Journal articles: 'Sparse adder'

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BKondalkar, Mangesh, Arunkumar P Chavan, and P. Narashimaraja. "Improved Fault Tolerant Sparse KOGGE Stone ADDER." International Journal of Computer Applications 75, no. 10 (2013): 36–41. http://dx.doi.org/10.5120/13150-0582.

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Kumar Athur, Deepak, Bhuvanesh Narayanan, Amshuman Gopalakrishnan, Sasipriya Palanisamy, and Anita Angeline Augustine. "Design of novel high speed parallel prefix adder." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1345. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1345-1354.

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Adders are crucial logical building blocks found almost in all the modern electronic system designs. In the adder architecture design, the fundamental issue is the propagation latency in the carry chain. As the length of the input operands increases, the length of the carry chain along with it. Parallel prefix adders, which address the problem of carry propagation in adders, are the most efficient adder topologies for hardware implementation. However, delay reduction still could be achieved for very high speed applications. Hence, in this paper design of 16bit novel parallel prefix adder is proposed and compared against the existing parallel prefix adder architectures. The design and simulation are carried out using xilinx vivado for field- programmable gate array (FPGA) simulation and Cadence® for ASIC. The results of ASIC implementation demonstrate 17.8% delay reduction while compared to sparse kogge-stone adder.

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Deepak, Kumar Athur, Narayanan Bhuvanesh, Gopalakrishnan Amshuman, Palanisamy Sasipriya, and Angeline Augustine Anita. "Design of novel high speed parallel prefix adder." Design of novel high speed parallel prefix adder 29, no. 3 (2023): 1345–54. https://doi.org/10.11591/ijeecs.v29.i3.pp1345-1354.

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Adders are crucial logical building blocks found almost in all the modern electronic system designs. In the adder architecture design, the fundamental issue is the propagation latency in the carry chain. As the length of the input operands increases, the length of the carry chain along with it. Parallel prefix adders, which address the problem of carry propagation in adders, are the most efficient adder topologies for hardware implementation. However, delay reduction still could be achieved for very high speed applications. Hence, in this paper design of 16bit novel parallel prefix adder is proposed and compared against the existing parallel prefix adder architectures. The design and simulation are carried out using xilinx vivado for field- programmable gate array (FPGA) simulation and Cadence® for ASIC. The results of ASIC implementation demonstrate 17.8% delay reduction while compared to sparse kogge-stone adder.

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Dorojevets, M., C. L. Ayala, N. Yoshikawa, and A. Fujimaki. "16-Bit Wave-Pipelined Sparse-Tree RSFQ Adder." IEEE Transactions on Applied Superconductivity 23, no. 3 (2013): 1700605. http://dx.doi.org/10.1109/tasc.2012.2233846.

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Anagani, Vamsidhar, Kasi Geethanjali, Anusha Gorantla, and Annamreddy Devi. "An improved approximate parallel prefix adder for high performance computing applications: a comparative analysis." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 2 (2025): 382. https://doi.org/10.11591/ijict.v14i2.pp382-392.

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Binary adders are fundamental in digital circuit designs, including digital signal processors and microprocessor data path units. Consequently, significant research has focused on improving adders’ power-delay efficiency. The carry tree adder (CTA) is alternatively referred to as the parallel prefix adder (PPA), is among the fastest adders, achieving superior performance in very large scale integrated (VLSI) implementations through efficient concurrent carry generation and propagation. This study introduces approximate PPAs (AxPPAs) by applying approximations in prefix operators (POs). Four types of AxPPAs approximate kogge-stone, approximate brent-kung, approximate ladner fischer, and approximate sparse kogge-stone-were designed and implemented on FPGA with bit widths up to 64-bit. Delay measurements from static timing analysis using Xilinx ISE design suite version 14.7 indicate that AxPPAs exhibit better latency performance than traditional PPAs. The AxPPA sparse kogge-stone, in particular, demonstrated superior area and speed performance, achieving a delay of 2.501ns for a 16-bit addition.

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S., Lakshmipriya. "A Review on Implementation of Parallel Prefix Adders using FPGA'S." International Journal of Trend in Scientific Research and Development 2, no. 1 (2019): 1304–6. https://doi.org/10.31142/ijtsrd7165.

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The paper mainly used in the implementation of parallel prefix adders using FPGA'S. The carry tree adders constitute spanning tree adder, sparse Kogge Stone and Kogge Stone adder. It also presents a representation of the carry skip adder. The best resolution of the VLSI system is obtained by the Prefix adders. Thus the implementation of the block diagram is difficult so the FPGA technology is used instead. They produce the high efficiency of the layout using the FPGA analysis. Better delay and performance of the RCA design in the 128 bits in the use of the fast chain array. Xilinx Spartan 3E is used in the implementation of the design. RCA is most commonly used in the implementation of the full adder and half adder. RCA is a serial which can perform many number of the addition but due to some situation delay occurs. Xilinx ISE is used in the simulation of the design. S. Lakshmipriya "A Review on Implementation of Parallel Prefix Adders using FPGA'S" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: https://www.ijtsrd.com/papers/ijtsrd7165.pdf

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KHAN, A., and S. WAIRYA. "Efficient and Power-Aware Design of a Novel Sparse Kogge-Stone Adder using Hybrid Carry Prefix Generator Adder." Advances in Electrical and Computer Engineering 24, no. 1 (2024): 71–80. http://dx.doi.org/10.4316/aece.2024.01008.

