Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

The systematic management of English online test results can ensure the fairness of the test, guarantee the accuracy and safety of the test results, and reduce the consumption of manpower and materials. Unfortunately, the existing data mining and management strategy for learner scores cannot track the learning process or score change of learners. This paper innovatively applies the trajectory data mining technology to the design of an English online process test results management system. After analyzing the functional requirements of the system, four basic information lists were constructed in SQL Server 2005. Then, an improved k-means clustering algorithm and the trajectory frequent pattern mining algorithm were combined to cluster the test results and analyze the learning trajectory deviation of the learners. Next, the four system functions were detailed, including login, entry of test results, trajectory setting, and deviation analysis. The effectiveness of our algorithm, and the performance of our system were fully verified through experiments.

In traffic image target detection, unusual targets like a running dog has not been paid sufficient attention. The mature detection methods for general targets cannot be directly applied to detect unusual targets, owing to their high complexity, poor feature expression ability, and requirement for numerous manual labels. To effectively detect unusual targets in traffic images, this paper proposes a multi-level semi-supervised one-class extreme learning machine (ML-S2OCELM). Specifically, the extreme learning machine (ELM) was chosen as the basis to develop a classifier, whose variables could be calculated directly at the cost of limited computing resources. The hypergraph Laplacian array was employed to improve the depiction of data smoothness, making semi-supervised classification more accurate. Furthermore, a stack auto-encoder (AE) was introduced to implement a multi-level neural network (NN), which can extract discriminative eigenvectors with suitable dimensions. Experiments show that the proposed method can efficiently screen out traffic images with unusual targets with only a few positive labels. The research results provide a time-efficient, and resource-saving instrument for feature expression and target detection.

Multimedia computer-aided teaching can comprehensively process sound, image, text, graphics, audio, video, animation and other materials to carry out tactical innovation, so that basketball tactical teaching forms an interactive teaching system. Combining the characteristics of basketball teaching, this paper explores computer multimedia technology to assist basketball tactics teaching, aiming at enriching basketball teaching methods and improving basketball tactics teaching effects. This thesis uses the relevant theories and methods of education, computer science, physical education and sports training to explore computer multimedia technology to assist basketball tactics teaching, and enrich teaching methods with pictures, videos, animations, texts and other multimedia materials to improve teaching quality of basketball tactics.

With the continuous development of social economy and the transformation of enterprises, the society needs a large number of innovative applied talents. This requires universities to continuously deepen the comprehensive reform, strengthen the cooperation between schools-enterprises and the industry-education integration. While doing well the basic theory teaching, we should strengthen the reform and construction of practical teaching system, and focus on cultivating practical talents with strong practical ability and practical operation level. In many years of educational practice, Taishan Institute of Science and Technology of SDUST has made full use of its professional advantages and industry characteristics in the fields of mining, manufacturing, construction, and finance. It has explored and constructed the practice system and model of "123", this is "one goal, two platforms, and three systems". It emphasizes the practice system that individualizes and diversified innovative applied talents, and has promoted the training of innovative applied talents.

Error Vector Magnitude (EVM) is the most important parameter of modulation quality of wireless digital signal. At present, there are some shortcomings in the calibration process as follows: 1) Lack of vector signal generator that can accurately set the required EVM value of the standard signal, so when calibrating the vector signal analyzer, only the measurement points near the zero modulation error are calibrated. 2) Closed-loop inspection between instruments, using vector signal analyzer and vector signal generator to calibrate the EVM in a closed-loop way. According to the calculation formula of error vector magnitude on digital modulation signal from the vector signal generator, the setting value of I/Q amplitude imbalance and signal-to-noise ratio at a given EVM value of the ZigBee signal are calculated, in the meantime the vector signal analysis software built in the N9030A is used to measure the EVM of the ZigBee signal. The result shows good consistency. At the end of the article, the traceability of adjustments on I/Q amplitude imbalance and signal-to-noise ratio is introduced.

Furnace safety supervisory system (FSSS) plays an important role in protecting the boiler of thermal power plant from danger. In order to evaluate the performance of FSSS itself, functional safety theoriesare applied in this paperto achievehazard and risk analysis, target safety integrity level (SIL) determinationand functional safety evaluation. The most important safety instrumented function (SIF) of FSSS --master fuel trip (MFT) is considered, and the probability of failure on demand (PFD) is calculated based on the method of fault tree analysis (FTA). According to the analysis result, target SILfor FSSS is 2, but the actual system does not meet the requirement. Through corrective measures of making one-out-of-two (1oo2) redundant configuration for each actuator and compressing the functional testing cycle, the safety index of MFT ultimately reaches the target value.

As an effective recommendation technology to solve "information overload" problem, collaborative filtering has widly attracted attention of scholars from various fields.First, this paper proposes a method for measuring the recommending ability of user based on popularity and long-tailed distribution.Then, a global core user set is constructed for recommadition based on the recommending ability and samples selection idea in data mining, aimming to take advantage of users in the different part of the long tail distribution and reduce computing complexity of the algorithm without lowing the recommended performance.Experimental results show that the algorithm is effective and can be used to solve the real-time problem and cold start recommendation.

