G. Han, Yue Peng, Huan Liu, Jiuren Zhou, Zhengdong Luo, Bing Chen, R. Cheng, C. Jin, W. Xiao, Fenning Liu, Jiayi Zhao, Shulong Wang, Xiao Yu, Y. Liu, Yue Hao
Recently, transistor scaling is approaching its physical limit, hindering the further development of the computing capability. In the post-Moore era, emerging logic and storage devices have been the fundamental hardware for expanding the capability of intelligent computing. In this article, the recent progress of ferroelectric devices for intelligent computing is reviewed. The material properties and electrical characteristics of ferroelectric devices are elucidated, followed by a discussion of novel ferroelectric materials and devices that can be used for intelligent computing. Ferroelectric capacitors, transistors, and tunneling junction devices used for low-power logic, high-performance memory, and neuromorphic applications are comprehensively reviewed and compared. In addition, to provide useful guidance for developing high-performance ferroelectric-based intelligent computing systems, the key challenges for realizing ultrascaled ferroelectric devices for high-efficiency computing are discussed.
{"title":"Ferroelectric Devices for Intelligent Computing","authors":"G. Han, Yue Peng, Huan Liu, Jiuren Zhou, Zhengdong Luo, Bing Chen, R. Cheng, C. Jin, W. Xiao, Fenning Liu, Jiayi Zhao, Shulong Wang, Xiao Yu, Y. Liu, Yue Hao","doi":"10.34133/2022/9859508","DOIUrl":"https://doi.org/10.34133/2022/9859508","url":null,"abstract":"Recently, transistor scaling is approaching its physical limit, hindering the further development of the computing capability. In the post-Moore era, emerging logic and storage devices have been the fundamental hardware for expanding the capability of intelligent computing. In this article, the recent progress of ferroelectric devices for intelligent computing is reviewed. The material properties and electrical characteristics of ferroelectric devices are elucidated, followed by a discussion of novel ferroelectric materials and devices that can be used for intelligent computing. Ferroelectric capacitors, transistors, and tunneling junction devices used for low-power logic, high-performance memory, and neuromorphic applications are comprehensively reviewed and compared. In addition, to provide useful guidance for developing high-performance ferroelectric-based intelligent computing systems, the key challenges for realizing ultrascaled ferroelectric devices for high-efficiency computing are discussed.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85937725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, cell imaging is frequently performed in a high-content manner. Consequently, the manual interpretation of cell images becomes extremely inefficient. Fortunately, with the advancement of deep-learning technologies, an increasing number of deep learning-based algorithms have been developed to automate and streamline this process. In this study, we present an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification. Despite the impressive score, the challenge still remains: most of the algorithms only verify the performance in their customized settings, causing a performance gap between academic research and practical application. Thus, we also review more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.
{"title":"Deep Learning in Cell Image Analysis","authors":"Junde Xu, Donghao Zhou, Danruo Deng, Jingpeng Li, Cheng Chen, Xiangyun Liao, Guangyong Chen, P. Heng","doi":"10.34133/2022/9861263","DOIUrl":"https://doi.org/10.34133/2022/9861263","url":null,"abstract":"Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, cell imaging is frequently performed in a high-content manner. Consequently, the manual interpretation of cell images becomes extremely inefficient. Fortunately, with the advancement of deep-learning technologies, an increasing number of deep learning-based algorithms have been developed to automate and streamline this process. In this study, we present an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification. Despite the impressive score, the challenge still remains: most of the algorithms only verify the performance in their customized settings, causing a performance gap between academic research and practical application. Thus, we also review more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91115993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the Labeled Network Stack (LNS) based server is a good case to gain orders of magnitude performance improvement compared to servers based on traditional network stacks. However, it is desirable to figure out 1) where the low tail latency and the low entropy of LNS mainly come from, compared with mTCP, a typical user-space network stack in academia, and Linux network stack, the mainstream network stack in industry, and 2) how much LNS can be further optimized. Therefore, we propose a queueing theory-based analytical method defining a bottleneck stage to simplify the quantitative analysis of tail latency. Facilitated by the analytical method, we establish models characterizing the change of processing speed in different stages for an LNS-based server, an mTCP-based server, and a Linux-based server, with bursty traffic as an example. Under such traffic, each network service stage's processing speed is obtained by non-intrusive basic tests to identify the slowest stage as the bottleneck according to traffic and system characteristics. Our models reveal that the full-datapath prioritized processing and the full-path zero-copy are primary sources of the low tail latency and the low entropy of the LNS-based server, with 0.8%-24.4% error for the 99th percentile latency. In addition, the model of the LNS-based server can give the best number of worker threads querying a database, improving 2.1×-3.5× in concurrency.
