49 results on '"Barmpoutis, Panagiotis"'
Search Results
2. Debt crisis, age and value relevance of goodwill: evidence from Greece
- Author
-
Pechlivanidis, Eleftherios, Ginoglou, Dimitrios, and Barmpoutis, Panagiotis
- Published
- 2022
- Full Text
- View/download PDF
3. Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space
- Author
-
Barmpoutis, Panagiotis, Dimitropoulos, Kosmas, Apostolidis, Anestis, and Grammalidis, Nikos
- Published
- 2019
- Full Text
- View/download PDF
4. Overall Survival Time Estimation for Epithelioid Peritoneal Mesothelioma Patients from Whole-Slide Images.
- Author
-
Papadopoulos, Kleanthis Marios, Barmpoutis, Panagiotis, Stathaki, Tania, Kepenekian, Vahan, Dartigues, Peggy, Valmary-Degano, Séverine, Illac-Vauquelin, Claire, Avérous, Gerlinde, Chevallier, Anne, Laverriere, Marie-Hélène, Villeneuve, Laurent, Glehen, Olivier, Isaac, Sylvie, Hommell-Fontaine, Juliette, Ng Kee Kwong, Francois, and Benzerdjeb, Nazim
- Subjects
- *
OVERALL survival , *CANCER patients , *PERITONEAL cancer , *CANCER chemotherapy , *CONVOLUTIONAL neural networks - Abstract
Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning's potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients' survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model's predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Wood species recognition through multidimensional texture analysis
- Author
-
Barmpoutis, Panagiotis, Dimitropoulos, Kosmas, Barboutis, Ioannis, Grammalidis, Nikos, and Lefakis, Panagiotis
- Published
- 2018
- Full Text
- View/download PDF
6. Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma
- Author
-
Dimitropoulos, Kosmas, Barmpoutis, Panagiotis, Koletsa, Triantafyllia, Kostopoulos, Ioannis, and Grammalidis, Nikos
- Published
- 2017
- Full Text
- View/download PDF
7. Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer.
- Author
-
Barmpoutis, Panagiotis, Kastridis, Aristeidis, Stathaki, Tania, Yuan, Jing, Shi, Mengjie, and Grammalidis, Nikos
- Subjects
- *
FIRE risk assessment , *FOREST fires , *FIRE detectors , *FOREST fire prevention & control , *DRONE aircraft , *CAMERAS , *SUBURBS - Abstract
In the current context of climate change and demographic expansion, one of the phenomena that humanity faces are the suburban wildfires. To prevent the occurrence of suburban forest fires, fire risk assessment and early fire detection approaches need to be applied. Forest fire risk mapping depends on various factors and contributes to the identification and monitoring of vulnerable zones where risk factors are most severe. Therefore, watchtowers, sensors, and base stations of autonomous unmanned aerial vehicles need to be placed carefully in order to ensure adequate visibility or battery autonomy. In this study, fire risk assessment of an urban forest was performed and the recently introduced 360-degree data were used for early fire detection. Furthermore, a single-step approach that integrates a multiscale vision transformer was introduced for accurate fire detection. The study area includes the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which is prone to wildfires. For the evaluation of the performance of the proposed workflow, real and synthetic 360-degree images were used. Experimental results demonstrate the great potential of the proposed system, which achieved an F-score for real fire event detection rate equal to 91.6%. This indicates that the proposed method could significantly contribute to the monitoring, protection, and early fire detection of the suburban forest of Thessaloniki. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. A digital pathology workflow for the segmentation and classification of gastric glands: Study of gastric atrophy and intestinal metaplasia cases.
