An improved bicubic imaging fitting algorithm for 3D radar detection target

3D ground-penetrating radar has been widely used in urban road underground disease detection due to its nondestructive,efficient,and intuitive results.However,the 3D imaging of the underground target body presents the edge plate phenomenon due to the space between the 3D radar array antennas.Consequently,direct 3D imaging using detection results cannot reflect underground spatial distribution characteristics.Due to the wide-beam polarization of the ground-penetrating radar antenna,the emission of electromagnetic waves with a specific width decreases the strong middle energy on both sides gradually.Therefore,a bicubic high-precision 3D target body slice-imaging fitting algorithm with changing trend characteristics is constructed by combining the subsurface target characteristics with the changing spatial morphology trends.Using the wide-angle polarization antenna’s characteristics in the algorithm to build the trend factor between the measurement lines,the target body change trend and the edge detail portrayal achieve a 3D ground-penetrating radar-detection target high-precision fitting.Compared with other traditional fitting techniques,the fitting error is small.This paper conducts experiments and analyses on GpaMax 3D forward modeling and 3D ground-penetrating measured radar data.The experiments show that the improved bicubic fitting algorithm can eff ectively improve the accuracy of underground target slice imaging and the 3D ground-penetrating radar’s anomaly interpretation.

Beam space weighted subspace fitting algorithm

The sensor space high resolution Weighted Subspace Fitting (WSF) algorithm is expanded into beam space in this paper. Beam space WSF algorithm uses beam outputs of array which can be regarded as the outputs of an virtual array having the same number of elements as the beam number to estimate target directions. In most underwater acoustic systems, the number of beams used for determining the direction of arrival is usually considerably less than that of the sensors, so the computation burdensome is decedent. Computer simulation results show that the beam space WSF algorithm retains the super performance of the sensor space WSF algorithm when applied to the beam outputs of some practical acoustic-receiving array.

BESⅢ track fitting algorithm

A track fitting algorithm based on the Kalman filter method has been developed for BESⅢ of BEPCⅡ. The effects of multiple scattering and energy loss when the charged particles go through the detector, non-uniformity of magnetic field （NUMF） and wire sag, etc., have been carefully handled. This algorithm works well and the performance satisfies the physical requirements tested by the simulation data.

A subspace fitting algorithm of acoustic vector sensor array and corresponding matrix pre-filter design

In order to ease the pass-band response distortion of the matrix pre-filter,a simple approach for designing matrix spatial filter is proposed,which minimizes the sum of the k maximal distortion norm（k is the number of the constraint points）within the pass-band,while constraining the filter response within the stop-band.Considering the costly amount of calculation of the high-resolution methods,an algorithm with small amount of calculation based on matrix pre-filtering and subspace fitting using acoustic vector array（MF-VSSF）is proposed.Through joint processing of signal subspace of both pressure and particle velocity,the pre-filtering matrix and the signal subspace is decreased to M-dimensional（M is the number of array-element）,hence reduces the time-consumption of the matrix pre-filter design and DOA searching.Simulation results show that,the method offers the same performance as MUSIC with pre-filtering,but has much lesser amount of calculation.Moreover,the designed prefilter can efficiently suppress the interference in the stop-band and improve the estimation and resolution performance of successive DOA estimators.

A reordered first fit algorithm based novel storage scheme for parallel turbo decoder

In this paper we discuss a novel storage scheme for simultaneous memory access in parallel turbo decoder. The new scheme employs vertex coloring in graph theory. Compared to a similar method that also uses unnatural order in storage, our scheme requires 25 more memory blocks but allows a simpler configuration for variable sizes of code lengths that can be implemented on-chip. Experiment shows that for a moderate to high decoding throughput （40-100 Mbps）, the hardware cost is still affordable for 3GPP＇s （3rd generation partnership project） interleaver.

EFFICIENT MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR JOB SHOP SCHEDULING

A new representation method is first presented based on priority roles. According to this method, each entry in the chromosome indicates that in the procedure of the Giffler and Thompson （GT） algorithm, the conflict occurring in the corresponding machine is resolved by the corresponding priority role. Then crowding-measure multi-objective evolutionary algorithm （CMOEA） is designed, in which both archive maintenance and fitness assignment use crowding measure. Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling.

