Groupwise Image Alignment via Self Quotient Images

Compared with pairwise registration, the groupwise one is capable of handling a large-scale population of images simultaneously in an unbiased way. In this work we improve upon the state-of-the-art pixel-level, Least-Squares (LS)-based groupwise image registration methods. Specifically, the registration technique is properly adapted by the use of Self Quotient Images (SQI) in order to become capable for solving the groupwise registration of photometrically distorted, partially occluded as well as unimodal and multimodal images. Moreover, the proposed groupwise technique is linear to the cardinality of the image set and thus it can be used for the successful solution of the problem on large image sets with low complexity. From the application of the proposed technique on a series of experiments for the groupwise registration of photometrically and geometrically distorted, partially occluded faces as well as unimodal and multimodal magnetic resonance image sets and its comparison with the Lucas–Kanade Entropy (LKE) algorithm, the obtained results look very promising, in terms of alignment quality, using as figures of merit the mean Peak Signal to Noise Ratio ( m P S N R ) and mean Structural Similarity ( m S S I M ), and computational cost.

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Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Sensors ◽

10.3390/s18124115 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4115 ◽

Cited By ~ 3

Author(s):

Feng Lian ◽

Liming Hou ◽

Bo Wei ◽

Chongzhao Han

Keyword(s):

Target Tracking ◽

Large Scale ◽

Sequential Monte Carlo ◽

Signal To Noise Ratio ◽

Computational Cost ◽

Gaussian Mixture ◽

Descent Method ◽

Sensor Selection ◽

Multi Target Tracking ◽

Finite Set

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

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Exploiting structural similarity in network reliability analysis using graph learning

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211009329 ◽

2021 ◽

pp. 1748006X2110093

Author(s):

Ping Zhang ◽

Min Xie ◽

Xiaoyan Zhu

Keyword(s):

Large Scale ◽

System Analysis ◽

Network Reliability ◽

Computational Cost ◽

Structural Similarity ◽

Learning Approach ◽

Network System ◽

Network Systems ◽

Graph Learning ◽

Significance Levels

Considering the large-scale networks that can represent construction of components in a unit, a transportation system, a supply chain, a social network system, and so on, some nodes have similar topological structures and thus play similar roles in the network and system analysis, usually complicating the analysis and resulting in considerable duplicated computations. In this paper, we present a graph learning approach to define and identify structural similarity between the nodes in a network or the components in a network system. Based on the structural similarity, we investigate component clustering at various significance levels that represent different extents of similarity. We further specify a spectral-graph-wavelet based graph learning method to measure the structural similarity and present its application in easing computation load of evaluating system survival signature and system reliability. The numerical examples and the application show the insights of structural similarity and effectiveness of the graph learning approach. Finally, we discuss potential applications of the graph-learning based structural similarity and conclude that the proposed structural similarity, component clustering, and graph learning approach are effective in simplifying the complexity of the network systems and reducing the computational cost for complex network analysis.

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FPGA Implementation of Adaptive Multiplier-Based Linear Image Interpolation

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j8859.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 551-557

Keyword(s):

Signal To Noise Ratio ◽

Similarity Index ◽

Image Interpolation ◽

Structural Similarity ◽

Linear Interpolation ◽

Low Complexity ◽

Adaptive Interpolation ◽

Field Programmable ◽

Interpolation Techniques

This work provides an image interpolation for multimedia applications by utilizing an adaptive multiplier-based stepwise linear interpolation with clam filter. Image interpolation is also termed as image up-scaling. Generally, while enlarging an image some vacant bit positions are introduced and due to this empty pixel positions, the quality of the image is decreased. Therefore to maintain the quality of the image, new pixels are introduced and those pixels are used to fill the vacant bit positions by using interpolation techniques. In the adaptive interpolation techniques, edge pixels are identified and filtered at prior to the interpolation process. This will improve the quality of the interpolated image. However, the adaptive interpolation scheme increases the complexity of the system. To reduce the complexity, this work uses low complexity stepwise linear interpolation and to maintain the quality it uses multiplier-based linear stepwise (MBLSI) and edge enhancement technique. The experimental results demonstrate that the complexity of the proposed work is less as compared with other related work as well as the quality is also maintained. The proposed work utilizes 275 LUTs to provide the average peak signal to noise ratio (PSNR) of 20.44 dB and structural similarity index (SSIM) as 0.8250. This proposed work increases the PSNR by 0.89 dB from the conventional multiplier-based stepwise linear interpolation. Further the proposed interpolation algorithm utilizes less number of resources in field programmable gate array (FPGA) by comparing with other related interpolation techniques.