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Choi, Seongrim, Jonghun Ahn, Kyungjin Byun, and Byeong‐Gyu Nam. "Energy‐efficient static sparse‐tree adder based on MUX‐less bypassing architecture." Electronics Letters 50, no. 25 (2014): 1914–15. http://dx.doi.org/10.1049/el.2014.1639.

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Hoe, David H. K., L. P. Deepthi Bollepalli, and Chris D. Martinez. "FPGA Fault Tolerant Arithmetic Logic: A Case Study Using Parallel-Prefix Adders." VLSI Design 2013 (November 7, 2013): 1–10. http://dx.doi.org/10.1155/2013/382682.

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This paper examines fault tolerant adder designs implemented on FPGAs which are inspired by the methods of modular redundancy, roving, and gradual degradation. A parallel-prefix adder based upon the Kogge-Stone configuration is compared with the simple ripple carry adder (RCA) design. The Kogge-Stone design utilizes a sparse carry tree complemented by several smaller RCAs. Additional RCAs are inserted into the design to allow fault tolerance to be achieved using the established methods of roving and gradual degradation. A triple modular redundant ripple carry adder (TMR-RCA) is used as a point of reference. Simulation and experimental measurements on a Xilinx Spartan 3E FPGA platform are carried out. The TMR-RCA is found to have the best delay performance and most efficient resource utilization for an FPGA fault-tolerant implementation due to the simplicity of the approach and the use of the fast-carry chain. However, the superior performance of the carry-tree adder over an RCA in a VLSI implementation makes this proposed approach attractive for ASIC designs.

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Jaiswal, Ritesh Kumar, Chatla Naveen Kumar, and Ram Awadh Mishra. "Area Efficient Sparse Modulo 2n - 3 Adder." Circuits and Systems 07, no. 12 (2016): 4024–35. http://dx.doi.org/10.4236/cs.2016.712333.

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Mathew, S., M. Anders, R. K. Krishnamurthy, and S. Borkar. "A 4-GHz 130-nm address generation unit with 32-bit sparse-tree adder core." IEEE Journal of Solid-State Circuits 38, no. 5 (2003): 689–95. http://dx.doi.org/10.1109/jssc.2003.810056.

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B. Jadhav, Sachin, and Nikhil Niwas Mane. "A Novel High Speed FPGA Architecture for FIR Filter Design." International Journal of Reconfigurable and Embedded Systems (IJRES) 1, no. 1 (2012): 1. http://dx.doi.org/10.11591/ijres.v1.i1.pp1-10.

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This paper presents the details of hardware implementation of linear phase FIR filter using merged MAC architecture. Speed of convolution operation of FIR filter is improved using merged MAC architecture. By exploiting the reduced complexity made possible by the use of sparse powers of two partial products terms coefficients, an FIR filter tap can be implemented with 2B full adders, and 2B latches, where B is intermediate wordlegnth. Word and bit level parallelism allows high sampling rates, limited only by the full adder delay. The proposed architecture is based on binary tree constructed using modified 4:2 and 5:2 compressor circuits. Increasing the speed of operation is achieved by using higher modified compressors in critical path. Our objective of work is, to increase the speed of multiplication and accumulation operation by minimizing the number of combinational gates using higher n: 2 compressors, which is required more for Array multiplier at the time of implementation of array architecture. This novel architecture allows the implementation of high sampling rate filters of significant length on FPGA Spartan-3 device (XC3S400 PQ-208). The simulation result shows convolution output of digital FIR filter which is done using Questa Sim 6.4c Mentor Graphics tool. The experimental test of the proposed digital FIR filter is done using Spartan-3 device (XC3S400 PQ-208)

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Young, Malcolm P., Stefano Panzeri, and Robert Robertson. "A population code with added grandmothers?" Behavioral and Brain Sciences 23, no. 4 (2000): 495–96. http://dx.doi.org/10.1017/s0140525x00523358.

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Page's “localist” code, a population code with occasional, maximally firing elements, does not seem to us usefully or testably different from sparse population coding. Some of the evidence adduced by Page for his proposal is not actually evidence for it, and coding by maximal firing is challenged by lower firing observed in neuronal responses to natural stimuli.

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Wei, Xingxing, Jun Zhu, Sha Yuan, and Hang Su. "Sparse Adversarial Perturbations for Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8973–80. http://dx.doi.org/10.1609/aaai.v33i01.33018973.

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Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored. Compared with images, attacking a video needs to consider not only spatial cues but also temporal cues. Moreover, to improve the imperceptibility as well as reduce the computation cost, perturbations should be added on as few frames as possible, i.e., adversarial perturbations are temporally sparse. This further motivates the propagation of perturbations, which denotes that perturbations added on the current frame can transfer to the next frames via their temporal interactions. Thus, no (or few) extra perturbations are needed for these frames to misclassify them. To this end, we propose the first white-box video attack method, which utilizes an l2,1-norm based optimization algorithm to compute the sparse adversarial perturbations for videos. We choose the action recognition as the targeted task, and networks with a CNN+RNN architecture as threat models to verify our method. Thanks to the propagation, we can compute perturbations on a shortened version video, and then adapt them to the long version video to fool DNNs. Experimental results on the UCF101 dataset demonstrate that even only one frame in a video is perturbed, the fooling rate can still reach 59.7%.

