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
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
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
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
Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of qua...Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrdinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems, such as momentum, energy and Hamiltonian, so Schrdinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrdinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.展开更多
A fast encoding algorithm based on the mean square error （MSE） distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform （WT） coefficients of images, can...A fast encoding algorithm based on the mean square error （MSE） distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform （WT） coefficients of images, can simplify the realization of the non-linear interpolated vector quantization （NLIVQ） technique and make the partial distance search （PDS） algorithm more efficient. Utilizing the relationship of vector L2-norm and its Euclidean distance, some conditions of eliminating unnecessary codewords are obtained. Further, using inequality constructed by the subvector L2-norm, more unnecessary codewords are eliminated. During the search process for code, mostly unlikely codewords can be rejected by the proposed algorithm combined with the non-linear interpolated vector quantization technique and the partial distance search technique. The experimental results show that the reduction of computation is outstanding in the encoding time and complexity against the full search method.展开更多
Information hiding schemes based on vector quantization （VQ） usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree ...Information hiding schemes based on vector quantization （VQ） usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization （CPTSVQ）. The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.展开更多
随着大语言模型(large language model,LLM)参数规模的指数级增长,模型部署和推理面临着严峻的内存和计算资源挑战。量化技术作为模型压缩的核心方法,通过降低权重和激活值的数值精度,显著减少了模型的存储需求和计算开销。首先回顾了...随着大语言模型(large language model,LLM)参数规模的指数级增长,模型部署和推理面临着严峻的内存和计算资源挑战。量化技术作为模型压缩的核心方法,通过降低权重和激活值的数值精度,显著减少了模型的存储需求和计算开销。首先回顾了量化技术的发展历程,从经典的Int8/4量化方法到前沿的超低比特量化算法,总结了典型方法的技术特征与性能演进规律,指出传统实数域量化在极低比特条件下存在受限于离散化误差的挑战,难以突破性能上限。为此,进而系统性地梳理了复域量化系列工作。该系列工作提出了基于复数域的量化范式,通过在参数表示中引入幅度与相位2个自由度,显著扩展了模型的表达空间;此外,类比信号处理中通过将时域信号进行傅里叶变换与低通滤波实现稳定表示的经典范式,进一步提出了由实数模型经复域变换与复域量化,达成了无乘法稳定推理的技术路线。实验结果表明,该方案在多个基准数据集上优于现有超低比特量化方法,有效突破了实数域模型的性能天花板,展现出复域量化在高效建模与性能保持方面的潜在价值。总体而言,通过对量化技术演进及复域量化系列研究的系统分析,旨在揭示超低比特量化的发展规律与未来趋势,为高效大模型的理论研究与工程实现提供参考。展开更多
故障根因分析旨在找到导致特定问题、故障或事件发生的原因,是多个领域中追踪溯源的重要支撑技术,但现有方法在效率、准确性和稳定性等方面仍不能满足故障根因分析任务的实际需求。对此,将贝叶斯网作为相关属性之间依赖关系表示和推理...故障根因分析旨在找到导致特定问题、故障或事件发生的原因,是多个领域中追踪溯源的重要支撑技术,但现有方法在效率、准确性和稳定性等方面仍不能满足故障根因分析任务的实际需求。对此,将贝叶斯网作为相关属性之间依赖关系表示和推理的知识框架,提出基于贝叶斯网的故障根因分析方法。首先,针对高维数据和稀疏样本带来的挑战,提出基于向量量化自编码器的高维属性约简算法,并给出α-BIC评分准则,高效地学习根因贝叶斯网(Root Cause Bayesian Network,RCBN)。随后,基于贝叶斯网嵌入技术实现RCBN的高效推理,高效计算各原因条件下故障产生的可能性,进而使用因果模型中的Blame机制度量各原因对给定故障的贡献度,从而实现故障根因分析。在3个公共数据集和3个合成数据集上的实验结果表明,所提方法的平均检测准确性和效率明显优于对比方法,在CHILD数据集上精度提升了7%,运行时间快了60%。展开更多
Potential electric and gravitational fields do not change steady quantized states of electrons in chemical bonds, microscopic clusters of charges and macroscopic superconducting rings. There are no theoretical grounds...Potential electric and gravitational fields do not change steady quantized states of electrons in chemical bonds, microscopic clusters of charges and macroscopic superconducting rings. There are no theoretical grounds to create Squid-type instruments to measure electric and gravitational fields with quantum accuracy basing on the Bohr-Sommerfeld quantization of charged particles. Squid-verified spatial flatness for superfluid electrons corresponds to the material space paradigm for reality.展开更多
基金supported by the National Natural Science Foundation of China(No.62201508)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ21F010001 and LQ23F010004)the State Key Laboratory of Millimeter Waves of Southeast University,China(No.K202212).
