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MobiIris: Attention-Enhanced Lightweight Iris Recognition with Knowledge Distillation and Quantization 认领 引用
1
作者 Trong-Thua Huynh De-Thu Huynh +2 位作者 Du-Thang Phu Hong-Son Nguyen Quoc HNguyen 《Computers, Materials & Continua》 SCIE EI 2026年第6期623-636,共14页
This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.Th... This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning,enabling fine-grained modeling of iris textures in a compact representation.To further improve discriminability,we design a training pipeline that combines dynamic-margin triplet loss,a staged hard/semi-hard negative mining strategy,and feature-level knowledge distillation from a ResNet-50 teacher.Finally,we investigate the use of post-training float16 quantization to reduce memory footprint and latency for deployment on mobile hardware.Experiments on the challenging CASIA-IrisV4-Thousand dataset show that the full-precision MobiIris model requires only 12 MB of storage and 27 ms inference latency,while achieving an EER of 1.409%,VR@FAR=1%of 98.184%,and CMC@1 of 94.785%,closely matching a ResNet-50 baseline that is more than 7×larger and slower.Under post-training quantization,the model shrinks to 5.94 MB with 13 ms latency and maintains a competitive balance between accuracy and efficiency compared to other optimized variants.These results demonstrate that a coherent combination of lightweight architecture design,attention mechanisms,metric-learning objectives,hard negative mining,and knowledge distillation yields a practical iris recognition solution suitable for secure,real-time authentication on mobile and embedded platforms. 展开更多
关键词 Iris recognition lightweight architecture model optimization attention mechanism knowledge distillation model quantization
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Geodesic Nature and Quantization of Shift Vector 认领 引用
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作者 Hua Wang Kai Chang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期143-148,共6页
We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosoni... We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosonic phonon drag shift vectors with non-vertical transitions.We demonstrate that the gauge invariant shift vector can be quantized as integer values,analogous to the Euler characteristic based on the Gauss-Bonnet theorem for a manifold with a smooth boundary.We reveal intricate relationships among geometric quantities such as the shift vector,Berry curvature,and quantum metric.Our findings demonstrate that the loop integral of the shift vector in the quantized interband formula contributes to the non-quantized component of the trace of conductivity in the circular photogalvanic effect.The Wilson loop method facilitates first-principles calculations,providing insights in the geometric underpinnings of these interband gauge invariant quantities and shedding light on their nonlinear optical manifestations in real materials. 展开更多
关键词 geometric shift vector geodesic nature gauge invariant shift vector wilson loop approachour bosonic phonon drag shift vectors bloch momentumusing euler characteristic quantization
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AI Model Compression Methods: A Distribution-Aware Residual Entropy Quantization 认领 引用
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作者 Nikita Sakovich Dmitry Aksenov +1 位作者 Ekaterina Pleshakova Sergey Gataullin 《Computers, Materials & Continua》 SCIE EI 2026年第8期854-864,共11页
We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Un... We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Unlike traditional approaches that solely optimize the mean squared error of weight approximation,DARE-Q additionally considers the entropy of the quantization residual,allowing for control over the statistical properties of the resulting error.The method is based on channel-wise symmetric uniform quantization with scaling based on a combined loss function that includes L2 distortion and entropy regularization.The DARE-Q method is implemented as a compact DAREQuantLinear module which can be easily integrated into standard transformer pipelines without changing the inference logic or using specific kernels.The experimental analysis was conducted on the language models facebook/opt-125m and facebook/opt-350m,which contain approximately 125 and 350 million parameters.The quality of the models was assessed using the standard perplexity metric(PPL)computed on the wikitext-2-raw-v1 dataset.DARE-Q is completely data-free and does not require model retraining or calibration data,which makes it the only viable option in privacy-sensitive or confidential environments where access to the original training data is restricted—precisely the setting where methods such as GPTQ and AWQ cannot be applied.The observed increase in PPL relative to data-dependent baselines reflects this fundamental trade-off rather than a shortcoming of the approach.By leveraging per-channel scale selection and a combined loss function,DARE-Q provides a flexible trade-off between approximation accuracy and quantization error structure,creating an attractive algorithmic basis for further improvement of model compression methods. 展开更多
关键词 Artificial intelligence large language models mathematical optimization methods model compression quantization methods information theory high-performance computing
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TP-ViT:truncated uniform-log2 quantizer and progressive bit-decline reconstruction for vision Transformer quantization 认领 引用
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作者 Xichuan ZHOU Sihuan ZHAO +4 位作者 Rui DING Jiayu SHI Jing NIE Lihui CHEN Haijun LIU 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第1期47-58,共12页
Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challe... Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware. 展开更多
关键词 Vision Transformers Post-training quantization Block reconstruction Image classification Object detection Instance segmentation
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Networked control with guaranteed performance for IoT rehabilitation robot under nonvanishing uncertainties and input quantization 认领 引用
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作者 Shilei Tan Xuesong Wang +1 位作者 Haoquan Zhou Wei Gong 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1774-1782,共9页
The Internet of Things(IoT)technology provides data acquisition,transmission,and analysis to control rehabilitation robots,encompassing sensor data from the robots as well as lidar signals for trajectory planning(desi... The Internet of Things(IoT)technology provides data acquisition,transmission,and analysis to control rehabilitation robots,encompassing sensor data from the robots as well as lidar signals for trajectory planning(desired trajectory).In IoT rehabilitation robot systems,managing nonvanishing uncertainties and input quantization is crucial for precise and reliable control performance.These challenges can cause instability and reduced effectiveness,particularly in adaptive networked control.This paper investigates networked control with guaranteed performance for IoT rehabilitation robots under nonvanishing uncertainties and input quantization.First,input quantization is managed via a quantization-aware control design,ensur stability and minimizing tracking errors,even with discrete control inputs,to avoid chattering.Second,the method handles nonvanishing uncertainties by adjusting control parameters via real-time neural network adaptation,maintaining consistent performance despite persistent disturbances.Third,the control scheme guarantees the desired tracking performance within a specified time,with all signals in the closed-loop system remaining uniformly bounded,offering a robust,reliable solution for IoT rehabilitation robot control.The simulation verifies the benefits and efficacy of the proposed control strategy. 展开更多
关键词 Networked control IoT rehabilitation robot Guaranteed performance Nonvanishing uncertainties Input quantization
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Automatic diagnosis of keratitis from low-quality slit-lamp images using feature vector quantization and self-attention mechanisms 认领 引用
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作者 JIANG Jiewei XIN Yu +3 位作者 DING Ke ZHU Mingmin CHEN Yi LI Zhongwen 《Optoelectronics Letters》 EI 2025年第10期612-618,共7页
This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 bac... This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application. 展开更多
关键词 keratitis low quality images adaptive average pooling densenet backbone networkfollowed self attention mechanism feature vector quantization diagnosis keratitis automatic diagnosis
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BER performance analysis of non-Hermitian symmetry OFDM-VLC systems with ADC quantization noise 认领 引用
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作者 WANG Zhongpeng AI Caihua ZHANG Lijuan 《Optoelectronics Letters》 EI 2025年第11期677-683,共7页
Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-div... Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-division multiplexing-based visible light communication(OFDM-VLC)system is presented.In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC,a quantized mathematical model of NHS OFDM-VLC is established.Based on the proposed quantized model,a closed-form bit error rate(BER)expression is derived.The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC.In addition,channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC. 展开更多
关键词 quantized modela communication systemsin Bit Error Rate quantized mathematical model reliability performance degradation non hermitian symmetry ADC quantization OFDM VLC
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Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization 认领 引用 被引量：1
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作者 Md Hasibur Rahman Mohammed Arif Uddin +1 位作者 Zinnat Fowzia Ria Rashedur M.Rahman 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期1637-1666,共30页
The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classificati... The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments. 展开更多
关键词 Bengali NLP black-box distillation emotion classification model compression post-training quantization unstructured pruning
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High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection 认领 引用
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作者 Krishan Kumar Mohamed Abouhawwash +2 位作者 Amit Kant Pandit Shubham Mahajan Mofreh A.Hogo 《Computers, Materials & Continua》 SCIE EI 2022年第10期1977-1993,共17页
The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth... The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats. 展开更多
关键词 Adaptive quantization high-efficient video coding(HEVC) quad-tree rate-distortion optimization(RDO) video compression variable quantization method(VQM) quantization parameter(QP)
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Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision 认领 引用
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作者 Yuejiao Wang Zhong Ma +2 位作者 Chaojie Yang Yu Yang Lu Wei 《Computers, Materials & Continua》 SCIE EI 2024年第4期819-836,共18页
The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to d... The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment. 展开更多
关键词 Mixed precision quantization quantization strategy optimal assignment reinforcement learning neural network model deployment