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Yang Chuping, 杨初平, 蔡汶曦 Cai Wenxi, and 翁嘉文 Weng Jiawen. "Window-Added Sampling for Improving Sparsity of Non-Sparse Representation of Signals." Laser & Optoelectronics Progress 52, no. 3 (2015): 031002. http://dx.doi.org/10.3788/lop52.031002.

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Chen, Gong, Xilu Lou, Chunxiang Li, Xifang Zhu, Xu Cheng, and Qingquan Xu. "Recognition of Pole Piece Defects of Lithium Battery Based on Sparse Decomposition." Open Electrical & Electronic Engineering Journal 9, no. 1 (2015): 540–46. http://dx.doi.org/10.2174/1874129001509010540.

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To study surface denoising of lithium battery film to extract feature effectively. The best atomic function by sparse decomposition is acquired by iteration under added noise, gaussian noise, salt and pepper noise, additive and multiplicative noise. Terminating iteration value is got by observation and used to filter under specific background noise. Experiment shows sparse decomposition denoising performance is better than the median filter, sparse decomposition is good for detection of lithium battery film defects.

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Kusumah, Dea Legina Ayu, and Chaznin R. Muhammad. "Penerapan 5s (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) untuk Reduksi Non Value Added Activity di PT X." Jurnal Riset Teknik Industri 1, no. 2 (2022): 143–53. http://dx.doi.org/10.29313/jrti.v1i2.484.

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Abstract. PT X is a manufacturing company in publishing, printing, security printing, and general trade. The company has several large machines to support the production process. The company also provides spare parts used for the engine repair process. The problem at the company is the high time to take and wait for spare parts that cause high downtime. The activity of taking spare parts makes time for 60 minutes and the activity of waiting for spare parts takes 2.880 minutes. The high time to take and wait is due to spare parts storage that does not separate old and new spare parts, spare parts are not arranged according to type, there is no sign according to the type of spare parts, the contents of the storage area are not visible, there are different spare parts in the same location, and there is no sign of spare parts stock. Taking and waiting activities are non value added activity (NVAA). Therefore, there needs to be improvements to reduce non value added activity. Improvement are made with the application of the 5S (seiri, seiton, seiso, seiketsu, and shitsuke). The 5S method can be used to rearrange spare parts storage in the company, so that spare parts are easy to find and availability can be known. After repairs, there was a decrease in the time to take spare parts by 95.37% and a decrease in spare parts waiting time by 100%. Abstrak. PT X merupakan perusahaan manufaktur yang bergerak pada bidang penerbitan, percetakan, security printing, dan perdagangan umum. Perusahaan ini memiliki beberapa mesin besar untuk menunjang proses produksi. Perusahaan juga menyediakan sparepart yang digunakan untuk proses perbaikan mesin. Permasalahan yang dihadapi PT X adalah tingginya waktu mengambil dan menunggu sparepart yang menyebabkan downtime tinggi. Aktivitas mengambil sparepart membuatuhkan waktu selama 60 menit dan aktivitas menunggu sparepart membutuhkan waktu selama 2.880 menit. Tingginya waktu mengambil dan menunggu disebabkan karena penyimpanan sparepart yang tidak memisahkan sparepart lama dan baru, sparepart tidak disusun sesuai jenis, tidak ada tanda sesuai jenis sparepart, isi dari tempat penyimpanan tidak terlihat, terdapat sparepart berbeda pada lokasi yang sama, dan tidak ada tanda stok sparepart. Aktivitas mengambil dan aktivitas menunggu merupakan aktivitas yang tidak memberikan nilai tambah. Oleh karena itu perlu adanya perbaikan untuk mereduksi aktivitas yang tidak memberikan nilai tambah. Upaya perbaikan dilakukan dengan penerapan 5S (seiri, seiton, seiso, seiketsu, and shitsuke). Metode 5S dapat digunakan untuk menata ulang penyimpanan sparepart di perusahaan, sehingga sparepart mudah ditemukan dan ketersediaan sparepart juga dapat diketahui. Setelah dilakukan perbaikan, terjadi penurunan waktu mengambil sparepart sebesar 95,37% dan penurunan waktu menunggu sparepart sebesar 100%.

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Feng, Junjie. "ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm." Mathematical Problems in Engineering 2020 (July 13, 2020): 1–8. http://dx.doi.org/10.1155/2020/1743593.

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A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

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Džamonja, Mirna. "Some Banach spaces added by a Cohen real." Topology and its Applications 195 (November 2015): 246–55. http://dx.doi.org/10.1016/j.topol.2015.09.027.

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Blassberg, Maksymilian, Nestor D. Kapusta, and Marina Bluvshtein. "Alfred Adler as a Doctor (Against the Background of Personal Memories): A Psychobiographical Fragment of Adler During World War I." Journal of Individual Psychology 79, no. 4 (2023): 297–302. http://dx.doi.org/10.1353/jip.2023.a915972.

