Journal articles: 'High-Resolution quantization'

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Korten, Pim, Jesper Jensen, and Richard Heusdens. "High-Resolution Spherical Quantization of Sinusoidal Parameters." IEEE Transactions on Audio, Speech and Language Processing 15, no. 3 (March 2007): 966–81. http://dx.doi.org/10.1109/tasl.2006.885929.

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Viswanathan, H., and R. Zamir. "On the whiteness of high-resolution quantization errors." IEEE Transactions on Information Theory 47, no. 5 (July 2001): 2029–38. http://dx.doi.org/10.1109/18.930935.

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Kreitmeier, Wolfgang, and Tamás Linder. "High-Resolution Scalar Quantization With Rényi Entropy Constraint." IEEE Transactions on Information Theory 57, no. 10 (October 2011): 6837–59. http://dx.doi.org/10.1109/tit.2011.2165809.

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Goldberg, Naftaly. "Colour image quantization for high resolution graphics display." Image and Vision Computing 9, no. 5 (October 1991): 303–12. http://dx.doi.org/10.1016/0262-8856(91)90035-n.

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Lookabaugh, T. D., and R. M. Gray. "High-resolution quantization theory and the vector quantizer advantage." IEEE Transactions on Information Theory 35, no. 5 (1989): 1020–33. http://dx.doi.org/10.1109/18.42217.

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Aurzada, Frank, Steffen Dereich, Michael Scheutzow, and Christian Vormoor. "High resolution quantization and entropy coding of jump processes." Journal of Complexity 25, no. 2 (April 2009): 163–87. http://dx.doi.org/10.1016/j.jco.2008.10.002.

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AL-Qaysi, Hayder Khaleel, Tahreer Mahmood, and Khalid Awaad Humood. "Evaluation of different quantization resolution levels on the BER performance of massive MIMO systems under different operating scenarios." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1493. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1493-1500.

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The massive MIMO system is one of the main technologies in the fifth generation (5G) of telecommunication systems, also recognized as a highly large-scale system. Constantly in massive MIMO systems, the base station (BS) is provided with a large number of antennas, and this large number of antennas need high-quantization resolution levels analog-to-digital converters (ADCs). In this situation, there will be more power consumption and hardware costs. This paper presents the simulation performance of a suggested method to investigate and analyze the effects of different quantization resolution levels of ADCs on the bit error rate (BER) performance of massive MIMO system under different operating scenarios using MATLAB software. The results show that the SNR exceeds 12 dB accounts for only 0.001% of BER signals when the number of antennas 60 with low quantization a 2 bits’ levels ADCs, approximately. But when the antenna number rises to 300, the SNR exceeds 12 dB accounts for almost 0.01% of BER transmitted signals. Comparably with the BER performance of high quantization, 4 bits-quantization resolution levels ADCs with the same different antennas have a slight degradation. Therefore, the number of antennas is a very important influence factor.

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Jiang, Jun, Lianping Guo, Kuojun Yang, and Huiqing Pan. "Information Entropy- and Average-Based High-Resolution Digital Storage Oscilloscope." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/947052.

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Vertical resolution is an essential indicator of digital storage oscilloscope (DSO) and the key to improving resolution is to increase digitalizing bits and lower noise. Averaging is a typical method to improve signal to noise ratio (SNR) and the effective number of bits (ENOB). The existing averaging algorithm is apt to be restricted by the repetitiveness of signal and be influenced by gross error in quantization, and therefore its effect on restricting noise and improving resolution is limited. An information entropy-based data fusion and average-based decimation filtering algorithm, proceeding from improving average algorithm and in combination with relevant theories of information entropy, are proposed in this paper to improve the resolution of oscilloscope. For single acquiring signal, resolution is improved through eliminating gross error in quantization by utilizing the maximum entropy of sample data with further noise filtering via average-based decimation after data fusion of efficient sample data under the premise of oversampling. No subjective assumptions and constraints are added to the signal under test in the whole process without any impact on the analog bandwidth of oscilloscope under actual sampling rate.

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Koch, Tobias, and Gonzalo Vazquez-Vilar. "A Rigorous Approach to High-Resolution Entropy-Constrained Vector Quantization." IEEE Transactions on Information Theory 64, no. 4 (April 2018): 2609–25. http://dx.doi.org/10.1109/tit.2018.2803064.

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Bach, Volker, and Ruedi Seiler. "Analysis of Optimal High Resolution and Fixed Rate Scalar Quantization." IEEE Transactions on Information Theory 55, no. 4 (April 2009): 1683–91. http://dx.doi.org/10.1109/tit.2009.2013020.