Pyrolysis Characteristics and Kinetics of Municipal Solid Waste

Based on a systemic survey, the pyrolysis characteristics and apparent kinetics of the municipal solid waste （ MSW） under different conditions were researched using a special pyrolysis reactor, which could overcome the disadvantage of thermo-gravimetric analyzer. The thermal decomposition behaviour of MSW was investigated using thermo-gravimetric （ TG ） analysis at rates of 4.8,6.6,8.4, 12.0 and 13. 2 K/min. The pyrolysis characteristics of MSW were also studied in different function districts. The pyrolysis of MSW is a complex reaction process and three main stages are found according to the results. The first stage represents the degradation of cellulose and hemicellulose, with the maximum degradation rate occuring at 150℃ -200 ℃： the second stage represents dehydrochlorination and depolymerization of intermediate products and the differential thermogravimetric （ DTG ） curves have shoulder peaks at about 300℃： the third stage is the decomposition of the residual big molecular organic substance and lignin at 400 ℃- 600 ℃. Within the range of given experimental conditions, the results of non-linear fitting algorithm and experiment are in agreement with each other and the correlation coefficients are over0. 99. The kinetic characteristics are concerned with the material component and heating rate. The activation energy of reaction decreases with the increase of heating rate.

Second-order calibration applied to quantification of two active components of Schisandra chinensis in complex matrix

The effectiveness of traditional Chinese medicine (TCM) against various diseases urges more low cost, speed and sensitive analytical methods for investigating the phamacology of TCM and providing a theoretical basis for clinical use. The potential of second-order calibration method was validated for the quantification of two effective ingredients of Schisandra chinensis in human plasma using spectrofluorimetry. The results obtained in the present study demonstrate the advantages of this strategy for multi-target determination in complex matrices. Although the spectra of the analytes are similar and a large number of interferences also exist, second-order calibration method could predict the accurate concentrations together with reasonable resolution of spectral profiles for analytes of interest owing to its ‘second-order advantage’. Moreover, the method presented in this work allows one to simply experimental procedure as well as reduces the use of harmful chemical solvents.

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.

Precautionary analysis of sprouting potato eyes using hyperspectral imaging technology

Sprouted potatoes are not allowed for healthy diet.A good knowledge of the sprouting stage of potatoes can help manage the storage conditions and guide market distribution,thus enabling the quality assurance of potatoes on table.This article presented an intelligent method for precautionary analysis of potato eyes based on hyperspectral imaging technique.Potential potato eyes were classified into two categories according to the time gap to the sprouting date,i.e.by-sprouting and pre-sprouting potato eyes,representing eyes about to sprout and eyes that will take a while to sprout.Features used for classification were extracted by two methods,including successive projections algorithm(SPA)and a newly-developed sine fit algorithm(SFA).Then classifiers of fisher discriminant analysis(FDA)and least square support vector machine(LSSVM)were utilized for classification of potential sprouting potato eyes.Results showed that FDA was more effective than LSSVM in classifying pre-sprouting and by-sprouting potato eyes,and SFA performed well in FDA classifier with the recognition accuracy of 95.3%for prediction set.It is concluded that hyperspectral imaging has the potential for predicting the sprouting stages of potato eyes.

Artificial neural network with modified rider optimization for design and control of PV-integrated quasi Z-source cascaded multilevel inverter system

The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter(qZSCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this paper.The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models.However,such processes are limited to known system models,which are uncertain in future systems.Here,the controller for 3-phase qZS-CMI is modeled based on two phases,and the source PV voltage and output grid current are controlled.In Phase I,optimized Proportional Integral(PI)controller is used for finding out the total PV voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the Artificial Neural Network(ANN)for controlling the grid current.For two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is used.Moreover,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid current.The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm.The main objective of phase I is to lessen the error among the reference PV voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid current.Hence,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.