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ABSTRACT: Sparse historical sources describe Alfred Adler’s life during World War I, and even fewer sources exist that would provide a thorough understanding of his life in that challenging time. An accidentally found obituary written in 1938 by the Polish Jewish physician Dr. Maksymilian Blassberg in a Polish medical journal unveils some interesting details about Adler’s duty as a military neurologist in the Kraków military hospital in 1917. This article helps fill the gap in understanding the circumstances of Adler’s life at the time. Combined with field letters from Kraków, the article provides a vivid psychobiographical picture of how Adler dealt with wartime circumstances, with fears of being mobilized to the front and the practices of war psychiatry. Many parallels inspire practitioners to think about the dangers and difficulties of today’s Individual Psychologists in Ukraine facing devastating war conditions. The English translation of the obituary is presented here.

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WEN, YING, and LIANGHUA HE. "AN IMPROVED LOCALLY LINEAR EMBEDDING FOR SPARSE DATA SETS." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 05 (2011): 763–75. http://dx.doi.org/10.1142/s0218001411008786.

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Locally linear embedding is often invalid for sparse data sets because locally linear embedding simply takes the reconstruction weights obtained from the data space as the weights of the embedding space. This paper proposes an improved method for sparse data sets, a united locally linear embedding, to make the reconstruction more robust to sparse data sets. In the proposed method, the neighborhood correlation matrix presenting the position information of the points constructed from the embedding space is added to the correlation matrix in the original space, thus the reconstruction weights can be adjusted. As the reconstruction weights adjusted gradually, the position information of sparse points can also be changed continually and the local geometry of the data manifolds in the embedding space can be well preserved. Experimental results on both synthetic and real-world data show that the proposed approach is very robust against sparse data sets.

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Kiaee, Farkhondeh, Christian Gagné, and Hamid Sheikhzadeh. "A double-layer ELM with added feature selection ability using a sparse Bayesian approach." Neurocomputing 216 (December 2016): 371–80. http://dx.doi.org/10.1016/j.neucom.2016.08.011.

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Folz, David H., and Chris Shults. "The Value Added by Professional Certification of Municipal Finance Officers." State and Local Government Review 50, no. 2 (2018): 85–97. http://dx.doi.org/10.1177/0160323x18789535.

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Many academics and practitioners value professional training and certification programs as important ways to improve management practice and organizational performance. However, these programs often are among the first cutback targets in times of fiscal stress. Evidence that documents the actual impacts of specialized training and certification on public organizational performance is sparse. This longitudinal panel study examines the effects of a state-mandated Certified Municipal Finance Officer program on cities’ financial management performance and finds that the program had a statistically significant independent impact on the observed reduction in the number of annual outside audit findings. The program’s impact on improved financial management performance underscores the need for scholars to evaluate similar programs, the results from which may help to alter public officials’ cutback calculus and restore public confidence in the public service.

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Carman, P. G., A. D. Milner, G. M. Stokes, and A. J. Wilson. "VENTILATORY RESPONSE TO ADDED DEAD SPACES IN PREMATURE INFANTS." Pediatric Research 19, no. 10 (1985): 1100. http://dx.doi.org/10.1203/00006450-198510000-00185.

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Wang, Duo, and Toshihisa Tanaka. "Kernel Principal Component Analysis Allowing Sparse Representation and Sample Selection." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 1 (2019): 9–20. http://dx.doi.org/10.37936/ecti-cit.2019131.187506.

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Kernel principal component analysis (KPCA) is a kernelized version of principal component analysis (PCA). A kernel principal component is a superposition of kernel functions. Due to the number of kernel functions equals the number of samples, each component is not a sparse representation. Our purpose is to sparsify coefficients expressing in linear combination of kernel functions, two types of sparse kernel principal component are proposed in this paper. The method for solving sparse problem comprises two steps: (a) we start with the Pythagorean theorem and derive an explicit regression expression of KPCA and (b) two types of regularization $l_1$-norm or $l_{2,1}$-norm are added into the regression expression in order to obtain two different sparsity form, respectively. As the proposed objective function is different from elastic net-based sparse PCA (SPCA), the SPCA method cannot be directly applied to the proposed cost function. We show that the sparse representations are obtained in its iterative optimization by conducting an alternating direction method of multipliers. Experiments on toy examples and real data confirm the performance and effectiveness of the proposed method.

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Arogunmati, Adeyemi, and Jerry M. Harris. "An effective crosswell seismic traveltime-estimation approach for quasi-continuous reservoir monitoring." GEOPHYSICS 77, no. 2 (2012): M17—M26. http://dx.doi.org/10.1190/geo2011-0197.1.

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We present an iterative approach for quasi-continuous time-lapse seismic reservoir monitoring. This approach involves recording sparse data sets frequently, rather than complete data sets infrequently. In other words, it involves acquiring a completely sampled baseline data set followed by sparse monitor data sets at short calendar-time intervals. We use the term “sparse” to describe a data set that is a small fraction of what would normally be recorded in the field to reconstruct a high-spatial-resolution image of the subsurface. Each monitor data set could be as little as 2% of a single, complete conventional data set. The series of recorded time-lapse data sets is then used to estimate missing, unrecorded data in the sparse data sets. The approach was tested on synthetic and field crosswell traveltime data sets. Results show that this approach can be effective for quasi-continuous reservoir monitoring. Also, the accuracy of the estimated data increases as more sparse data sets are added to the time-lapse data series. Finally, a moving estimation window can be used to reduce computational effort for estimating data.