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Misra, Vinith, Vivek K. Goyal, and Lav R. Varshney. "Distributed Scalar Quantization for Computing: High-Resolution Analysis and Extensions." IEEE Transactions on Information Theory 57, no. 8 (August 2011): 5298–325. http://dx.doi.org/10.1109/tit.2011.2158882.

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Dereich, S., and C. Vormoor. "The High Resolution Vector Quantization Problem with Orlicz Norm Distortion." Journal of Theoretical Probability 24, no. 2 (November 12, 2010): 517–44. http://dx.doi.org/10.1007/s10959-010-0327-2.

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Dereich, Steffen, and Michael Scheutzow. "High Resolution Quantization and Entropy Coding for Fractional Brownian Motion." Electronic Journal of Probability 11 (2006): 700–722. http://dx.doi.org/10.1214/ejp.v11-344.

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Tozer, Daniel J., and Paul S. Tofts. "Removing spikes caused by quantization noise from high-resolution histograms." Magnetic Resonance in Medicine 50, no. 3 (August 20, 2003): 649–53. http://dx.doi.org/10.1002/mrm.10561.

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Zhang, Wence, Jing Xia, and Xu Bao. "Massive MIMO Systems with Low-Resolution ADCs: Achievable Rates and Allocation of Quantization Bits." Wireless Communications and Mobile Computing 2023 (February 17, 2023): 1–12. http://dx.doi.org/10.1155/2023/4012841.

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In massive multiple-input multiple-output (MIMO) systems, the large number of high-resolution analog-to-digital converters (ADCs) lead to high hardware cost and power consumption. In this work, the uplink achievable rates of massive MIMO systems with low-resolution ADCs are studied with consideration of both “Uniform-ADC” that uses ADCs with the same number of quantization bits and “Mixed-ADC” that allows the use of ADCs with different resolutions. By leveraging an additive quantization noise model (AQNM), the asymptotic achievable rates are obtained for maximum ratio combining (MRC), zero-forcing (ZF), and linear minimum mean squared error (LMMSE) receivers in very simple forms. Taking advantages of the theoretical results, we propose two criteria for allocation of quantization bits. It is found that the optimal quantization bits allocation for LMMSE is Mixed-ADCs with number of quantization bits that are polarized, while Uniform-ADC is optimal for MRC and ZF. When there is a constraint on the total ADC power consumption, the proposed quantization-bit allocation scheme for LMMSE becomes Uniform-ADC when the transmit signal-to-noise ratio (SNR) is below a threshold, which is related to the system scale and the ADC power consumption. The theoretical results are verified by Monte-Carlo simulations.

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Lv, Zhongze, Ying Huang, Hu Guan, Jie Liu, Shuwu Zhang, and Yang Zheng. "Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation." Electronics 10, no. 14 (July 12, 2021): 1655. http://dx.doi.org/10.3390/electronics10141655.

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Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

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Luschgy, Harald, and Gilles Pagès. "High-resolution product quantization for Gaussian processes under sup-norm distortion." Bernoulli 13, no. 3 (August 2007): 653–71. http://dx.doi.org/10.3150/07-bej6025.

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Kreitmeier, W. "Error bounds for high-resolution quantization with Rényi-α-entropy constraints." Acta Mathematica Hungarica 127, no. 1-2 (January 11, 2010): 34–51. http://dx.doi.org/10.1007/s10474-010-9079-9.

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Jun, Liu, Luo Zhongqiang, and Xiong Xingzhong. "Low-Complexity Synchronization Scheme with Low-Resolution ADCs." Information 9, no. 12 (December 7, 2018): 313. http://dx.doi.org/10.3390/info9120313.

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An important function of next-generation (5G) and beyond mobile communication systems is aim to provide thousand-fold capacity growth and to support high-speed data transmission up to several megabits per second. However, the research community and industries have to face a dilemma of power consumption and hardware design to satisfy the increasing communication requirements. For the purpose of improving the system cost, power consumption, and implementation complexity, a novel scheme of symbol timing and frequency offset estimation with low-resolution analog-to-digital converters (ADCs) based on an orthogonal frequency division multiplexing ultra-wideband (OFDM-UWB) system is proposed in this paper. In our work, we first verified the principle that the autocorrelation of the pseudo-noise (PN) sequences was not affected by low-resolution quantization. With the help of this property, the timing synchronization could be strongly implemented against the influence of low-resolution quantization. Then, the transmitted signal structure and low-resolution quantization scheme under the synchronization scheme were designed. Finally, a frequency offset estimation model with one-bit timing synchronization was established. Theoretical analysis and simulation results corroborate that the performance of the proposed scheme not only approximates to that of the full-resolution synchronization scheme, but also has lower power consumption and computational complexity.