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A low-complexity channel training method for efficient SVD beamforming over MIMO channels

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02026-x ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Felipe Kettlun ◽

Fernando Rosas ◽

Christian Oberli

Keyword(s):

Signal To Noise Ratio ◽

Computational Cost ◽

Low Complexity ◽

Iteration Algorithm ◽

Mimo Channels ◽

Maximum Ratio Combining ◽

Alamouti Coding ◽

Order Of Magnitude ◽

The One ◽

Value Decomposition

AbstractSingular value decomposition (SVD) beamforming is an attractive tool for reducing the energy consumption of data transmissions in wireless sensor networks whose nodes are equipped with multiple antennas. However, this method is often not practical due to two important shortcomings: it requires channel state information at the transmitter and the computation of the SVD of the channel matrix is generally too complex. To deal with these issues, we propose a method for establishing an SVD beamforming link without requiring feedback of actual channel or SVD coefficients to the transmitter. Concretely, our method takes advantage of channel reciprocity and a power iteration algorithm (PIA) for determining the precoding and decoding singular vectors from received preamble sequences. A low-complexity version that performs no iterations is proposed and shown to have a signal-to-noise-ratio (SNR) loss within 1 dB of the bit error rate of SVD beamforming with least squares channel estimates. The low-complexity method significantly outperforms maximum ratio combining diversity and Alamouti coding. We also show that the computational cost of the proposed PIA-based method is less than the one of using the Golub–Reinsch algorithm for obtaining the SVD. The number of computations of the low-complexity version is an order of magnitude smaller than with Golub–Reinsch. This difference grows further with antenna array size.

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ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2020.v25i2.2691 ◽

2020 ◽

Vol 25 (2) ◽

pp. 86-97

Author(s):

Sandy Suryo Prayogo ◽

Tubagus Maulana Kusuma

Keyword(s):

Deep Learning ◽

Bit Error Rate ◽

Error Rate ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Signal To Noise ◽

Structural Similarity Index ◽

Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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Low-complexity and Low-overhead Receiver for OTFS via Large-scale Antenna Array

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2021.3072667 ◽

2021 ◽

pp. 1-1

Author(s):

Yaru Shan ◽

Fanggang Wang

Keyword(s):

Antenna Array ◽

Large Scale ◽

Low Complexity

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Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01904-8 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Rong Ran ◽

Hayoung Oh

Keyword(s):

Large Scale ◽

Mimo Systems ◽

Minimum Mean Square Error ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Finite Alphabet ◽

Multiuser Detectors ◽

Input Multiple Output ◽

Error Detector ◽

Significant Performance

AbstractSparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

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Thyroidectomy for thyroid cancer via transareola single-site endoscopic approach: results of a case-match study with large-scale population

Surgical Endoscopy ◽

10.1007/s00464-021-08424-y ◽

2021 ◽

Author(s):

Juyong Liang ◽

Ling Zhan ◽

Ming Xuan ◽

Qiwu Zhao ◽

Lingxie Chen ◽

...

Keyword(s):

Thyroid Cancer ◽

Large Scale ◽

Endoscopic Approach ◽

Single Site ◽

Scale Population

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Computing 3D Phase-Type Holograms Based on Deep Learning Method

Photonics ◽

10.3390/photonics8070280 ◽

2021 ◽

Vol 8 (7) ◽

pp. 280

Author(s):

Huadong Zheng ◽

Jianbin Hu ◽

Chaojun Zhou ◽

Xiaoxi Wang

Keyword(s):

Deep Learning ◽

Signal To Noise Ratio ◽

Contrast Ratio ◽

Angular Spectrum ◽

Structural Similarity ◽

Computational Time ◽

Experimental Investigations ◽

Training Strategy ◽

Holographic Display ◽

Phase Type

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

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