2018 IEEE Workshop on Complexity in Engineering (COMPENG)最新文献

{"title":"Approximate Entropy of Spiking Series of a Neuronal Network in Either Subcritical or Critical State","authors":"L. Ermini, L. Mesin, P. Massobrio","doi":"10.1109/CompEng.2018.8536242","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536242","url":null,"abstract":"Spontaneous activity of neural networks depends on their stage of development. Computational performances of a network increase when the maturation leads to a self-organized criticality. Thus, an increasing complexity in the behavior of the network is expected when it enters in this developmental stage, called critical state. We tested this hypothesis investigating with a Micro-Electrodes Array of 60 electrodes a neuronal culture that during maturation exhibited first a subcritical and then a critical state. We found that in the critical state the local complexity (measured in terms of Approximate Entropy) was larger than in subcritical conditions ($mathbf{mean}pm mathbf{std}$, ApEn about $mathbf{1.03}+mathbf{0.10},mathbf{0.77}+mathbf{0.18}$ in critical and sub-critical states, respectively; differences statistically significant), but only if the embedding dimension is at least 3 and the tolerance is fixed (we considered it equal to 1 ms, which is close to the characteristic time of neural communications).","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131993830","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":"A Review on the Role of Water Diffusion Modeling in Magnetic Resonance Imaging of Prostate Cancer","authors":"A. Barucci, D. Farnesi, F. Ratto, R. Pini, R. Carpi, M. Esposito, M. Olmastroni, G. Zatelli","doi":"10.1109/CompEng.2018.8536248","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536248","url":null,"abstract":"Prostate Cancer (PCa) is among of the tumors with highest incidence in men. Diagnosis of PCa is usually based on different techniques as digital rectal examination, prostate-specific antigen (PSA), transrectal ultrasonography, Magnetic Resonance Imaging (MRI) and transrectal biopsy. Thanks to its intrinsic ability to obtain anatomical, functional and molecular information, MRI is one of the most spread and powerful tools to diagnosis and staging of PCa. In particular, Diffusion-Weighted Imaging (DWI) MRI technique allows to obtain images with contrast depending on the microscopic mobility of water molecules in tissue, probing the microscopic structure. Moreover, from DWI images is possible to quantify the Apparent Diffusion Coefficient of water (ADC) using different diffusion models, as “Mono-exponential”, “Bi-exponential”, “Kurtosis”, “Gamma distribution” and “Stretched exponential”, all of them based on different assumptions on the water mobility in the tissue microenvironment. Despite that the diagnostic and prognostic power of some of these models be known, a clear connection with the physical, biological and physiological underlying features is lacking. In this work we will review all these models, showing results for patients suffering PCa, for which we have a complete clinical picture thanks to transrectal biopsy and other examinations.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134477284","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":"Fractal-Radiomics as Complexity Analysis of CT and MRI Cancer Images","authors":"A. Barucci, D. Farnesi, F. Ratto, S. Pelli, R. Pini, R. Carpi, M. Esposito, M. Olmastroni, C. Romei, A. Taliani, M. Materassi","doi":"10.1109/CompEng.2018.8536249","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536249","url":null,"abstract":"Cancer is the second leading cause of death globally. Early diagnosis can allow intervention to reduce mortality but due to cancer complex structure and spatial heterogeneity among different tumors and within each lesion, it is difficult to differentiate it from healthy tissue using conventional imaging techniques. Quantification of its complexity can be a prognostic tool for fighting this disease. In recent years, clinical imaging allows this quantification thanks to Radiomics, which extracts features from images. In this study, Fractal Dimension (FD) and Lacunarity $(pmb{L})$ in computed tomography (CT) and magnetic resonance (MR) images for different kinds of cancer were examined using box counting method. Our aim is to highlight the potentiality of features based on fractal analysis, in order to obtain new indicators able to detect tumor spatial complexity and heterogeneity. The results indicated that both FD and $pmb{L}$ show problems linked to the lack of connection between complexity estimated with Radiomics and the underlying biological model.