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Raza, Md Salim, Nahid Hasan, Sayed Tonmoy Ahamed, Kazi Ghulam Mostafa, and Mohammad Rabiul Alam. "Optimization of the Ratio of Sampling Rate to Quantization Level for the Reconstruction of Audio Signal." International Journal of Sensors, Wireless Communications and Control 9, no. 1 (July 15, 2019): 111–23. http://dx.doi.org/10.2174/2210327908666180817103609.

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Background and Objective: In this research work, the basic digital communication system with frequency shift keying modulation technique has been implemented in MATLAB. Methods: The same sinusoidal signal in audio frequency range has been transmitted and reconstructed with the different number of quantization level and sampling rate, where sampling frequency has been taken according to the Nyquist theorem. It has been seen that reconstructed signal at low data rate has appeared with better resolution compared with the reconstructed signal at the high data rate. Results and Conclusion: In this regard, it is evident that the resolution of a reconstructed signal not only depends on data rate but it also depends on the ratio of sampling rate to quantization level. So it is required to optimize the ratio of sampling rate to quantization level before transmitting audio signal.

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Huang, Bormin, Alok Ahuja, and Hung-Lung Huang. "Optimal Compression of High Spectral Resolution Satellite Data via Adaptive Vector Quantization with Linear Prediction." Journal of Atmospheric and Oceanic Technology 25, no. 6 (June 1, 2008): 1041–47. http://dx.doi.org/10.1175/2007jtecha917.1.

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Abstract Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.

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Gao, Jian, Peng Ye, Hao Zeng, Zhixiang Pan, Yu Zhao, Hao Li, and Jie Meng. "Theory of Quantization-Interleaving ADC and Its Application in High-Resolution Oscilloscope." IEEE Access 7 (2019): 156722–32. http://dx.doi.org/10.1109/access.2019.2949701.

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Puidokas, Vytenis, and Albinas J. Marcinkevičius. "High Resolution High Power Low Frequency Digital-to-Analog Converter." Solid State Phenomena 164 (June 2010): 133–38. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.133.

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The architectural scheme of the designed Sigma-Delta DAC on the FPGA is considered. The place of the interpolator in Sigma-Delta DACs is briefly discussed. The summarized structure of the most common interpolators is presented. More applicable structures of interpolators were suggested and analyzed, providing the comparison with [1]. Having changed the structure of the incomplete interpolator and having optimized the stages, it was possible to improve the characteristic of amplitude frequency response with a smaller number of non-zero coefficients and much lower FPGA resources. The paper provides simulated results of the interpolator filter transmission characteristics as well as Sigma-Delta modulator quantization noise parameters. It is demonstrated that simulation of the complete converter system (interpolator + modulator + output filter) allows to identify places of the interpolator, where hardware resources could be saved, thereby reducing the chip area occupied by the converter, which is not always obvious when analyzing nodes separately. Therefore another version of the interpolator was proposed for the system ensuring larger suppression of the additional frequency band in the whole system compared with the previous interpolator. Simulated results related to occupied chip resources are also confirmed by the experiment, which was implemented in Xilinx Spartan FPGA.

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JIANG, MINGHU, and GEORGES GIELEN. "THE EFFECTS OF QUANTIZATION ON MULTI-LAYER FEEDFORWARD NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 04 (June 2003): 637–61. http://dx.doi.org/10.1142/s0218001403002514.

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In this paper we investigate the combined effect of quantization and clipping on multi-layer feedforward neural networks (MLFNN). Statistical models are used to analyze the effects of quantization in a digital implementation. We analyze the performance degradation caused as a function of the number of fixed-point and floating-point quantization bits in the MLFNN. To analyze a true nonlinear neuron, we adopt the uniform and normal probability distributions, compare the training performances with and without weight clipping, and derive in detail the effect of the quantization error on forward and backward propagation. No matter what distribution the initial weights comply with, the weights distribution will approximate a normal distribution for the training of floating-point or high-precision fixed-point quantization. Only when the number of quantization bits is very low, the weights distribution may cluster to ± 1 for the training with fixed-point quantization. We establish and analyze the relationships for a true nonlinear neuron between inputs and outputs bit resolution, the number of network layers and the performance degradation, based on statistical models of on-chip and off-chip training. Our experimental simulation results verify the presented theoretical analysis.