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Ding, Yiyang, Anyong Qin, Zhaowei Shang, and Jiye Qian. "Spatial distribution preserving-based sparse subspace clustering for hyperspectral image." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (2019): 1940010. http://dx.doi.org/10.1142/s0219691319400101.

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The high dimensionality and heterogeneity of the hyperspectral image (HSI) make a challenge to the application of machine learning methods, such as sparse subspace clustering (SSC). SSC is designed to represent data as an union of affine subspaces, while it cannot capture the latent structure of the given data. In Mosers theory, the distribution can represent the intrinsic structure efficiently. Hence, we propose a novel approach called spatial distribution preserving-based sparse subspace clustering (SSC-SDP) in this paper for HSI data, which can help sparse representation preserve the underlying manifold structure. Specifically, the density constraint is added by minimizing the inconsistency of the densities estimated in the HSI data and the corresponding sparse coefficient matrix. In addition, we incorporate spatial information into the density estimation of the original data, and the optimization solution based on alternating direction method of multipliers (ADMM) is devised. Three HSI data sets are conducted to evaluate the performance of our SSC-SDP compared with other state-of-art algorithms.

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Yao, Jean Yves Aristide, Kacoutchy Jean Ayikpa, Pierre Gouton, and Tiemoman Kone. "A Multi-Shot Approach for Spatial Resolution Improvement of Multispectral Images from an MSFA Sensor." Journal of Imaging 10, no. 6 (2024): 140. http://dx.doi.org/10.3390/jimaging10060140.

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Multispectral imaging technology has advanced significantly in recent years, allowing single-sensor cameras with multispectral filter arrays to be used in new scene acquisition applications. Our camera, developed as part of the European CAVIAR project, uses an eight-band MSFA to produce mosaic images that can be decomposed into eight sparse images. These sparse images contain only pixels with similar spectral properties and null pixels. A demosaicing process is then applied to obtain fully defined images. However, this process faces several challenges in rendering fine details, abrupt transitions, and textured regions due to the large number of null pixels in the sparse images. Therefore, we propose a sparse image composition method to overcome these challenges by reducing the number of null pixels in the sparse images. To achieve this, we increase the number of snapshots by simultaneously introducing a spatial displacement of the sensor by one to three pixels on the horizontal and/or vertical axes. The set of snapshots acquired provides a multitude of mosaics representing the same scene with a redistribution of pixels. The sparse images from the different mosaics are added together to get new composite sparse images in which the number of null pixels is reduced. A bilinear demosaicing approach is applied to the composite sparse images to obtain fully defined images. Experimental results on images projected onto the response of our MSFA filter show that our composition method significantly improves image spatial resolution and minimizes reconstruction errors while preserving spectral fidelity.

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Ohuchi, N., T. Ogitsu, Y. Ajima, S. Sasaki, and K. Tsuchiya. "Field quality measurements of spare TRISTAN insertion quadrupole magnet with yoke added." IEEE Transactions on Appiled Superconductivity 7, no. 2 (1997): 574–77. http://dx.doi.org/10.1109/77.614569.

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Yang, Geonmo, Juhui Lee, Ayoung Kim, and Younggun Cho. "Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection." Sensors 23, no. 3 (2023): 1212. http://dx.doi.org/10.3390/s23031212.

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We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes.

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Wei, Yun, Hua Chen, Senqing Hu, et al. "Overcoming the challenges of a shallow-water sparse wide-azimuth survey to improve deep reservoir imaging in the East China Sea." Leading Edge 38, no. 8 (2019): 610–16. http://dx.doi.org/10.1190/tle38080610.1.

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A new broadband wide-azimuth towed-streamer (WATS) survey was acquired to better resolve reservoir compartments in a shallow-water region of the East China Sea. To offset the shortcomings of narrow-azimuth acquisition along the strike direction, two vessels were added side-by-side as additional source vessels to form the WATS acquisition geometry for this survey. This WATS acquisition was much sparser than typical WATS surveys used in deepwater environments due to its one-sided configuration. The combination of sparse acquisition, shallow water, and deep targets set the challenge of how to optimally reveal the potential of side-gun data to improve the final image. Three-dimensional effects and severe aliasing in the crossline direction pose significant challenges for side-gun data processing. We present a comprehensive workflow to resolve these challenges consisting of 3D deghosting, 3D model-based water-layer demultiple, 3D surface-related multiple elimination, and 4D regularization for sparse and shallow-water wide-azimuth data. A tilted orthorhombic velocity model is built with better constraints from the wide-azimuth data, leading to improved fault positioning and imaging. Side-gun data clearly enhance the final target reservoir image and tie better with well data due to improved illumination. A new channel is discovered based on interpretation from the inverted VP/VS, explaining the previous incorrect prediction for one failed well that was drilled into a thinner and shallower channel unconnected to the main reservoir. An analysis of the impact of side-gun data from different offsets and azimuths shows that better azimuthal distribution within middle offset ranges had a more significant impact than far offsets in the final image of this survey. This information provides valuable reference in similar geologic conditions for future acquisition designs.