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375972","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":"Using RSCAD's Simplified Inverter Components to Model Smart Inverters in Power Systems","authors":"T. Ustun, J. Hashimoto, K. Otani","doi":"10.1109/CompEng.2018.8536238","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536238","url":null,"abstract":"There is growing interest in Smart Inverters (SIs) thanks to their capabilities of providing auxiliary support. Power companies are interested in deploying them in their networks to get necessary frequency and voltage support at times of need. However, these inverters dynamically exchange real and reactive power with the grid and try to change operating point of the system. This dynamic behavior at the distribution level of the power systems may create unprecedented problems. In order to test their impact on the network, hardware-in-the-Ioop (HIL) testing is preferred. HIL tests give higher fidelity than simulation-only studies and can model real power networks to study a particular phenomenon. With a combination of real SI hardware and power system modeled in software, different operating modes and their impact on the system can be investigated thoroughly. It is a real challenge to model several SIs in a distribution network as they require small time step modeling which limits computing capacity of real-time simulation platforms such as RTDS. In order to circumvent this issue, simplified inverter models in RSCAD are utilized to model SI functions such as Volt-Var or Power Factor control. With this approach, individual switches within an inverter are not modeled and phenomena that are resulting from rapid switching, such as harmonics, are not taken into account. For studies that focus on the power flow control or voltage support, this trade off is acceptable as many SIs can be easily implemented within a network.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115132286","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":"Exposing Cancer's Complexity Using Radiomics in Clinical Imaging An Investigation on the Role of Histogram Analysis as Imaging Biomarker to Unravel Intra-Tumour Heterogeneity","authors":"A. Barucci, D. Farnesi, F. Ratto, R. Pini, R. Carpi, M. Olmastroni, M. Materassi, C. Romei, A. Taliani","doi":"10.1109/CompEng.2018.8536244","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536244","url":null,"abstract":"Thanks to the most advanced investigation techniques, cancer is showing to be something more complex than we ever imagined. Genomic pattern, epigenetic modifications, environmental and life-style influences leads to subjective expression of the disease. In addition, cancer can be extremely heterogeneous intrinsically, and does not stand still but changes over time. These hallmarks can explain how cancer adapts to therapies, evolving to something than can be totally different from the beginning of the disease. It's an expression of Darwin evolution. Spatial heterogeneity can be found among different tumors and within each lesion, which manifests at genomic, phenotypic, and physiologic levels. Today we know that heterogeneity is a hallmark of malignant tumors. Usually intratumor heterogeneity tends to increase as tumors grow and may increase or decrease following the response to the therapy. This means that tumor heterogeneity must be explored as prognostic tool, but how do we measure this heterogeneity? Clinical imaging allows to quantify this heterogeneity thanks to Radiomics, which extracts quantitative features from images (especially from computed tomography [CT], magnetic resonance [MR], and positron emission tomography [PET] images). The link of these imaging parameters to different phenotypes or genotypes enables the mapping of biologic heterogeneity of tumors, from which inference on gene expression, signaling pathway activity, and tumor microenvironment features can be obtained. These features have the potentiality to become a powerful tool to unravel tumor, providing quantitative information that allows a better phenotypization. In this work we want to show how a subset of radiomic features connected to histogram analysis, in particular skewness, kurtosis and Shannon entropy, evaluated in images of patients with different kinds of cancer, show diagnostic power to differentiate healthy from ill tissues. We will conclude introducing problems linked to the lack of connection between the complexity estimated with radiomics and the underlying biological model.