{"title":"Queueing-Theoretic Performance Analysis of a Low-Entropy Labeled Network Stack","authors":"Hongrui Guo, Wenli Zhang, Zishu Yu, Mingyu Chen","doi":"10.34133/2022/9863054","DOIUrl":"https://doi.org/10.34133/2022/9863054","url":null,"abstract":"Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the Labeled Network Stack (LNS) based server is a good case to gain orders of magnitude performance improvement compared to servers based on traditional network stacks. However, it is desirable to figure out 1) where the low tail latency and the low entropy of LNS mainly come from, compared with mTCP, a typical user-space network stack in academia, and Linux network stack, the mainstream network stack in industry, and 2) how much LNS can be further optimized. Therefore, we propose a queueing theory-based analytical method defining a bottleneck stage to simplify the quantitative analysis of tail latency. Facilitated by the analytical method, we establish models characterizing the change of processing speed in different stages for an LNS-based server, an mTCP-based server, and a Linux-based server, with bursty traffic as an example. Under such traffic, each network service stage's processing speed is obtained by non-intrusive basic tests to identify the slowest stage as the bottleneck according to traffic and system characteristics. Our models reveal that the full-datapath prioritized processing and the full-path zero-copy are primary sources of the low tail latency and the low entropy of the LNS-based server, with 0.8%-24.4% error for the 99th percentile latency. In addition, the model of the LNS-based server can give the best number of worker threads querying a database, improving 2.1×-3.5× in concurrency.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86815826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As machine learning (ML) becomes the prominent technology for many emerging problems, dedicated ML computers are being developed at a variety of scales, from clouds to edge devices. However, the heterogeneous, parallel, and multilayer characteristics of conventional ML computers concentrate the cost of development on the software stack, namely, ML frameworks, compute libraries, and compilers, which limits the productivity of new ML computers. Fractal von Neumann architecture (FvNA) is proposed to address the programming productivity issue for ML computers. FvNA is scale-invariant to program, thus making the development of a family of scaled ML computers as easy as a single node. In this study, we generalize FvNA to the field of general-purpose parallel computing. We model FvNA as an abstract parallel computer, referred to as the fractal parallel machine (FPM), to demonstrate several representative general-purpose tasks that are efficiently programmable. FPM limits the entropy of programming by applying constraints on the control pattern of the parallel computing systems. However, FPM is still general-purpose and cost-optimal. We settle some preliminary results showing that FPM is as powerful as many fundamental parallel computing models such as BSP and alternating Turing machine. Therefore, FvNA is also generally applicable to various fields other than ML.
{"title":"Fractal Parallel Computing","authors":"Yongwei Zhao, Yunji Chen, Zhiwei Xu","doi":"10.34133/2022/9797623","DOIUrl":"https://doi.org/10.34133/2022/9797623","url":null,"abstract":"As machine learning (ML) becomes the prominent technology for many emerging problems, dedicated ML computers are being developed at a variety of scales, from clouds to edge devices. However, the heterogeneous, parallel, and multilayer characteristics of conventional ML computers concentrate the cost of development on the software stack, namely, ML frameworks, compute libraries, and compilers, which limits the productivity of new ML computers. Fractal von Neumann architecture (FvNA) is proposed to address the programming productivity issue for ML computers. FvNA is scale-invariant to program, thus making the development of a family of scaled ML computers as easy as a single node. In this study, we generalize FvNA to the field of general-purpose parallel computing. We model FvNA as an abstract parallel computer, referred to as the fractal parallel machine (FPM), to demonstrate several representative general-purpose tasks that are efficiently programmable. FPM limits the entropy of programming by applying constraints on the control pattern of the parallel computing systems. However, FPM is still general-purpose and cost-optimal. We settle some preliminary results showing that FPM is as powerful as many fundamental parallel computing models such as BSP and alternating Turing machine. Therefore, FvNA is also generally applicable to various fields other than ML.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88888975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chuanqi Zhang, Sa Wang, Zihao Yu, Huizhe Wang, Yinan Xu, Luoshan Cai, Dan Tang, Ninghui Sun, Yungang Bao
Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource utilization often could cause complicated resource contention between colocated cloud applications on different resources, spanning from the underlying hardware to the software stack, leading to unexpected performance degradation. The low-entropy cloud proposes a new software-hardware codesigned technology stack to holistically curb performance interference from the bottom up and obtain both high resource efficiency and high quality of application performance. In this paper, we introduce a new computer architecture for the low-entropy cloud stack, called labeled von Neumann architecture (LvNA), which incorporates a set of label-powered control mechanisms to enable shared components and resources on chip to differentiate, isolate, and prioritize user-defined application requests when competing for hardware resource. With the power of these mechanisms, LvNA was able to protect the performance of certain applications, such as latency-critical applications, from disorderly resource contention while improving resource utilization. We further build and tapeout Beihai, a 1.2 GHz 8-core RISC-V processor based on the LvNA architecture. The evaluation results show that Beihai could drastically reduce the performance degradation caused by memory bandwidth contention from 82.8% to 0.4%. When improving the CPU utilization over 70%, Beihai could reduce the 99th tail latency of Redis from 115 ms to 18.1 ms. Furthermore, Beihai can realize hardware virtualization, which boots up two unmodified virtual machines concurrently without the intervention of any software hypervisor.
{"title":"A Labeled Architecture for Low-Entropy Clouds: Theory, Practice, and Lessons","authors":"Chuanqi Zhang, Sa Wang, Zihao Yu, Huizhe Wang, Yinan Xu, Luoshan Cai, Dan Tang, Ninghui Sun, Yungang Bao","doi":"10.34133/2022/9795476","DOIUrl":"https://doi.org/10.34133/2022/9795476","url":null,"abstract":"Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource utilization often could cause complicated resource contention between colocated cloud applications on different resources, spanning from the underlying hardware to the software stack, leading to unexpected performance degradation. The low-entropy cloud proposes a new software-hardware codesigned technology stack to holistically curb performance interference from the bottom up and obtain both high resource efficiency and high quality of application performance. In this paper, we introduce a new computer architecture for the low-entropy cloud stack, called labeled von Neumann architecture (LvNA), which incorporates a set of label-powered control mechanisms to enable shared components and resources on chip to differentiate, isolate, and prioritize user-defined application requests when competing for hardware resource. With the power of these mechanisms, LvNA was able to protect the performance of certain applications, such as latency-critical applications, from disorderly resource contention while improving resource utilization. We further build and tapeout Beihai, a 1.2 GHz 8-core RISC-V processor based on the LvNA architecture. The evaluation results show that Beihai could drastically reduce the performance degradation caused by memory bandwidth contention from 82.8% to 0.4%. When improving the CPU utilization over 70%, Beihai could reduce the 99th tail latency of Redis from 115 ms to 18.1 ms. Furthermore, Beihai can realize hardware virtualization, which boots up two unmodified virtual machines concurrently without the intervention of any software hypervisor.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75302895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Programming robot swarms is hard because system requirements are formulated at the swarm level (i.e., globally) while control rules need to be coded at the individual robot level (i.e., locally). Connecting global to local levels or vice versa through mathematical modeling to predict the system behavior is generally assumed to be the grand challenge of swarm robotics. We propose to approach this problem by programming directly at the swarm level. Key to this solution is the use of heterogeneous swarms that combine appropriate subsets of agents whose hard-coded agent behaviors have known global effects. Our novel global-to-local design methodology allows to compose heterogeneous swarms for the example application of self-organized task allocation. We define a large but finite number of local agent controllers and focus on the global dynamics of behaviorally heterogeneous swarms. The user inputs the desired global task allocation for the swarm as a stationary probability distribution of agents allocated over tasks. We provide a generic method that implements the desired swarm behavior by mathematically deriving appropriate compositions of heterogeneous swarms that approximate these global user requirements. We investigate our methodology over several task allocation scenarios and validate our results with multiagent simulations. The proposed global-to-local design methodology is not limited to task allocation problems and can pave the way to formal approaches to design other swarm behaviors.