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Wang, Ning, Jian-Wen Bao, Jin-Luen Lee, Fanthune Moeng, and Cliff Matsumoto. "Wavelet Compression Technique for High-Resolution Global Model Data on an Icosahedral Grid." Journal of Atmospheric and Oceanic Technology 32, no. 9 (September 2015): 1650–67. http://dx.doi.org/10.1175/jtech-d-14-00217.1.

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AbstractModern Earth modeling systems often use high-resolution unstructured grids to discretize their horizontal domains. One of the major challenges in working with these high-resolution models is to efficiently transmit and store large volumes of model data for operational forecasts and for modeling research.A newly developed compression technique is presented that significantly reduces the size of datasets produced by high-resolution global models that are discretized on an icosahedral grid. The compression technique is based on the wavelet transform together with a grid rearrangement algorithm and precision-controlled quantization technology. The grid rearrangement algorithm converts an icosahedral grid to a set of 10 rhombus grids that retain the spatial correlation of model data so that a three-dimensional wavelet transform can be effectively applied. The precision-controlled quantization scheme guarantees specified precision of compressed datasets.The technique is applied to the output of a global weather prediction model, the Flow-Following, Finite-Volume Icosahedral Model (FIM) developed by NOAA’s Earth System Research Laboratory. Experiments show that model data at 30-km resolution can be compressed up to 50:1 without noticeable visual differences; at specified precision requirements, the proposed compression technique achieves better compression compared to a state-of-the-art compression format [Gridded Binary (GRIB) with JPEG 2000 packing option]. In addition, model forecasts initialized with original and compressed initial conditions are compared and assessed. The assessment indicates that it is promising to use the technique to compress model data for those applications demanding high fidelity of compressed datasets.

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Mielens, Jason D., Matthew R. Hoffman, Michelle R. Ciucci, Timothy M. McCulloch, and Jack J. Jiang. "Application of Classification Models to Pharyngeal High-Resolution Manometry." Journal of Speech, Language, and Hearing Research 55, no. 3 (June 2012): 892–902. http://dx.doi.org/10.1044/1092-4388(2011/11-0088).

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Purpose The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were evaluated for their ability to identify disordered swallowing. Data were collected from 12 control subjects and 13 subjects with swallowing disorders; for this experiment, these subjects swallowed 5-ml water boluses. Following extraction of relevant parameters, a subset of the data was used to train the models, and the remaining swallows were then independently classified by the networks. Results All methods produced high average classification accuracies, with MLP, SVM, and LVQ achieving accuracies of 96.44%, 91.03%, and 85.39%, respectively. When evaluating the individual contributions of each parameter and groups of parameters to the classification accuracy, parameters pertaining to the upper esophageal sphincter were most valuable. Conclusion Classification models show high accuracy in segregating HRM data sets and represent 1 method of facilitating application of HRM to the clinical setting by eliminating the time required for some aspects of data extraction and interpretation.

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Wielgosz, Maciej, and Andrzej Skoczeń. "Using Neural Networks with data Quantization for time Series Analysis in LHC Superconducting Magnets." International Journal of Applied Mathematics and Computer Science 29, no. 3 (September 1, 2019): 503–15. http://dx.doi.org/10.2478/amcs-2019-0037.

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Abstract The aim of this paper is to present a model based on the recurrent neural network (RNN) architecture, the long short-term memory (LSTM) in particular, for modeling the work parameters of Large Hadron Collider (LHC) super-conducting magnets. High-resolution data available in the post mortem database were used to train a set of models and compare their performance for various hyper-parameters such as input data quantization and the number of cells. A novel approach to signal level quantization allowed reducing the size of the model, simplifying the tuning of the magnet monitoring system and making the process scalable. The paper shows that an RNN such as the LSTM or a gated recurrent unit (GRU) can be used for modeling high-resolution signals with the accuracy of over 0.95 and a small number of parameters, ranging from 800 to 1200. This makes the solution suitable for hardware implementation, which is essential in the case of monitoring the performance critical and high-speed signal of LHC superconducting magnets.

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Wang, Siye, Yeqin Huang, and Yong Yang. "Ergodic Rate and Outage Performance of Full-Duplex NOMA Relaying with Channel Estimation Errors and Low-Resolution ADCs." Applied Sciences 13, no. 12 (June 8, 2023): 6950. http://dx.doi.org/10.3390/app13126950.