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Quantization rules for low dimensional quantum dots 认领 引用 被引量：5
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作者 许田 曹庄琪 方靖淮 《Chinese Physics B》 SCIE EI CAS 2008年第12期4378-4381,共4页
This paper applies the analytical transfer matrix method （ATMM） to calculate energy eigenvalues of a particle in low dimensional sharp confining potential for the first time, and deduces the quantization rules of th... This paper applies the analytical transfer matrix method （ATMM） to calculate energy eigenvalues of a particle in low dimensional sharp confining potential for the first time, and deduces the quantization rules of this system. It presents three cases in which the applied method works very well. In the first quantum dot, the energy eigenvalues and eigenfunction are obtained, and compared with those acquired from the exact numerical analysis and the WKB （Wentzel, Kramers and Brillouin） method; in the second or the third case, we get the energy eigenvalues by the ATMM, and compare them with the EBK （Einstein, Brillouin and Keller） results or the wavefunction outcomes. From the comparisons, we find that the semiclassical method （WKB, EBK or wavefunction） is inexact in such systems. 展开更多
关键词 analytical transfer matrix method quantization rules energy eigenvalues confining potential
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Rate distortion optimization for adaptive gradient quantization in federated learning 认领 引用 被引量：2
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作者 Guojun Chen Kaixuan Xie +4 位作者 Wenqiang Luo Yinfei Xu Lun Xin Tiecheng Song Jing Hu 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1813-1825,共13页
Federated Learning(FL)is an emerging machine learning framework designed to preserve privacy.However,the continuous updating of model parameters over uplink channels with limited throughput leads to a huge communicati... Federated Learning(FL)is an emerging machine learning framework designed to preserve privacy.However,the continuous updating of model parameters over uplink channels with limited throughput leads to a huge communication overload,which is a major challenge for FL.To address this issue,we propose an adaptive gradient quantization approach that enhances communication efficiency.Aiming to minimize the total communication costs,we consider both the correlation of gradients between local clients and the correlation of gradients between communication rounds,namely,in the time and space dimensions.The compression strategy is based on rate distortion theory,which allows us to find an optimal quantization strategy for the gradients.To further reduce the computational complexity,we introduce the Kalman filter into the proposed approach.Finally,numerical results demonstrate the effectiveness and robustness of the proposed rate-distortion optimization adaptive gradient quantization approach in significantly reducing the communication costs when compared to other quantization methods. 展开更多
关键词 Federated learning Communication efficiency Adaptive quantization Rate distortion
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A hybrid quantum encoding algorithm of vector quantization for image compression 认领 引用 被引量：4
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作者 庞朝阳 周正威 郭光灿 《Chinese Physics B》 CAS 2006年第12期3039-3043,共5页
Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability... Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm. 展开更多
关键词 vector quantization Grover＇s algorithm image compression quantum algorithm
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Finite-time Control of Discrete-time Systems With Variable Quantization Density in Networked Channels 认领 引用 被引量：2
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作者 Yiming Cheng Xu Zhang +1 位作者 Tianhe Liu Changhong Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第5期1394-1402,共9页
This paper is concerned with the problem of finitetime control for a class of discrete-time networked systems.The measurement output and control input signals are quantized before being transmitted in communication ne... This paper is concerned with the problem of finitetime control for a class of discrete-time networked systems.The measurement output and control input signals are quantized before being transmitted in communication network.The quantization density of the network is assumed to be variable depending on the throughputs of network for the sake of congestion avoidance.The variation of the quantization density modes satisfies persistent dwell-time(PDT)switching which is more general than dwell-time switching in networked channels.By using a quantization-error-dependent Lyapunov function approach,sufficient conditions are given to ensure that the quantized systems are finite-time stable and finite-time bounded with a prescribed H∞performance,upon which a set of controllers depending on the mode of quantization density are designed.In order to show the effectiveness of the designed H_∞controller,we apply the developed theoretical results to a numerical example. 展开更多
关键词 Finite-time H∞controller design quantization-error-dependent Lyapunov function quantized signal
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Effects of quantization on detrended fluctuation analysis 认领 引用 被引量：2
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作者 朱松盛 徐泽西 +1 位作者 殷奎喜 徐寅林 《Chinese Physics B》 SCIE EI CAS CSCD 2011年第5期153-158,共6页