{"title":"Global-to-Local Design for Self-Organized Task Allocation in Swarms","authors":"Gabriele Valentini, Heiko Hamann, M. Dorigo","doi":"10.34133/2022/9761694","DOIUrl":"https://doi.org/10.34133/2022/9761694","url":null,"abstract":"Programming robot swarms is hard because system requirements are formulated at the swarm level (i.e., globally) while control rules need to be coded at the individual robot level (i.e., locally). Connecting global to local levels or vice versa through mathematical modeling to predict the system behavior is generally assumed to be the grand challenge of swarm robotics. We propose to approach this problem by programming directly at the swarm level. Key to this solution is the use of heterogeneous swarms that combine appropriate subsets of agents whose hard-coded agent behaviors have known global effects. Our novel global-to-local design methodology allows to compose heterogeneous swarms for the example application of self-organized task allocation. We define a large but finite number of local agent controllers and focus on the global dynamics of behaviorally heterogeneous swarms. The user inputs the desired global task allocation for the swarm as a stationary probability distribution of agents allocated over tasks. We provide a generic method that implements the desired swarm behavior by mathematically deriving appropriate compositions of heterogeneous swarms that approximate these global user requirements. We investigate our methodology over several task allocation scenarios and validate our results with multiagent simulations. The proposed global-to-local design methodology is not limited to task allocation problems and can pave the way to formal approaches to design other swarm behaviors.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81969018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the past three years, we have witnessed the emergence of a new class of artificial intelligence systems–—so-called foundation models, which are characterised by very large machine learning models (with tens or hundreds of billions of parameters) trained using extremely large and broad data sets. Foundation models, it is argued, have competence in a broad range of tasks, which can be specialised for specific applications. Large language models, of which GPT-3 is perhaps the best known, are the most prominent example of current foundation models. While foundation models have demonstrated impressive capabilities in certain tasks—natural language generation being the most obvious example—I argue that because they are inherently disembodied, and they are limited with respect to what they have learned and what they can do. Foundation models are likely to be very useful in many applications: but they are not the end of the road in artificial intelligence.
{"title":"What Is Missing from Contemporary AI? The World","authors":"","doi":"10.34133/2022/9847630","DOIUrl":"https://doi.org/10.34133/2022/9847630","url":null,"abstract":"In the past three years, we have witnessed the emergence of a new class of artificial intelligence systems–—so-called foundation models, which are characterised by very large machine learning models (with tens or hundreds of billions of parameters) trained using extremely large and broad data sets. Foundation models, it is argued, have competence in a broad range of tasks, which can be specialised for specific applications. Large language models, of which GPT-3 is perhaps the best known, are the most prominent example of current foundation models. While foundation models have demonstrated impressive capabilities in certain tasks—natural language generation being the most obvious example—I argue that because they are inherently disembodied, and they are limited with respect to what they have learned and what they can do. Foundation models are likely to be very useful in many applications: but they are not the end of the road in artificial intelligence.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76133574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Intelligent Computing – A Flagship Journal towards the New Frontier of Computing and Intelligence","authors":"Shiqiang Zhu, Ninghui Sun","doi":"10.34133/2022/9801324","DOIUrl":"https://doi.org/10.34133/2022/9801324","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83111697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Imaging through scattering media is a longstanding issue in a wide range of applications, including biomedicine, security, and astronomy. Speckle-correlation imaging is promising for noninvasively seeing through scattering media by assuming shift invariance of the scattering process called the memory effect. However, the memory effect is known to be severely limited when the medium is thick. Under such a scattering condition, speckle-correlation imaging is not practical because the correlation of the speckle decays, reducing the field of view. To address this problem, we present a method for expanding the field of view of single-shot speckle-correlation imaging by extrapolating the correlation with a limited memory effect. We derive the imaging model under this scattering condition and its inversion for reconstructing the object. Our method simultaneously estimates both the object and the decay of the speckle correlation based on the gradient descent method. We numerically and experimentally demonstrate the proposed method by reconstructing point sources behind scattering media with a limited memory effect. In the demonstrations, our speckle-correlation imaging method with a minimal lensless optical setup realized a larger field of view compared with the conventional one. This study will make techniques for imaging through scattering media more practical in various fields.