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In this paper, we analyze the performance of a full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) relaying system with an amplify-and-forward (AF) protocol in the presence of loopback interference in FD transceivers. Particularly, by considering channel estimation errors and quantization noise in low-resolution analog-to-digital converters (ADCs), the accurate approximation expression for the ergodic rate and closed-form solution for the outage probability are derived, respectively. The validity of the theoretical results is verified by Monte Carlo simulations, which show that both channel estimation errors and quantization noise have deleterious effects on ergodic rate and outage performance for moderate and high signal-to-noise ratios (SNR). In the second phase of the C-NOMA system, both the outage performance and ergodic sum rate decrease at high SNRs due to the effects of loop interference. When the ADC dynamic range reaches a certain level, the system performance is more affected by loopback interference and channel estimation errors compared to the quantization noise of the ADCs.

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Tai, Ying, Yicong Liang, Xiaoming Liu, Lei Duan, Jilin Li, Chengjie Wang, Feiyue Huang, and Yu Chen. "Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8893–900. http://dx.doi.org/10.1609/aaai.v33i01.33018893.

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In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-ofthe-art models.

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Liu, Jiafeng, Haoyang Shi, Siyuan Zhang, Yin Yang, Chongyang Ma, and Weiwei Xu. "Automatic quantization for physics-based simulation." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–16. http://dx.doi.org/10.1145/3528223.3530154.

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Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simply specifying either an error bound or a memory compression rate. Based on the error propagation theory, our method takes advantage of auto-diff to estimate the contributions of each quantization operation to the total error. We formulate the task as a constrained optimization problem, which can be efficiently solved with analytical formulas derived for the linearized objective function. Our workflow extends the Taichi compiler and introduces dithering to improve the precision of quantized simulations. We demonstrate the generality and efficiency of our method via several challenging examples of physics-based simulation, which achieves up to 2.5× memory compression without noticeable degradation of visual quality in the results. Our code and data are available at https://github.com/Hanke98/AutoQantizer.

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Zheng, Jun, Ethan R. Duni, and Bhaskar D. Rao. "Analysis of Multiple-Antenna Systems With Finite-Rate Feedback Using High-Resolution Quantization Theory." IEEE Transactions on Signal Processing 55, no. 4 (April 2007): 1461–76. http://dx.doi.org/10.1109/tsp.2006.889407.

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Martinez, Dominique, and Marc M. Van Hulle. "Generalized boundary adaptation rule for minimizing rth power law distortion in high resolution quantization." Neural Networks 8, no. 6 (January 1995): 891–900. http://dx.doi.org/10.1016/0893-6080(95)00032-u.

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Nikolic, Jelena, and Zoran Peric. "Two approaches to design of forward adaptive piecewise uniform scalar quantizers." Facta universitatis - series: Electronics and Energetics 26, no. 1 (2013): 61–68. http://dx.doi.org/10.2298/fuee1301061n.

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In this paper, two forward adaptive piecewise uniform scalar quantizers are proposed for high-quality quantization of speech signals modeled by the Laplacian probability density function. In designing both forward adaptive piecewise uniform scalar quantizers an equidistant support region partition is assumed and a distribution of the number of reproduction levels per segments is optimized. The proposed models differ in the approach of determining the reproduction levels. In particular, one model defines the reproduction levels as the cell centroids and the other one as the cell midpoints. We show that, in the high-resolution case, the proposed quantizers provide approximately the same performance being close to the one of the forward adaptive nonlinear scalar compandor with equal number of quantization levels.

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Bellan, Diego. "On the Validity of the Noise Model of Quantization for the Frequency-Domain Amplitude Estimation of Low-Level Sine Waves." Metrology and Measurement Systems 22, no. 1 (March 1, 2015): 89–100. http://dx.doi.org/10.1515/mms-2015-0004.

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Abstract This paper deals with the amplitude estimation in the frequency domain of low-level sine waves, i.e. sine waves spanning a small number of quantization steps of an analog-to-digital converter. This is a quite common condition for high-speed low-resolution converters. A digitized sine wave is transformed into the frequency domain through the discrete Fourier transform. The error in the amplitude estimate is treated as a random variable since the offset and the phase of the sine wave are usually unknown. Therefore, the estimate is characterized by its standard deviation. The proposed model evaluates properly such a standard deviation by treating the quantization with a Fourier series approach. On the other hand, it is shown that the conventional noise model of quantization would lead to a large underestimation of the error standard deviation. The effects of measurement parameters, such as the number of samples and a kind of the time window, are also investigated. Finally, a threshold for the additive noise is provided as the boundary for validity of the two quantization models

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Raj, Raghu G., Maxine R. Fox, and Ram M. Narayanan. "Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks." Sensors 21, no. 15 (July 22, 2021): 4981. http://dx.doi.org/10.3390/s21154981.