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122128659","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":"Progress in Modeling Railway Hunting Behaviors by Means of Chaotic Equations","authors":"E. Costamagna, E. D. Gialleonardo","doi":"10.1109/CompEng.2018.8536231","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536231","url":null,"abstract":"This work presents some further achievements in modeling the hunting behavior of railway vehicles starting from experience in mimicking error processes in radio channels and from a recent work on hunting lateral accelerations, in which a first attempt to use similar chaotic equations has been recently proposed. Model sequences are modeled as weighted sums of chaotic functions, whose statistical features are matched to target sequences provided by experience or reliable mechanical multi-body procedures. Different examples are shown, various comments are presented, and some future work is outlined.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678714","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":"Torus Breakdown in a Uni Junction Memristor","authors":"J. Ginoux, R. Meucci, S. Euzzor, A. D. Garbo","doi":"10.1142/S0218127418501286","DOIUrl":"https://doi.org/10.1142/S0218127418501286","url":null,"abstract":"Experimental study of a uni junction transistor (UJT) has enabled to show that this electronic component has the same features as the so-called “memristor”. So, we have used the memristor's direct current (DC) current-voltage characteristic for modeling the UJT's DC current-voltage characteristic. This led us to confirm on the one hand, that the UJT is a memristor and, on the other hand to propose a new four-dimensional autonomous dynamical system allowing to describe experimentally observed phenomena such as the transition from a limit cycle to torus breakdown.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"4 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123632125","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":"Simulation and Validation of a SpaceWire On-Board Data-Handling Network for the PLATO Mission","authors":"Matteo Rotundo, A. Leoni, L. Serafini, C. D. V. Blanco, Daniele Davalle, D. Vangelista, M. Focardi, R. Cosentino, S. Pezzuto, G. Giusy, D. Biondi, L. Fanucci","doi":"10.1109/CompEng.2018.8536237","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536237","url":null,"abstract":"PLAnetary Transits and Oscillations of stars (PLATO) is a medium-class mission belonging to the European Space Agency (ESA) Cosmic Vision programme. The mission payload is composed of 26 telescopes and cameras which will observe uninterruptedly stars like our Sun in order to identify new exoplanets candidates down to the range of Earth analogues. The images from the cameras are generated by several distributed Digital Processing Units (DPUs) connected together in a SpaceWire network and producing a large quantity of data to be processed by the Instrument Control Unit. The paper presents the results of the analyses and simulations performed using the Simulator for HI-Speed Networks (SHINE) with the objective to assess the on-board data network performance.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131065158","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":"The PLATO Payload and Data Processing System Space Wire Network","authors":"M. Focardi, R. Cosentino, S. Pezzuto, D. Biondi, G. Giusi, L. Serafini, C. D. V. Blanco, D. Vangelista, Matteo Rotundo, L. Fanucci, Daniele Davalle, P. D. Team","doi":"10.1109/CompEng.2018.8536225","DOIUrl":"https://doi.org/10.1109/CompEng.2018.8536225","url":null,"abstract":"PLATO [1] has been selected and adopted by ESA as the third medium-class Mission (M3) of the Cosmic Vision Program, to be launched in 2026 with a Soyuz-Fregat rocket from the French Guiana. Its Payload (P/L) is based on a suite of 26 telescopes and cameras in order to discover and characterise, thanks to ultra-high accurate photometry and the transits method, new exoplanets down to the range of Earth analogues. Each camera is composed of 4 CCDs working in full-frame or frame-transfer mode. 24 cameras out of 26 host 4510 by 4510 pixels CCDs, operated in full-frame mode with a pixel depth of 16 bits and a cadence of 25 s. Given the huge data volume to be managed, the PLATO P/L relies on an efficient Data Processing System (DPS) whose Units perform images windowing, cropping and compression. Each camera and DPS Unit is connected to a fast SpaceWire (SpW) network running at 100 MHz and interfaced to the satellite On-Board Computer (OBC) by means of an Instrument Control Unit (ICU), performing data collection and compression.","PeriodicalId":194279,"journal":{"name":"2018 IEEE Workshop on Complexity in Engineering (COMPENG)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132676601","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}