{"title":"Extrapolated Speckle-Correlation Imaging","authors":"Yuto Endo, J. Tanida, M. Naruse, R. Horisaki","doi":"10.34133/2022/9787098","DOIUrl":"https://doi.org/10.34133/2022/9787098","url":null,"abstract":"Imaging through scattering media is a longstanding issue in a wide range of applications, including biomedicine, security, and astronomy. Speckle-correlation imaging is promising for noninvasively seeing through scattering media by assuming shift invariance of the scattering process called the memory effect. However, the memory effect is known to be severely limited when the medium is thick. Under such a scattering condition, speckle-correlation imaging is not practical because the correlation of the speckle decays, reducing the field of view. To address this problem, we present a method for expanding the field of view of single-shot speckle-correlation imaging by extrapolating the correlation with a limited memory effect. We derive the imaging model under this scattering condition and its inversion for reconstructing the object. Our method simultaneously estimates both the object and the decay of the speckle correlation based on the gradient descent method. We numerically and experimentally demonstrate the proposed method by reconstructing point sources behind scattering media with a limited memory effect. In the demonstrations, our speckle-correlation imaging method with a minimal lensless optical setup realized a larger field of view compared with the conventional one. This study will make techniques for imaging through scattering media more practical in various fields.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83141965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reconstruction of a stable and reliable solution from noisy and incomplete Fourier intensity data is a challenging problem for iterative phase retrieval algorithms. The typical methodology employed in the coherent X-ray imaging (CXI) literature involves thousands of iterations of well-known phase retrieval algorithms, e.g., hybrid input-output (HIO) or relaxed averaged alternating reflections (RAAR), that are concluded with a smaller number of error reduction (ER) iterations. Since the single run of this methodology may not provide a reliable solution, hundreds of trial solutions are first obtained by initializing the phase retrieval algorithm with independent random guesses. The resulting trial solutions are then averaged with appropriate phase adjustment, and resolution of the averaged reconstruction is assessed by plotting the phase retrieval transfer function (PRTF). In this work, we examine this commonly used RAAR-ER methodology from the perspective of the complexity parameter introduced by us in recent years. It is observed that the single run of the RAAR-ER algorithm provides a solution with undesirable grainy artifacts that persist to some extent even after averaging the multiple trial solutions. The grainy features are spurious in the sense that they are smaller in size compared to the resolution predicted by the PRTF curve. This inconsistency can be addressed by a novel methodology that we refer to as complexity-guided RAAR (CG-RAAR). The methodology is demonstrated with simulations and experimental data sets from the CXIDB database. In addition to providing consistent solution, CG-RAAR is also observed to require reduced number of independent trials for averaging.
{"title":"Robust Phase Retrieval with Complexity-Guidance for Coherent X-Ray Imaging","authors":"Mansi Butola, Sunaina Rajora, K. Khare","doi":"10.34133/2022/9819716","DOIUrl":"https://doi.org/10.34133/2022/9819716","url":null,"abstract":"Reconstruction of a stable and reliable solution from noisy and incomplete Fourier intensity data is a challenging problem for iterative phase retrieval algorithms. The typical methodology employed in the coherent X-ray imaging (CXI) literature involves thousands of iterations of well-known phase retrieval algorithms, e.g., hybrid input-output (HIO) or relaxed averaged alternating reflections (RAAR), that are concluded with a smaller number of error reduction (ER) iterations. Since the single run of this methodology may not provide a reliable solution, hundreds of trial solutions are first obtained by initializing the phase retrieval algorithm with independent random guesses. The resulting trial solutions are then averaged with appropriate phase adjustment, and resolution of the averaged reconstruction is assessed by plotting the phase retrieval transfer function (PRTF). In this work, we examine this commonly used RAAR-ER methodology from the perspective of the complexity parameter introduced by us in recent years. It is observed that the single run of the RAAR-ER algorithm provides a solution with undesirable grainy artifacts that persist to some extent even after averaging the multiple trial solutions. The grainy features are spurious in the sense that they are smaller in size compared to the resolution predicted by the PRTF curve. This inconsistency can be addressed by a novel methodology that we refer to as complexity-guided RAAR (CG-RAAR). The methodology is demonstrated with simulations and experimental data sets from the CXIDB database. In addition to providing consistent solution, CG-RAAR is also observed to require reduced number of independent trials for averaging.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77260213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：