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The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.

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SINGH, VIPULA, NAVIN RAJPAL, and K. SRIKANTA MURTHY. "A NEURO-WAVELET MODEL USING FUZZY VECTOR QUANTIZATION FOR EFFICIENT IMAGE COMPRESSION." International Journal of Image and Graphics 09, no. 02 (April 2009): 299–320. http://dx.doi.org/10.1142/s0219467809003447.

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Images have large data quantity. For storage and transmission of images, high efficiency image compression methods are under wide attention. In this paper, we propose a neuro- wavelet based model for image compression, which combines the advantages of wavelet transform and neural network and uses fuzzy vector quantization on hidden layer coefficients. Images are decomposed using wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. The coefficients in the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network. The coefficients of the hidden layer of the neural network are further fuzzy vector quantized, which increases the compression ratio. The visual quality of the image has been increased by introducing fuzziness to vector quantization algorithm. Satisfactory reconstructed images with large compression ratios have been achieved using this scheme.

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NAGASHIMA, Tomotaka, Takema SATOH, Petre CATALIN, Kazuyoshi ITOH, and Tsuyoshi KONISHI. "Parallel Use of Dispersion Devices for Resolution Improvement of Optical Quantization at High Sampling Rate." IEICE Transactions on Electronics E97.C, no. 7 (2014): 787–94. http://dx.doi.org/10.1587/transele.e97.c.787.

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Zhong, Yi, Jin Shao, Changyou Men, and Nan Sun. "A Closed-Loop VCO-based 1-1 SMASH CT ΔΣ ADC Architecture." Journal of Physics: Conference Series 2613, no. 1 (October 1, 2023): 012017. http://dx.doi.org/10.1088/1742-6596/2613/1/012017.

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Abstract A novel closed-loop voltage-controlled oscillator (VCO)-based 1-1 sturdy multistage noise-shaping (SMASH) continuous-time ΔΣ ADC is presented in this paper. The proposed architecture uses VCOs as integrators and puts them in a closed loop to suppress the VCO nonlinearity. The multi-bit quantization is employed in the proposed architecture so that the requirement of a high over-sampling ratio (OSR) is relaxed while maintaining a high resolution. The architecture has been verified in MATLAB Simulink.

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Zhao, Xue Jun, and Yuan Fei Jing. "The Application of Vector Quantization Algorithm in Hyperspectral Image Compression." Advanced Materials Research 756-759 (September 2013): 1479–83. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1479.

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With the development of Aerospace remote sensing technology, the hyperspectral remote sensing technology is widely applied in earth resources detection, environment investigation and military reconnaissance, etc. It is different from multispectral image, hyperspectral image spectrum period is as many as dozens to hundreds of band, with high resolution, large amount of data, corresponding to the data transmission problem, so the hyperspectral image compression is become necessary. In this paper the vector quantization algorithm is applied for hyperspectral image compression, first convert the three dimensional hyperspectral images into two dimensional pixel vectors, and then applied the vector quantization on the transformed pixel vectors. The experimental results show that the algorithm not only has better compression ratio, and can effectively save image spectrum section characteristics, which is a kind of efficient three-dimensional compression method.

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Cheng, Ji-Xin, Xiao-Gang Wang, Hai Lin, and Qing-Shi Zhu. "The high resolution spectrum of AsH3 (400) local mode state: symmetry reduction and rotational re-quantization." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 54, no. 12 (October 1998): 1947–60. http://dx.doi.org/10.1016/s1386-1425(98)00157-7.

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Song, Xiaoying, Bing Liu, Qijun Huang, and Ruihan Hu. "Design of high-resolution quantization scheme with exp-Golomb code applied to compression of special images." Journal of Visual Communication and Image Representation 65 (December 2019): 102684. http://dx.doi.org/10.1016/j.jvcir.2019.102684.

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Ahmed, Ahmed Mohammed, Wurod Qasim Mohamed, and Israa Hazem Ali. "Wireless transceiver bit error rate and capacity improvement using advanced decoding techniques." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1633–41. http://dx.doi.org/10.11591/eei.v11i3.3381.