- Author
-
Barmpoutis, Panagiotis, Waddingham, William, Yuan, Jing, Ross, Christopher, Kayhanian, Hamzeh, Stathaki, Tania, Alexander, Daniel C., and Jansen, Marnix
- Subjects
- *
METAPLASIA , *PATHOLOGY , *ATROPHY , *INTESTINES , *DISEASE risk factors , *GASTRIC mucosa - Abstract
Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric atrophy (GA) and gastric intestinal metaplasia (IM) of the mucosa of the stomach have been found to increase the risk of gastric cancer and are considered precancerous lesions. Therefore, the early detection of GA and IM may have a valuable role in histopathological risk assessment. However, GA and IM are difficult to confirm endoscopically and, following the Sydney protocol, their diagnosis depends on the analysis of glandular morphology and on the identification of at least one well-defined goblet cell in a set of hematoxylin and eosin (H&E) -stained biopsy samples. To this end, the precise segmentation and classification of glands from the histological images plays an important role in the diagnostic confirmation of GA and IM. In this paper, we propose a digital pathology end-to-end workflow for gastric gland segmentation and classification for the analysis of gastric tissues. The proposed GAGL-VTNet, initially, extracts both global and local features combining multi-scale feature maps for the segmentation of glands and, subsequently, it adopts a vision transformer that exploits the visual dependences of the segmented glands towards their classification. For the analysis of gastric tissues, segmentation of mucosa is performed through an unsupervised model combining energy minimization and a U-Net model. Then, features of the segmented glands and mucosa are extracted and analyzed. To evaluate the efficiency of the proposed methodology we created the GAGL dataset consisting of 85 WSI, collected from 20 patients. The results demonstrate the existence of significant differences of the extracted features between normal, GA and IM cases. The proposed approach for gland and mucosa segmentation achieves an object dice score equal to 0.908 and 0.967 respectively, while for the classification of glands it achieves an F1 score equal to 0.94 showing great potential for the automated quantification and analysis of gastric biopsies. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
9. Pedestrian Detection Using Integrated Aggregate Channel Features and Multitask Cascaded Convolutional Neural-Network-Based Face Detectors.
- Author
-
Yuan, Jing, Barmpoutis, Panagiotis, and Stathaki, Tania
- Subjects
- *
DETECTORS , *PEDESTRIANS , *CONVOLUTIONAL neural networks - Abstract
Pedestrian detection is a challenging task, mainly owing to the numerous appearances of human bodies. Modern detectors extract representative features via the deep neural network; however, they usually require a large training set and high-performance GPUs. For these cases, we propose a novel human detection approach that integrates a pretrained face detector based on multitask cascaded convolutional neural networks and a traditional pedestrian detector based on aggregate channel features via a score combination module. The proposed detector is a promising approach that can be used to handle pedestrian detection with limited datasets and computational resources. The proposed detector is investigated comprehensively in terms of parameter choices to optimize its performance. The robustness of the proposed detector in terms of the training set, test set, and threshold is observed via tests and cross dataset validations on various pedestrian datasets, including the INRIA, part of the ETHZ, and the Caltech and Citypersons datasets. Experiments have proved that this integrated detector yields a significant increase in recall and a decrease in the log average miss rate compared with sole use of the traditional pedestrian detector. At the same time, the proposed method achieves a comparable performance to FRCNN on the INRIA test set compared with sole use of the Aggregated Channel Features detector. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
