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Identification of various food residuals on denim based on hyperspectral imaging system and combination optimal strategy

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Artificial Intelligence in Agriculture 2021年第1期5卷 125-132页

作者： Yuzhen Chen Ziyi Xu Wencheng Tang Menghan Hu Douning Tang Guangtao Zhai Qingli Li Shanghai Key Laboratory of Multidimensional Information Processing School of Communication&Electronic EngineeringEast China Normal UniversityShanghai 200062China School of Statistics East China Normal UniversityShanghai 200062China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China McCormick School of Engineering Northwestern UniversityEvanstonILUnited States

As the science and technology develop,crime methods and scenes have become increasingly complex and *** evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace *** can provide not only energy,but clues to solve *** this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark *** obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data ***,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed *** modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification *** experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food *** respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification *** future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification *** research may play a positive role in the construction of a harmonious society.

关键词： Hyperspectral imaging Food residual on denim Combination optimal strategy Variable selection Forensic application

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Coarse carrier and timing synchronization for interleaved OFDMA uplink system

Coarse carrier and timing synchronization for interleaved OF...

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2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2010

作者： Miao, Ruiqin Sun, Jun Gui, Lin Xiong, Jian Institute of Image Communication and Information Processing Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424444625

In orthogonal frequency division multiple access (OFDMA) uplink system, estimations of time delay and carrier frequency offset (CFO) are both particularly challenging. In this paper, a coarse CFO and time delay estimation method is proposed for interleaved OFDMA uplink system. By exploiting the special scheme of interleaved OFDMA, the repetitive time-domain training symbol is designed, and the training sequences for different users have good autocorrelation and cross-correlation properties. Furthermore, the time delay and CFO estimation metrics are presented by making certain correlation between the received training symbol and the local sequence. Through searching peak of the proposed metrics, integral CFO of many subcarriers spaces for each user can be estimated, and then timing delay for each user can be found. Simulations illustrate the proposed estimation scheme can get good performance under multipath channel.

关键词： Frequency division multiple access

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An Efficient Fast Inter Mode Decision Algorithm for H.264/AVC

An Efficient Fast Inter Mode Decision Algorithm for H.264/AV...

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WRI International Conference on communications and Mobile Computing, CMC

作者： Huaibin Huang Dong Hu key Laboratory of image processing and image communications of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China

An efficient fast inter mode decision algorithm for the baseline H.264/AVC video coding is proposed in this paper. The main idea of the algorithm is to reduce the number of candidate modes for rate distortion optimization (RDO) by using early mode detection in four different levels, viz. SKIP mode detection, type detection at macroblock-level, type detection at submacroblock-level and intra modes detection. In addition, the correlation of cost of current MB and the one of previous frame, as well as the cost's monotonous property are both used to help the mode decision. Compared to JM8.6 reference software, our algorithm can save encoding time 71.743% on average with negligible PSNR loss, which is very helpful for H.264/AVC inter frame. encoding.

关键词： Automatic voltage control Costs image coding Encoding Video coding Rate-distortion Video sequences Mobile communication Mobile computing Telecommunication computing

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A source-driven error recovery scheme using Wyner-Ziv coding

A source-driven error recovery scheme using Wyner-Ziv coding

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IEEE International Conference onMultimedia and Expo, ICME 2007

作者： Yongsheng, Zhang Hongkai, Xiong Songyu, Yu Hui, Lv Institute of Image Communication and Information Processing Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)1424410177

In this paper, we propose an error recovery scheme to cope with the frame loss problem in large end-to-end delay scenario. Because Wyner-Ziv coding can produce deterministic output with nondeterministic inputs, we adopt the decoder's error-corrupted reference picture as side information to exploit its correlation with source picture, and thus improve the coding efficiency. To prevent retransmission request, we propose a feasible encoder-driven rate estimation scheme by only storing MVs and part of the error pattern at encoder. Furthermore, a new Laplacian parameter computing method is proposed based on discrete Laplacian PDF. The experimental results show that the proposed estimation schemes have quite high precision, and the error recovery scheme outperforms INTRA refresh scheme up to 5 dB. © 2007 IEEE.

关键词： Errors

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A novel interference cancellation approach for interleaved OFDMA uplink system

A novel interference cancellation approach for interleaved O...

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In this paper, the issue of carrier frequency offset (CFO) compensation in interleaved orthogonal frequency division multiple access (OFDMA) uplink system is investigated. To mitigate the effect of multiple access interference (MAI) caused by CFOs of different users, a new parallel interference cancellation (PIC) compensation algorithm is proposed. This scheme uses minimum mean square error (MMSE) criterion to obtain the estimation of interference users, then circular convolutions [6] are employed to restore MAI and compensate CFO. To tackle the complexity problem of circular convolutions, an efficient MAI restoration and cancellation method is developed. Simulations illustrate the good performance and low computational complexity of the proposed algorithm. © 2009 The Institute of Electronics, Information and communication Engineers.