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This paper considers advanced techniques for multiple-input multiple-output (MIMO) detection and decoding techniques to improve bit error rate (BER) and channel capacity. These are requirements for sixth generation (6G) (the next generation) access networks. A parallel decoding and detection scheme and a soft bit decoding scheme are implemented meregely to boost the overall performance of MIMO communication systems. The proposed new system is called the advanced system which comprises the two mentioned advanced techniques of decoding. For simplicity, these advanced techniques are employed and developed using two antennas at both ends, transmitter, and receiver. Then it is compared with the other different techniques which are spatial multiplexing SM–sequential decoding zero forcing-interference cancelation (ZF-IC) technique and SM–parallel decoding technique. We show that the advanced system outperforms the other two mentioned systems by achieving ultra-reliability and a high capacity simultaneously without employing space time coding and error control coding techniques. Additionally, better BER performance is achieved with less resolution and the quantization error reduced with an increasing the resolution. The new advanced system is simulated and evaluated with three terms, channel capacity, BER, and quantization error.

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Ahiadormey, Roger Kwao, and Kwonhue Choi. "Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs." Applied Sciences 11, no. 20 (October 11, 2021): 9409. http://dx.doi.org/10.3390/app11209409.

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In this paper, we propose rate-splitting (RS) multiple access to mitigate the effects of quantization noise (QN) inherent in low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We consider the downlink (DL) of a multiuser massive multiple-input multiple-output (MIMO) system where the base station (BS) is equipped with low-resolution ADCs/DACs. The BS employs the RS scheme for data transmission. Under imperfect channel state information (CSI), we characterize the spectral efficiency (SE) and energy efficiency (EE) by deriving the asymptotic signal-to-interference-and-noise ratio (SINR). For 1-bit resolution, the QN is very high, and the RS scheme shows no rate gain over the non-RS scheme. As the ADC/DAC resolution increases (i.e., 2–3 bits), the RS scheme achieves higher SE in the high signal-to-noise ratio (SNR) regime compared to that of the non-RS scheme. For a 3-bit resolution, the number of antennas can be reduced by 27% in the RS scheme to achieve the same SE as the non-RS scheme. Low-resolution DACs degrades the system performance more than low-resolution ADCs. Hence, it is preferable to equip the system with low-resolution ADCs than low-resolution DACs. The system achieves the best SE/EE tradeoff for 4-bit resolution ADCs/DACs.

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Guo, Xuan, Danyu Wu, Lei Zhou, Huasen Liu, Jin Wu, and Xinyu Liu. "High speed high resolution direct digital frequency synthesizer with non-linear DAC coarse quantization and ROM-based piecewise linear interpolation." Analog Integrated Circuits and Signal Processing 90, no. 1 (November 22, 2016): 263–72. http://dx.doi.org/10.1007/s10470-016-0882-2.

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J, Padmini, and V. Nanammal. "FPGA Implementation of Digital Modulation Schemes Using Verilog HDL." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 560–67. http://dx.doi.org/10.22214/ijraset.2022.46596.

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Abstract: This paper describes the design and development of an FPGA-based digital Modulation Scheme for high-resolution Communication Application. We are focusing on implementation of Verilog based code simulation for fundamental and widely used digital modulation techniques such as Binary Amplitude-shift keying (BASK), Binary Frequency-shift keying (BFSK), Binary Phase-shift keying (BPSK) and Quadrature Phase Shift Keying(QPSK). In this work the idea of sinusoidal signals that have been generated is plain sailing in nature and based on fundamentals of signal sampling and quantization. Such concept of sinusoidal signals generation is not unfamiliar but somehow simplified using sampling and quantization in time and amplitude domain, respectively. The whole simulation is done on Modelsim and Xilinx-ISE using VERILOG Hardware descriptive language. The work has been accomplished on Thirty two bit serial data transmission with self-adjustable carrier frequency and bit duration length.

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Athavale, Yashodhan, and Sridhar Krishnan. "A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health." Sensors 18, no. 9 (September 6, 2018): 2966. http://dx.doi.org/10.3390/s18092966.

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Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

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Xu, Wei, Lu Bai, Pingping Huang, Weixian Tan, and Yifan Dong. "A Space-Borne SAR Azimuth Multi-Channel Quantization Method." Electronics 13, no. 6 (March 17, 2024): 1102. http://dx.doi.org/10.3390/electronics13061102.