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Guo, Zhenyu, Yujuan Sun, Muwei Jian, and Xiaofeng Zhang. "Deep Residual Network with Sparse Feedback for Image Restoration." Applied Sciences 8, no. 12 (2018): 2417. http://dx.doi.org/10.3390/app8122417.

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A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or noisy images. Experimental results show the effectiveness of our designed model and method.

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Martín, Nuria, and Francisco G. Cirujano. "Organic synthesis of high added value molecules with MOF catalysts." Organic & Biomolecular Chemistry 18, no. 40 (2020): 8058–73. http://dx.doi.org/10.1039/d0ob01571g.

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Recent examples of organic synthesis of fine chemicals and pharmaceuticals in confined spaces of MOFs are highlighted and compared with silica-based ordered porous solids, such as zeolites or mesoporous (organo)silica.

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Chen, Renxiang, Siyang Chen, Miao He, David He, and Baoping Tang. "Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 6 (2017): 666–79. http://dx.doi.org/10.1177/1748006x17726452.

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Liu, Haiming, Bo Xu, and Bin Liu. "A Tracking Algorithm for Sparse and Dynamic Underwater Sensor Networks." Journal of Marine Science and Engineering 10, no. 3 (2022): 337. http://dx.doi.org/10.3390/jmse10030337.

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An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF.

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Schultz, Gary E., and Peter H. Santschi. "Effect of the Added Acyl Homoserine Lactones on Separated Free-Living Marine Bacteria as a Model of Quorum Sensing." Journal of Marine Science and Engineering 11, no. 7 (2023): 1258. http://dx.doi.org/10.3390/jmse11071258.

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Quorum sensing is a communication system by which bacteria use signal molecules to induce a physiological response. In natural marine environments, quorum sensing is suspected to occur in regions with high cell densities. Free-living bacteria, however, are largely believed to exist at concentrations too low to make use of a density-dependent quorum-sensing system. Due to so-called ‘free-living’ bacteria inhabiting marine gels composed of exopolymeric substances occurring in an operationally defined colloidal fraction, it is possible that quorum-sensing molecules would also occur in this fraction. In this study, possible signaling molecules were collected from marine water, separated, and concentrated as some organic matter. Their identification with an Agrobacterium tumefaciens assay indicated the presence of AHLs. In fall 2002 and spring 2003, free-living (single cells) fraction of marine bacterioplankton was sampled and separated. Various AHLs were added to these disperse populations (109 cells/L). The studied bacterial communities responded with change in the thymidine incorporation. The results are discussed as existence of active reaction of marine free-living bacteria to signaling molecules even in sparse inhabiting marine gels. That there is a bacterial response to signaling molecules in so sparse populations is of great significance for marine and environmental sciences.

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Espinoza, B., and W. Wharton. "Effects of cholera toxin and isobutylmethylxanthine on growth of human fibroblasts." American Journal of Physiology-Cell Physiology 251, no. 2 (1986): C238—C246. http://dx.doi.org/10.1152/ajpcell.1986.251.2.c238.

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Cholera toxin produced a dose-dependent decrease in the restimulation of G0/G1 traverse in density-arrested human fibroblasts but did not inhibit the stimulation of cells arrested in G0 after serum starvation at low density. In addition, cholera toxin did not inhibit the proliferation of sparse logarithmically growing human fibroblasts, even when low concentrations of the phosphodiesterase inhibitor isobutylmethylxanthine (IBMX) were also present. However, the final density to which sparse cells grew was limited by cholera toxin, when added either alone or together with low concentrations of IBMX. In contrast, high concentrations of the phosphodiesterase inhibitor alone produced a profound inhibition in the growth of sparse human fibroblasts. IBMX produced an inhibition both in the G1 and in the G2 phases of the cell cycle by a mechanism(s) that was not related to the magnitude of the increases in adenosine 3',5'-cyclic monophosphate concentrations.

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Zhang, Shuaiyang, Wenshen Hua, Gang Li, Jie Liu, Fuyu Huang, and Qianghui Wang. "Double Regression-Based Sparse Unmixing for Hyperspectral Images." Journal of Sensors 2021 (September 3, 2021): 1–14. http://dx.doi.org/10.1155/2021/5575155.

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Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression-based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression-based sparse unmixing via K -means ( DRSU M K − means ) is proposed. The improved K -means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l 2 , 0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row-hard-threshold function to solve the l 2 , 0 norm directly. Then, DRSU M K − means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSU M K − means .

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Zhang, Shanwen, Chuanlei Zhang, Yihai Zhu, and Zhuhong You. "Discriminant WSRC for Large-Scale Plant Species Recognition." Computational Intelligence and Neuroscience 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/9581292.

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In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

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Zhang, Ning, Zhenghui Xue, Pei Zheng, Lu Gao, and Jiaqi Liu. "Synthesis of Low Sidelobe Pattern with Enhanced Axial Radiation for Sparse Conformal Arrays Based on MCDE Algorithm." Electronics 11, no. 22 (2022): 3679. http://dx.doi.org/10.3390/electronics11223679.

