch-and-bound algorithm

In this paper, we propose an effective full array and sparse array adaptive beamforming scheme that can be applied for multiple desired signals based on the branch-and-bound algorithm. Adaptive beamforming for the multiple desired signals is realized by the improved Capon method. At the same time,the sidelobe constraint is added to reduce the sidelobe level. To reduce the pointing errors of multiple desired signals, the array response phase of the desired signal is firstly optimized by using auxilary variables while keeping the response amplitude unchanged. The whole design is formulated as a convex optimization problem solved by the branch-and-bound algorithm. In addition,the beamformer weight vector is penalized with the modified reweighted l_(1)-norm to achieve sparsity. Theoretical analysis and simulation results show that the proposed algorithm has lower sidelobe level, higher SINR, and less pointing error than the stateof-the-art methods in the case of a single expected signal and multiple desired signals.

Babao Dan Inhibits Gastric Cancer Progression in vivo Through Multiple Signaling Pathways

Objective:The aim of this study was to explore the effects of Babao dan(BBD),a traditional Chinese medicine,on gastric cancer(GC)progression in vivo.Materials and Methods:A subcutaneous xenograft mouse model of GC was established using MGC80-3 cells.The terminal deoxynucleotidyl transferase-mediated dUTP:2'-deoxyuridine 5'-triphosphate-biotin nick-end labeling method was adopted to detect cell apoptosis in vivo.The expression levels of proteins associated with proliferation,apoptosis,and angiogenesis were measured by immunohistochemical staining or western blotting(WB).The activation and protein levels of p-c-Jun N-terminal kinase(JNK),p-p38,p-extracellular-regulated kinase 1/2,p-nuclear factor-κB(NF-κB),and p-STAT3 were examined by Bio-plex and WB.Results:BBD significantly inhibited tumor growth in GC mouse models with no adverse effect on body weight or organ function.It was also found that BBD significantly suppressed the proliferation of GC tumor cells,induced the apoptosis of tumor cells,and inhibited angiogenesis through inactivating with mitogen-activated protein kinase,NF-κB,and STAT3 pathways.Conclusions:BBD exerts suppressive effects on GC tumor growth by regulating multiple pathways in vivo,which may provide a novel treatment option for GC therapy.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"massive multiple-input multiple-output(MIMO)").We consider a time-division duplexing(TDD)scheme,in which reciprocity between the uplink and downlink channels can be assumed.Channel estimation is essential for downlink beamforming in massive MIMO,nevertheless,the pilot contamination effect hinders accurate channel estimation,which leads to overall performance degradation.Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals(AOAs)in the large-scale antenna system,we propose a multiple signals classification(MUSIC)based channel estimation algorithm during the uplink transmission.Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

FAST MUSIC SPECTRUM PEAK SEARCH VIA METROPOLIS-HASTINGS SAMPLER

A fast MUltiple SIgnal Classification (MUSIC) spectrum peak search algorithm is devised, which regards the power of the MUSIC spectrum function as target distribution up to a constant of proportionality, and uses Metropolis-Hastings (MH) sampler, one of the most popular Markov Chain Monte Carlo (MCMC) techniques, to sample from it. The proposed method reduces greatly the tremendous computation and storage costs in conventional MUSIC techniques i.e., about two and four orders of magnitude in computation and storage costs under the conditions of the experiment in the paper respectively.

ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.

Efficient hybrid method for time reversal superresolution imaging

An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.

Computationally efficient direction finding using polynomial rooting with reduced-order and real-valued computations

The root multiple signal classification（root-MUSIC） algorithm is one of the most important techniques for direction of arrival（DOA） estimation. Using a uniform linear array（ULA） composed of M sensors, this method usually estimates L signal DOAs by finding roots that lie closest to the unit circle of a（2M-1）-order polynomial, where L 〈 M. A novel efficient root-MUSIC-based method for direction estimation is presented, in which the order of polynomial is efficiently reduced to 2L. Compared with the unitary root-MUSIC（U-root-MUSIC） approach which involves real-valued computations only in the subspace decomposition stage, both tasks of subspace decomposition and polynomial rooting are implemented with real-valued computations in the new technique,which hence shows a significant efficiency advantage over most state-of-the-art techniques. Numerical simulations are conducted to verify the correctness and efficiency of the new estimator.

A Time-Frequency Associated MUSIC Algorithm Research on Human Target Detection by Through-Wall Radar

In this paper,a time-frequency associated multiple signal classification(MUSIC)al-gorithm which is suitable for through-wall detection is proposed.The technology of detecting hu-man targets by through-wall radar can be used to monitor the status and the location information of human targets behind the wall.However,the detection is out of order when classical MUSIC al-gorithm is applied to estimate the direction of arrival.In order to solve the problem,a time-fre-quency associated MUSIC algorithm suitable for through-wall detection and based on S-band stepped frequency continuous wave(SFCW)radar is researched.By associating inverse fast Fouri-er transform(IFFT)algorithm with MUSIC algorithm,the power enhancement of the target sig-nal is completed according to the distance calculation results in the time domain.Then convert the signal to the frequency domain for direction of arrival(DOA)estimation.The simulations of two-dimensional human target detection in free space and the processing of measured data are com-pleted.By comparing the processing results of the two algorithms on the measured data,accuracy of DOA estimation of proposed algorithm is more than 75%,which is 50%higher than classical MUSIC algorithm.It is verified that the distance and angle of human target can be effectively de-tected via proposed algorithm.