Detrended fluctuation analysis （DFA） is a method foro estimating the long-range power-law correlation exponent in noisy signals. It has been used successfully in many different fields, especially in the research of ... Detrended fluctuation analysis （DFA） is a method foro estimating the long-range power-law correlation exponent in noisy signals. It has been used successfully in many different fields, especially in the research of physiological signals. As an inherent part of these studies, quantization of continuous signals is inevitable. In addition, coarse-graining, to transfer original signals into symbol series in symbolic dynamic analysis, can also be considered as a quantization-like operation. Therefore, it is worth considering whether the quantization of signal has any effect on the result of DFA and if so, how large the effect will be. In this paper we study how the quantized degrees for three types of noise series （anti-correlated, uncorrelated and long-range power-law correlated signals） affect the results of DFA and find that their effects are completely different. The conclusion has an essential value in choosing the resolution of data acquisition instrument and in the processing of coarse-graining of signals. 展开更多
关键词 detrended fluctuation analysis quantization
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s-parameterized Weyl transformation and the corresponding quantization scheme 认领 引用 被引量：3
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作者 王继锁 孟祥国 范洪义 《Chinese Physics B》 SCIE EI CAS CSCD 2015年第1期265-268,共4页
By extending the usual Weyl transformation to the s-parameterized Weyl transformation with s being a real parameter,we obtain the s-parameterized quantization scheme which includes P–Q quantization, Q–P quantization... By extending the usual Weyl transformation to the s-parameterized Weyl transformation with s being a real parameter,we obtain the s-parameterized quantization scheme which includes P–Q quantization, Q–P quantization, and Weyl ordering as its three special cases. Some operator identities can be derived directly by virtue of the s-parameterized quantization scheme. 展开更多
关键词 generalized Wigner transformation s-parameterized quantization scheme
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Number-Phase Quantization Scheme for L-C Circuit 认领 引用 被引量：2
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作者 FAN Hong-Yi LIANG Bao-Long WANG Ji-Suo 《Communications in Theoretical Physics》 SCIE CAS 2007年第12期1038-1040,共3页
For a mesoscopic L-C circuit,besides the Louisell's quantization scheme in which electric charge q andelectric current I are respectively quantized as the coordinate operator Q and momentum operator P,in this pape... For a mesoscopic L-C circuit,besides the Louisell's quantization scheme in which electric charge q andelectric current I are respectively quantized as the coordinate operator Q and momentum operator P,in this paperwe propose a new quantization scheme in the context of number-phase quantization through the standard Lagrangianformalism.The comparison between this number-phase quantization with the Josephson junction's Cooper pair number-phase-difference quantization scheme is made. 展开更多
关键词 mesoscopic L-C circuit phase-difference quantization scheme magnetic flux Josephson junction
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Quantization-Based Robust Image Watermarking Using the Dual Tree Complex Wavelet Transform 认领 引用 被引量：4
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作者 LIU Jinhua SHE Kun 《China Communications》 SCIE 2010年第4期1-6,共6页
Conventional quantization index modulation(QIM)watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas... Conventional quantization index modulation(QIM)watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets(perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise(AWGN),and some kinds of geometric attacks such as scaling,rotation,etc. 展开更多
关键词 Image Watermarking Quantization IndexModulation Dual Tree Complex Wavelet Transform JPEG Compression
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ADC border effect and suppression of quantization error in the digital dynamic measurement 认领 引用 被引量：3
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作者 白丽娜 刘海东 +7 位作者 周渭 张勇 翟鸿启 崔震健 赵明英 谷小倩 刘蓓玲 黄李贝 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第9期91-97,共7页
The digital measurement and processing is an important direction in the measurement and control field. The quantization error widely existing in the digital processing is always the decisive factor that restricts the ... The digital measurement and processing is an important direction in the measurement and control field. The quantization error widely existing in the digital processing is always the decisive factor that restricts the development and applications of the digital technology. In this paper, we find that the stability of the digital quantization system is obviously better than the quantization resolution. The application of a border effect in the digital quantization can greatly improve the accuracy of digital processing. Its effective precision has nothing to do with the number of quantization bits, which is only related to the stability of the quantization system. The high precision measurement results obtained in the low level quantization system with high sampling rate have an important application value for the progress in the digital measurement and processing field. 展开更多
关键词 quantization error border effect digital converter（ADC）
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QUANTIZATION OF LIE ALGEBRAS OF BLOCK TYPE 认领 引用 被引量：3
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作者 程永胜 苏育才 《Acta Mathematica Scientia》 SCIE 2010年第4期1134-1142,共9页
In this article, we use the general method of quantization by Drinfeld’s twist to quantize explicitly the Lie bialgebra structures on Lie algebras of Block type.
关键词 Quantization Lie bialgebras Drinfeld twist Lie algebras of Block type
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