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A hybrid optimization method for the synthesis of a sparse conformal array with the verification of a truncated cone antenna array is proposed in this manuscript. This array synthesis is studied aiming at enhancing axial radiation and reducing the peak sidelobe level (PSLL) by figuring out the optimal antenna element arrangement and corresponding feeding scheme. A multi-agent composite differential evolution (MCDE) algorithm is established by integrating a multi-agent system (MAS) with a differential evolution (DE) algorithm. In addition, a hybrid strategy method and a time-varying weighting factor are added to the mutation operator to accelerate convergence. Two examples of 64-element and 900-element truncated cone antenna arrays were synthesized. After forming a sparse antenna array out of the original full array, the number of antennas was decreased to 80% and 56.8%, respectively. The results show that the main beam of the sparse conformal antenna array is accurately fixed to the axial direction with the PSLL less than −20 dB at both the φ = 0° and φ = 90° planes, which proves the effectiveness of this method in conformal sparse array synthesis.

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Yu, Nannan, Funian Hu, Dexuan Zou, Qisheng Ding, and Hanbing Lu. "Single-Trial Sparse Representation-Based Approach for VEP Extraction." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8569129.

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Sparse representation is a powerful tool in signal denoising, and visual evoked potentials (VEPs) have been proven to have strong sparsity over an appropriate dictionary. Inspired by this idea, we present in this paper a novel sparse representation-based approach to solving the VEP extraction problem. The extraction process is performed in three stages. First, instead of using the mixed signals containing the electroencephalogram (EEG) and VEPs, we utilise an EEG from a previous trial, which did not contain VEPs, to identify the parameters of the EEG autoregressive (AR) model. Second, instead of the moving average (MA) model, sparse representation is used to model the VEPs in the autoregressive-moving average (ARMA) model. Finally, we calculate the sparse coefficients and derive VEPs by using the AR model. Next, we tested the performance of the proposed algorithm with synthetic and real data, after which we compared the results with that of an AR model with exogenous input modelling and a mixed overcomplete dictionary-based sparse component decomposition method. Utilising the synthetic data, the algorithms are then employed to estimate the latencies of P100 of the VEPs corrupted by added simulated EEG at different signal-to-noise ratio (SNR) values. The validations demonstrate that our method can well preserve the details of the VEPs for latency estimation, even in low SNR environments.

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Bao, Han, and Shinsaku Sakaue. "Sparse Regularized Optimal Transport with Deformed q-Entropy." Entropy 24, no. 11 (2022): 1634. http://dx.doi.org/10.3390/e24111634.

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Optimal transport is a mathematical tool that has been a widely used to measure the distance between two probability distributions. To mitigate the cubic computational complexity of the vanilla formulation of the optimal transport problem, regularized optimal transport has received attention in recent years, which is a convex program to minimize the linear transport cost with an added convex regularizer. Sinkhorn optimal transport is the most prominent one regularized with negative Shannon entropy, leading to densely supported solutions, which are often undesirable in light of the interpretability of transport plans. In this paper, we report that a deformed entropy designed by q-algebra, a popular generalization of the standard algebra studied in Tsallis statistical mechanics, makes optimal transport solutions supported sparsely. This entropy with a deformation parameter q interpolates the negative Shannon entropy (q=1) and the squared 2-norm (q=0), and the solution becomes more sparse as q tends to zero. Our theoretical analysis reveals that a larger q leads to a faster convergence when optimized with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In summary, the deformation induces a trade-off between the sparsity and convergence speed.

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Feng, Junjie, Yinan Sun, and XiuXia Ji. "High-Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm." Wireless Communications and Mobile Computing 2021 (April 2, 2021): 1–7. http://dx.doi.org/10.1155/2021/5541116.

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In order to solve the problem of high-resolution ISAR imaging under the condition of finite pulses, an improved smoothed L0 norm (SL0) sparse signal reconstruction ISAR imaging algorithm is proposed. Firstly, the ISAR imaging is transformed into the optimization problem of minimum L0 norm. Secondly, a single-loop structure is used instead of two loop layers in SL0 algorithm which increases the searching density of variable parameter to ensure the recovery accuracy. Finally, the compared step is added to ensure the optimization solution along the steepest descent gradient direction. The experimental results show that the proposed algorithm has better imaging effect.

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Fei, Yin, Gao Wei, and Song Zongxi. "Medical Image Fusion Based on Feature Extraction and Sparse Representation." International Journal of Biomedical Imaging 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3020461.

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As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

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Steinbrinker, Tabea, and Nicolai Spicher. "Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets." Current Directions in Biomedical Engineering 10, no. 4 (2024): 612–15. https://doi.org/10.1515/cdbme-2024-2150.

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Abstract Wearable electrocardiography monitors, e.g. embedded in textile shirts, offer new approaches in diagnosis but suffers upon limited computational capacities. Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. For each type of noise a dictionary is built using K-singular value decomposition. This iterative method alternates between finding a sparse representation for every training signal and then updating every atom of the dictionary on its own. A sparse representation is found using the orthogonal matching pursuit algorithm. The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm. Electrocardiography data stems from synthetically-generated signals as well as the freely-available Brno University of Technology ECG Quality Database. Noise is added to the signals using the MIT-BIH Noise Stress Database. Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. Moreover, a small number of training data is sufficient for satisfying results which indicates the suitability of the method for wearable hardware with low memory and power specifications.

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Zhang, Chuncheng, and Zhiying Long. "Euler’s Elastica Regularization for Voxel Selection of fMRI Data." International Journal of Signal Processing Systems 8, no. 2 (2020): 32–41. http://dx.doi.org/10.18178/ijsps.8.2.32-41.