关键词： Mean square error

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A Video Code Based on Distribution Compressive Sensing

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Procedia Engineering 2012年 29卷 3613-3618页

作者： Wu Ming-hu Zhu Xiu-chang Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China School of Electrical and Electronic Engineering Hubei University of Technology Wuhan China

Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new video coding algorithm based on Distributed Compressive Sampling(DCS) principles, where almost all computation burdens can be shifted to the decoder, resulting in a very lowcomplexity encoder. At the decoder, compressed video can be efficiently reconstructed. Our algorithm can be useful in those video applications that require very low complex encoders. Simulation results show that our scheme compares favorably with existing schemes at a much lower implementation cost.

关键词： Distributed Compressive Sampling sparsity distributed video coding

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Generating Cartoon images from Face Photos with Cycle-Consistent Adversarial Networks

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Computers, Materials & Continua 2021年第11期69卷 2733-2747页

作者： Tao Zhang Zhanjie Zhang Wenjing Jia Xiangjian He Jie Yang School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214000China Key Laboratory of Artificial Intelligence Jiangsu214000China The Global Big Data Technologies Centre University of Technology SydneyUltimoNSW2007Australia The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai201100China

The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style *** transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output ***-GAN is a classic GAN model,which has a wide range of scenarios in style *** its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output ***,it is difficult for CYCLE-GAN to converge and generate high-quality *** order to solve this problem,spectral normalization is introduced into each convolutional kernel of the *** convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed ***,we use pretrained model(VGG16)to control the loss of image content in the position of l1 *** avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss *** terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative *** results show that the proposed model converges faster and achieves better FID scores than the state of the art.

关键词： Generative adversarial network spectral normalization Lipschitz stability constraint VGG16 l1 regularization term l2 regularization term Frechet inception distance

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Mean Shift Object Tracking with Modified LTP

Mean Shift Object Tracking with Modified LTP

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： ZHANG Shaona HU Dong HU Yanting Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications The key lab of image processing and transmission of Jiangsu province Nanjing University of Posts and Telecommunications School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of squares (LMedS) algorithm is used to adaptively calculate the noise threshold for accurate estimation of the LTP texture information. Furthermore, target scale and orientation is estimated in case of partial occlusion or rotation, so as to realize robust object tracking. Experimental results show that the proposed algorithm can acquire robust tracking performance under complex background .

关键词： Object tracking, Mean Shift, LTP, LMedS

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Advancing Cross-Subject Domain Generalization in Brain-Computer Interfaces with Multi-Adversarial Strategies

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Liu, Yici Qin, Lang Chen, Xin Jeannes, Regine Le Bouquin Coatrieux, Jean Louis Shu, Huazhong Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing 210096 China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing 210096 China Univ- Rennes Centre de Recherche en Information Biomedicale Sino-français (CRIBs) Rennes F-3502 France Southeast University INSERM Nanjing 210096 China The First Affiliated Hospital With Nanjing Medical University Department of Radiology China Universit de Rennes 1 Laboratoire Traitement du Signal et de l’Image Rennes 35000 France Centre de Recherche en Information Biomedicale Sino-Francais Rennes 35042 France National Institute for Health and Medical Research Rennes 35000 France

A cross-subject domain generalization (DG) approach with multi-adversarial strategies (DGMA) is introduced to reduce brain-computer interfaces (BCIs) systems’ dependency on high-quality, subject-specific EEG data, making it adaptable to unseen domains. DGMA leverages annotated training data from other subjects and consists of three modules: (1) Prefeature extraction, enhancing EEG signal separability through preprocessing, data augmentation, and tangent space mapping;(2) Distribution feature updater, aligning inter-subject feature distributions with marginal maximum mean discrepancy (MMD);(3) Multi-adversarial training, initially using gradient reversal layer (GRL) to amplify domain differences and classification loss, allowing the model to learn diverse domain-specific features before minimizing these differences to balance domain transferability and discriminability. DGMA is capable of better capturing domain-specific features while achieving stronger generalization compared to traditional methods focused solely on minimizing domain differences. Validated on four motor imagery datasets, DGMA achieved state-of-the-art accuracies of 76.1% on BCI Competition IV 2a and 72.4% on the 002-2014 dataset. Additional tests on a private fatigue dataset and the SEED dataset yielded accuracies of 99.5% and 86.6%, respectively. © 1963-2012 IEEE.

关键词： adversarial neural network domain generalization EEG transfer learning

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Bayesian network based abnormality detection with genetic algorithm optimization

Bayesian network based abnormality detection with genetic al...

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作者： Qiu, Jingbang Zhang, Chongyang Zheng, Shibao Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9789810863227

Abnormality Detection (AD), being the core part of intelligent surveillance systems, is calling for growing research interest due to its importance in providing higher efficiency and labor saving. In this paper, we propose a novel Bayesian Network (BN) based AD method for smart surveillance in scenes containing large scale viewpoint changes without modelrelearning. In the proposed AD scheme, Reasoning Layer is introduced into BN to strengthen logical inferences, and a localized Genetic Algorithm (GA) is developed to optimize BN parameters and structure. With the expert knowledge aided BN structure modeling and GA based optimization, the proposed method can provide more robust detection experience with retained accuracy. Experiments on unlearned surveillance test sequences are shown to exhibit the validity of this method. ©Memetic Computing Society.

关键词： Bayesian networks

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