ESTIMATION OF 2-D DOA USING NON-CIRCULAR MUSIC METHOD FOR UNIFORM CIRCULAR AND RECTANGULAR ARRAYS

This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the algorithm to estimate 2-D Direction Of Arrival(DOA).A comparison between UCAs and URAs of NC-MUSIC is made in this paper.The simulations show that the NC-MUSIC method doubles the maximum estimation number of standard MUSIC.Using non-circular signals,the performance of URAs is improved remarkably while the improvement of UCAs is not so significantly.Moreover,the influence of arrays structures on the NC-MUSIC method is discussed.

POLYNOMIAL COEFFICIENT FINDING FOR ROOT-MUSIC

Abs Root-MUSIC （MUltiple Signal Classification） is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger exaltation. The particular simulations are given and attest correctness of the theory analysis and also indicate that the proposed algorithm has preferable estimating performance.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

A New Calibration Method for Microphone Array with Gain, Phase, and Position Errors

Microphone array can be used in sound source localization and separation. But gain, phase, and position errors can seriously influence the performance of localization algorithms such as multiple signal classification （MUSIC） algorithm. In this paper, a new calibration method for microphone array with gain, phase, and position errors is proposed. Unlike traditional calibration methods for antenna array, the proposed method can be used in the broadband and near-field signal model such as microphone array with arbitrary sensor geometries in one plane. Computer simulations are presented and simulation results show the new method having good performance.

Application of Extended Multiplicative Signal Correction to Short-Wavelength near Infrared Spectra of Moisture in Marzipan

Short-wavelength near infrared spectroscopy (SW-NIR) is a very rapid, versatile and precise technique, which can be used in many different situations and for very types of products and chemical compounds. Extended multiplicative signal correction (EMSC) is a modification of the standard MSC pre-processing method that allows the separation of physical light scattering effects from chemical (vibrational) light absorbance effects in spectra. In this paper, the EMSC is applied and compared with first derivate, second derivate, MSC and SNV in combination of PLSR to obtain robust models in terms of accuracy and predict ability with a reduced calibration data set using SW-NIR spectra of moisture in marzipan. The Extended Multiplicative Signal Correction—EMSC and combination methods provide the best results in terms of prediction ability and calibration SW-NIR spectra of moisture in marzipan. The best classification results were obtained by Extended Multiplicative Signal Correction followed by second derivates.

Fast BSC-based algorithm for near-field signal localization via uniform circular array

In this paper,we propose a beam space coversion(BSC)-based approach to achieve a single near-field signal local-ization under uniform circular array(UCA).By employing the centro-symmetric geometry of UCA,we apply BSC to extract the two-dimensional(2-D)angles of near-field signal in the Van-dermonde form,which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal para-meters via rotational invariance techniques(ESPRIT)algorithm.By substituting the calculated 2-D angles into the direction vec-tor of near-field signal,the range parameter can be conse-quently obtained by the 1-D multiple signal classification(MU-SIC)method.Simulations demonstrate that the proposed al-gorithm can achieve a single near-field signal localization,which can provide satisfactory performance and reduce computational complexity.

Polarization quaternion DOA estimation based on vector MISC array

This paper examines the direction of arrival(DOA)estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint(MISC).The vector array effectively utilizes the polarization domain information of incident signals,and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction.The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom(DOFs).The quaternion data model based on vector MISC arrays is established,which extends the scalar MISC array into the vector MISC array.Based on the model,a quaternion multiple signal classification(MUSIC)algorithm based on vector MISC arrays is proposed for DOA estimation.The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance.Analytical simulations are performed to certify the capability of the algorithm.

A low-complexity multiple signal representation scheme in downlink OFDM-CDMA

OFDM-CDMA is an attractive technique for broadband wireless communication. However, the high peakto-average power ratio （PAPR） of the downlink signals, generated from multiple spread codes, remains a serious problem. In this paper, a low-complexity multiple signal representation （MSR） scheme is proposed to control the PAPR problem in downlink OFDM-CDMA systems. The proposed scheme generates multiple candidate signals by a novel user grouping scheme, which is without distortion and can provide more PAPR reduction than the conventional MSR schemes, such as partial transmit sequence （PTS） and selective mapping （SLM）. Furthermore, a low-complexity processing structure is developed using a novel joint spreading and inverse fast Fourier transform （S-IFFT） to simplify the generation of multiple candidate signals. Complexity analysis and numerical results show that the OFDM-CDMA systems employing the proposed scheme have better tradeoff between PAPR reduction and computational complexity, compared with the conventional MSR schemes.