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Multivariate analysis methods have been widely applied to functional Magnetic Resonance Imaging (fMRI) data to reveal brain activity patterns and decode brain states. Among the various multivariate analysis methods, the multivariate regression models that take high-dimensional fMRI data as inputs while using relevant regularization were proposed for voxel selection or decoding. Although some previous studies added the sparse regularization to the multivariate regression model to select relevant voxels, the selected sparse voxels cannot be used to map brain activity of each task. Compared to the sparse regularization, the Euler’s Elastica (EE) regularization that considers the spatial information of data can identify the clustered voxels of fMRI data. Our previous study added EE Regularization to Logical Regression (EELR) and demonstrated its advantages over the other regularizations in fMRI-based decoding. In this study, we further developed a multivariate regression model using EE in 3D space as constraint for voxel selection. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of EE regression model. The performance of EE regression was compared with the Generalized Linear Model (GLM) and Total Variation (TV) regression in brain activity detection, and was compared with GLM, Laplacian Smoothed L0 norm (LSL0) and TV regression methods in feature selection for brain state decoding. The results indicated that EE regression possessed better sensitivity to detect brain regions specific to a task than did GLM and better spatial detection power than TV regression. Moreover, EE regression outperformed GLM, LSL0 and TV in feature selection.

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Hong, Yuxin, Caihong Wang, and Xiuhua Jiang. "A No-Reference Quality Assessment Method for Screen Content Images Based on Human Visual Perception Characteristics." Electronics 11, no. 19 (2022): 3155. http://dx.doi.org/10.3390/electronics11193155.

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The widespread application of screen content images (SCIs) has met the needs of remote display and online working. It is a topic that is challenging and worthwhile discussing in research on quality assessment for SCIs. However, existing methods focus on extracting artificial features to predict image quality, which are subjective and incomplete, or lack good interpretability. To overcome these problems, we propose an effective quality assessment method for SCIs based on human visual perceptual characteristics. The proposed method simulates the multi-channel working mechanism of the human visual system (HVS) through pyramid decomposition and the information extraction process of brains with the help of dictionary learning and sparse coding. The input SCIs are first decomposed at multiple scales, and then dictionary learning and sparse coding are applied to the images at each scale. Furthermore, the sparse representation results are analyzed from multiple perspectives. First, a pooling scheme about generalized Gaussian distribution and log-normal distribution is designed to describe the sparse coefficients with and without zero values, respectively. Then the sparse coefficients are used to characterize the energy characteristics. Additionally, the probability of each atom is calculated to describe the statistical property of SCIs. Since the above process only deals with brightness, color-related features are also added to make the model more general and robust. Experimental results on three public SCI databases show that the proposed method can achieve better performance than existing methods.

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Wang, Lei, Juntao Li, Juanfang Liu, and Mingming Chang. "RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification." Computational and Mathematical Methods in Medicine 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5584684.

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In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.

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Qin, Yuxin, Xinlin Li, Linan Zu, and Ming Liang Jin. "Novel View Synthesis with Depth Priors Using Neural Radiance Fields and CycleGAN with Attention Transformer." Symmetry 17, no. 1 (2025): 59. https://doi.org/10.3390/sym17010059.

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Novel view synthesis aims to generate new perspectives from a limited number of input views. Neural Radiance Field (NeRF) is a key method for this task, and it produces high-fidelity images from a comprehensive set of inputs. However, a NeRF’s performance drops significantly with sparse views. To mitigate this, depth information can be used to guide training, with coarse depth maps often readily available in practical settings. We propose an improved sparse view NeRF model, ATGANNeRF, which integrates an enhanced U-Net generator with a dual-discriminator framework, CBAM, and Multi-Head Self-Attention mechanisms. The symmetric design enhances the model’s ability to capture and preserve spatial relationships, ensuring a more consistent generation of novel views. Additionally, local depth ranking is employed to ensure depth consistency with coarse maps, and spatial continuity constraints are introduced to synthesize novel views from sparse samples. SSIM loss is also added to preserve local structural details like edges and textures. Evaluation on LLFF, DTU, and our own datasets shows that ATGANNeRF significantly outperforms state-of-the-art methods, both quantitatively and qualitatively.

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Ye, Sheng, Yuze He, Matthieu Lin, et al. "PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction." ACM Transactions on Graphics 43, no. 6 (2024): 1–13. http://dx.doi.org/10.1145/3687896.

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Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views. A few pioneering works have sought to tackle this challenge by leveraging additional geometric priors or multi-scene generalizability. However, they are still hindered by the imperfect choice of input views, using images under empirically determined viewpoints. We propose PVP-Recon , a novel and effective sparse-view surface reconstruction method that progressively plans the next best views to form an optimal set of sparse viewpoints for image capturing. PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views which are determined based on a novel warping score that reflects the information gain of each newly added view. This progressive view planning progress is interleaved with a neural SDF-based reconstruction module that utilizes multi-resolution hash features, enhanced by a progressive training scheme and a directional Hessian loss. Quantitative and qualitative experiments on three benchmark datasets show that our system achieves high-quality reconstruction with a constrained input budget and outperforms existing baselines.

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Journal articles on the topic 'Sparse adder'

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