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 62376215)the Key Research and Development Project of Shaanxi Province(No.2025CY-YBXM-044)+3 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(Nos.2022RC069 and 2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)the Ningbo Top Medical and Health Research Program(No.2023030716).
文摘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.
基金supported in part by the National Natural Science Foundation of China under Grant 62302475in part by the Research Funds of Centre for Leading Medicine and Advanced Technologies of IHM under Grant 2023IHM01081 and 2023IHM01085+1 种基金in part by the Hefei Municipal Natural Science Foundation under Grant 202328partly by the Anhui Science and Technology Innovation Tackling Plan Project under Grant 202423k09020044。
文摘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.
文摘Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrdinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems, such as momentum, energy and Hamiltonian, so Schrdinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrdinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.
基金the National Natural Science Foundation of China (60602057)the NaturalScience Foundation of Chongqing Science and Technology Commission (2006BB2373).
文摘A fast encoding algorithm based on the mean square error （MSE） distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform （WT） coefficients of images, can simplify the realization of the non-linear interpolated vector quantization （NLIVQ） technique and make the partial distance search （PDS） algorithm more efficient. Utilizing the relationship of vector L2-norm and its Euclidean distance, some conditions of eliminating unnecessary codewords are obtained. Further, using inequality constructed by the subvector L2-norm, more unnecessary codewords are eliminated. During the search process for code, mostly unlikely codewords can be rejected by the proposed algorithm combined with the non-linear interpolated vector quantization technique and the partial distance search technique. The experimental results show that the reduction of computation is outstanding in the encoding time and complexity against the full search method.
基金supported by the National Natural Science Foundation of China under Grant No.60133012 and No.661272374
文摘Information hiding schemes based on vector quantization （VQ） usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization （CPTSVQ）. The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.
文摘随着大语言模型(large language model,LLM)参数规模的指数级增长,模型部署和推理面临着严峻的内存和计算资源挑战。量化技术作为模型压缩的核心方法,通过降低权重和激活值的数值精度,显著减少了模型的存储需求和计算开销。首先回顾了量化技术的发展历程,从经典的Int8/4量化方法到前沿的超低比特量化算法,总结了典型方法的技术特征与性能演进规律,指出传统实数域量化在极低比特条件下存在受限于离散化误差的挑战,难以突破性能上限。为此,进而系统性地梳理了复域量化系列工作。该系列工作提出了基于复数域的量化范式,通过在参数表示中引入幅度与相位2个自由度,显著扩展了模型的表达空间;此外,类比信号处理中通过将时域信号进行傅里叶变换与低通滤波实现稳定表示的经典范式,进一步提出了由实数模型经复域变换与复域量化,达成了无乘法稳定推理的技术路线。实验结果表明,该方案在多个基准数据集上优于现有超低比特量化方法,有效突破了实数域模型的性能天花板,展现出复域量化在高效建模与性能保持方面的潜在价值。总体而言,通过对量化技术演进及复域量化系列研究的系统分析,旨在揭示超低比特量化的发展规律与未来趋势,为高效大模型的理论研究与工程实现提供参考。
文摘故障根因分析旨在找到导致特定问题、故障或事件发生的原因,是多个领域中追踪溯源的重要支撑技术,但现有方法在效率、准确性和稳定性等方面仍不能满足故障根因分析任务的实际需求。对此,将贝叶斯网作为相关属性之间依赖关系表示和推理的知识框架,提出基于贝叶斯网的故障根因分析方法。首先,针对高维数据和稀疏样本带来的挑战,提出基于向量量化自编码器的高维属性约简算法,并给出α-BIC评分准则,高效地学习根因贝叶斯网(Root Cause Bayesian Network,RCBN)。随后,基于贝叶斯网嵌入技术实现RCBN的高效推理,高效计算各原因条件下故障产生的可能性,进而使用因果模型中的Blame机制度量各原因对给定故障的贡献度,从而实现故障根因分析。在3个公共数据集和3个合成数据集上的实验结果表明,所提方法的平均检测准确性和效率明显优于对比方法,在CHILD数据集上精度提升了7%,运行时间快了60%。
文摘Potential electric and gravitational fields do not change steady quantized states of electrons in chemical bonds, microscopic clusters of charges and macroscopic superconducting rings. There are no theoretical grounds to create Squid-type instruments to measure electric and gravitational fields with quantum accuracy basing on the Bohr-Sommerfeld quantization of charged particles. Squid-verified spatial flatness for superfluid electrons corresponds to the material space paradigm for reality.