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The space-borne synthetic aperture radar (SAR) azimuth multi-channel system has extensive applications because it can achieve high-resolution and wide-swath radar imaging. The thermal noise generated by the radar receiver of each channel during operation will cause an imbalance between channels. If the echoes of each channel are quantized with the same number of bits without considering the influence of thermal noise, false targets will appear in the imaging consequences. Considering that the thermal noise generated in the receiver will affect the quantization process of the space-borne SAR azimuth multi-channel system, a new space-borne SAR azimuth multi-channel quantization method is proposed to improve this problem. Firstly, the pure noise power of the receiver is calculated without transmitting the radar signal. The signal power is estimated by subtracting the pure noise power from the total power. Then, the average value of the radar echo signal minus k times the standard deviation is used as the left endpoint of the original data amplitude range, and the average value of the radar echo signal plus k times the standard deviation is used as the right endpoint of the original data amplitude range. The original echo data after adjusting the amplitude range is quantified. This method can effectively reduce the influence of thermal noise and random outliers in the receiver on quantization and suppress the appearance of false targets. Finally, simulation is used to confirm the viability of the suggested quantization approach.

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Yakimov, V. N. "Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals." Devices and Methods of Measurements 12, no. 3 (October 15, 2021): 220–21. http://dx.doi.org/10.21122/2220-9506-2021-12-3-220-221.

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The method of averaging modified periodograms is one of the main methods for estimating the power spectral density (PSD). The aim of this work was the development of mathematical and algorithmic support, which can increase the computational efficiency of signals digital spectral analysis by this method.The solution to this problem is based on the use of binary-sign stochastic quantization for converting the analyzed signal into a digital code. A special feature of this quantization is the use of a randomizing uniformly distributed auxiliary signal as a stochastic continuous quantization threshold (threshold function). Taking into account the theory of discrete-event modeling the result of binary-sign quantization is interpreted as a chronological sequence of instantaneous events in which its values change. In accordance with this we have a set of time samples that uniquely determine the result of binary-sign quantization in discrete-time form. Discrete-event modeling made it possible to discretize the process of calculating PSD estimates. As a result, the calculation of PSD estimates was reduced to discrete processing of the cosine and sine Fourier transforms for window functions. These Fourier transforms are calculated analytically based on the applied window functions. The obtained mathematical equations for calculating the PSD estimates practically do not require multiplication operations. The main operations of these equations are addition and subtraction. As a consequence, the time spent on digital spectral analysis of signals is reduced.Numerical experiments have shown that the developed mathematical and algorithmic support allows us to calculate the PSD estimates by the method of averaging modified periodograms with a high frequency resolution and accuracy even for a sufficiently low signal-to-noise ratio. This result is especially important for spectral analysis of broadband signals.The developed software module is a problem-oriented component that can be used as part of metrologically significant software for the operational analysis of complex signals.

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Ma, Wen Hua, Jin Xu, and Yu Li You. "Temporal Transient Localization for Enhanced Pre-Echo Suppression." Applied Mechanics and Materials 339 (July 2013): 355–60. http://dx.doi.org/10.4028/www.scientific.net/amm.339.355.

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A specially designed short window of reduced effective size and other supporting windows, as used in a switched MDCT audio codec, are sequenced in such a way that this special window is placed around transient samples, thereby providing transient localization while leaving frequency resolution unchanged. Pre-echo is better suppressed because transient localization reduces thespread of both quantization noise and high bitrates associated with transients.The overheads include one or two additiona lbits and a short procedure, both for windows equencing, and more memory for storing additional window functions.

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Liu, Xiao Wei, Hong Lin Xu, Bing Jun Lv, Jia Jun Zhou, Song Chen, and Jian Yang. "Nonlinearity Analysis of Closed-Loop ΣΔ Micro-Mechanical Accelerometer." Key Engineering Materials 562-565 (July 2013): 380–86. http://dx.doi.org/10.4028/www.scientific.net/kem.562-565.380.

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A high-order interface circuit based on sigma-delta (ΣΔ) modulation is advantageous to enhance the resolution and reduce the quantization noise of micro-accelerometer, while the performance is restricted by the effect of nonlinearity of op-amp, electrostatic force feedback and quantizer. A fifth-order closed-loop ΣΔ capacitive accelerometer is proposed in this paper and a theoretic nonlinearity of the accelerometer was investigated. The nonlinear behavioral models based on SIMULINK are given in the paper. The simulation and test result are presented through optimization based on the nonlinearity analysis at last.

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Journal articles on the topic 'High-Resolution quantization'

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