In digital modulation quality parameters traceability, the Error Vector Magnitude, Magnitude Error and Phase Error must be traced, and the measurement uncertainty of the above parameters needs to be assessed. Although the calibration specification JJF1128-2004 Calibration Specification for Vector Signal Analyzers is published domestically, the measurement uncertainty evaluation is unreasonable, the parameters selected is incorrect, and not all error terms are selected in measurement uncertainty evaluation. This article lists formula about magnitude error and phase error, than presents the measurement uncertainty evaluation processes for magnitude error and phase errors.

This paper provides empirical evidence of fast root cause identification in assembly house for a case in which SiP chip wire bond showed high ratio of quality and reliability escapes. The identification has been performed through FMEA with a large number of RMAs and through quality control to rescreen goods called-back to minimize the field risk by using reliability tests. Furthermore, as a corrective action, authors have been working with major assembly house to implement a set of critical quality gates to improve the quality control in the wire bond process and management.

According to the Calibration Specification of Digital Radio Communication Testers, the output power of RF signal generator is one of the items must be calibrated, the output power from −50dBm to −10dBm is usually measured by microwave power meter, although the measurement uncertainty evaluation is given in the Calibration Specification of Digital Radio Communication Testers, but is imperfect, based on the principle of zero omission and no repetition adopted in measurement uncertainty evaluation, this paper reassesses the measurement uncertainty to make it more complete. The assessment process also applies to measurement uncertainty evaluation on output level of all kinds of signal generator by using microwave power meter.

According to the parameter great changes of electron components in actual working environment,and high and low temperature test must be carried out on some components in the screening test.Typical capacitor,resistor and integrated operational amplifier are take as research object,variation of electron components is realized through the research on these two devices under different temperature conditions,and through the processing and analysis of experimental data,the analysis method for improving reliability of electronic components is provided,the evidence of necessity that high and low temperature test on electronic components is provided.

In order to solve the key technical problem in the verification process of mechanical and electrical coal mine anemometer, the paper presents digital recognition approach base on the characters of the anemometer image.First of all, collect the mirror images of the anemometer from a plain mirror which in the wind tunnels.And then we process the mirror images with secondary mirroring, grayscaling, denoising and binarization.Followed the location of the digital area can be determined and the incline distortion of the image can be adjusted.The final step is segmenting the single character in the digital character region and identify the corresponding number base on the characteristics of anemometer, which using the LED digital tube to display numbers.The experiments show that this method can accurately identify the numbers on the anemometer and has high recognition rate.The recognition time is able to meet the requirement for the verification of anemometer.Thus the method has high practical value.

为了有效地捕捉步态的连续性动态信息,快速进行身份认证和识别,提出一种以帧差能量图(FDEI)的行质量向量作为步态特征的步态识别方法.该算法通过目标检测、二值化、形态学处理、连通性分析等预处理后得到步态轮廓图像,并利用其序列的宽度进行准周期性分析,再用连续隐马尔可夫模型(CHMM)对所提取的步态帧差能量图行质量向量进行模型参数训练和识别.在CASIA数据库上进行了仿真实验,结果表明该算法具有特征提取简单、特征维数低、识别速度快和识别率高的优点,可以满足实时识别的需要.

The existing structured light measurement technologies mainly focus on the single color objects, especially for measuring white object. Mainly because of in the process of threedimensional measurement for color objects, color object’s surface has a great influence on color components of structured light, leading to the color of structured light changing, this will cause serious errors in the decoding process. To solve this problem, combined with the actual measurements for colored objects, this paper adopts a color Gray code for encoding and decoding structured light, and presents an obtaining technical for color components of structured light, which first through regression analysis builds a mathematical model, and then uses the least squares method for solving it, at last restores the color of the projected stripes to ensure the correctness of decoding, to achieve the measurement for color object and to improve the measurement accuracy. The experimental results show that this method has a good effect on decoding.

The paper considers a feedback optimising control of drinking water distribution systems (DWDS). Although the optimised pump and valves scheduling and disinfectant injection control attracted considerable attention over last two decades most of the contributions were limited to an open-loop optimisation repetitively performed during the DWDS operation. Also, while a strong interaction between the water quantity and quality exists most of the proposals regards either quality or quantity control. The paper derives a two times-scale hierarchical controller with MPC control algorithms at the slow upper and fast lower control levels. The controller performance is validated by a comprehensive simulation study based on an example case study DWDS.

This paper explores the feasibility of a new triggering technology that is based on reversed-LTD (linear transformer driver) principle. The triggering technology which is named as LTD-trigger has a similar structure to LTD device, but it operates reversely relative to LTD. The LTD-trigger is small size and can obtain multiple trigger pulsed voltages flexible with one single primary high-voltage pulsed power supply. It reduces the delay and jitter time due to the intrinsic advantages from coaxial configuration over the conventional triggering method. The paper analyzed the working principle of LTD-trigger by establishing an equivalent circuit model and an experimental prototype, which is able to generate 20 trigger pulses with voltage amplitude of about 22 kV and rise time of about 75 ns. Simulation and experimental results show a good performance of synchronization among the output trigger pulses of different cavities. Taking some fault factors into consideration, the experimental results show the reliability of LTD-trigger.

Image target detection and tracking is an important research field of image processing for its great potential in Military and Civil applications. This paper proposes improved image target detection based on component analysis. At first, it explains the concept about image connected region. Then, it introduces the component analysis algorithm to extract connected components, and uses support vector machine to classify the target region. It is very useful to Forward Looking Infra-Red image processing and analysis. The experiments performed on Forward Looking Infra-Red images, and the results show that the proposed algorithm is effective.