10. Can intangible assets predict future performance? A deep learning approach.
- Author
-
Pechlivanidis, Eleftherios, Ginoglou, Dimitrios, and Barmpoutis, Panagiotis
- Subjects
DEEP learning ,INTANGIBLE property ,STANDARD deviations ,INTERNATIONAL Financial Reporting Standards ,FINANCIAL statements ,SUPPORT vector machines - Abstract
Purpose: The aim of this study is to evaluate of the predictive ability of goodwill and other intangible assets on forecasting corporate profitability. Subsequently, this study compares the efficiency of deep learning model to that of other machine learning models such as random forest (RF) and support vector machine (SVM) as well as traditional statistical methods such as the linear regression model. Design/methodology/approach: Studies confirm that goodwill and intangibles are valuable assets that give companies a competitive advantage to increase profitability and shareholders' returns. Thus, by using as sample Greek-listed financial data, this study investigates whether or not the inclusion of goodwill and intangible assets as input variables in this modified deep learning models contribute to the corporate profitability prediction accuracy. Subsequently, this study compares the modified long-short-term model with other machine learning models such as SVMs and RF as well as the traditional panel regression model. Findings: The findings of this paper confirm that goodwill and intangible assets clearly improve the performance of a deep learning corporate profitability prediction model. Furthermore, this study provides evidence that the modified long short-term memory model outperforms other machine learning models such as SVMs and RF , as well as traditional statistical panel regression model, in predicting corporate profitability. Research limitations/implications: Limitation of this study includes the relatively small amount of data available. Furthermore, the aim is to challenge the authors' modified long short-term memory by using listed corporate data of Greece, a code-law country that suffered severely during the recent fiscal crisis. However, this study proposes that future research may apply deep learning corporate profitability models on a bigger pool of data such as STOXX Europe 600 companies. Practical implications: Subsequently, the authors believe that their paper is of interest to different professional groups, such as financial analysts and banks, which the authors' paper can support in their corporate profitability evaluation procedure. Furthermore, as well as shareholders are concerned, this paper could be of benefit in forecasting management's potential to create future returns. Finally, management may incorporate this model in the evaluation process of potential acquisitions of other companies. Originality/value: The contributions of this work can be summarized in the following aspects. This study provides evidence that by including goodwill and other intangible assets in the authors' input portfolio, prediction errors represented by root mean squared error are reduced. A modified long short-term memory model is proposed to predict the numerical value of the profitability (or the profitability ratio) in contrast to other studies which deal with trend predictions, i.e. the binomial output result of positive or negative earnings. Finally, posing an extra challenge to the authors' deep learning model, the authors' used financial statements according to International Financial Reporting Standard data of listed companies in Greece, a code-law country that suffered during the recent fiscal debt crisis, heavily influenced by tax legislation and characterized by its lower investors' protection compared to common-law countries. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.
- Author
-
Barmpoutis, Panagiotis, Di Capite, Matthew, Kayhanian, Hamzeh, Waddingham, William, Alexander, Daniel C., Jansen, Marnix, and Kwong, Francois Ng Kee
- Subjects
- *
TERTIARY structure , *SENSITIVITY & specificity (Statistics) , *LUNG cancer , *CONVOLUTIONAL neural networks , *HISTOLOGY , *BREAST cancer prognosis , *CANCER prognosis - Abstract
Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&E slides implying that the automated approach was able to reproduce the histopathologists' assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245μm2. Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/μm2 and 0.0026/μm2 respectively compared to 0.004/μm2 and 0.001/μm2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&E slides. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
12. Utilization of wood and bark of fast-growing hardwood species in energy production.
- Author
-
KAMPERIDOU, VASILIKI, LYKIDIS, CHARALAMPOS, and BARMPOUTIS, PANAGIOTIS
- Subjects
HARDWOODS ,ENERGY industries ,BIOMASS energy ,AILANTHUS ,PAULOWNIA tomentosa - Abstract
In this research, the calorific value and ash content of wood and bark of some fast-growing hardwood species, such as tree-of-heaven, (Ailanthus altissima (Miller) Swingle), empress tree (Paulownia tomentosa (Thunberg) Steudel), trembling aspen (Populus tremuloides Michaux), oriental plane (Platanus orientalis Linnaeus) and black locust (Robinia pseudoacacia Linnaeus) were investigated in order to comprehend their behaviour during combustion and estimate their utilization potential as solid biofuels (pellets). Beech (Fagus sylvatica Linnaeus) wood was used for comparative reasons. Different ratios of all the studied species in mixture were examined in order to investigate the material ratio that provides a satisfactory calorific value, while parallelly meeting the ash content requirements of the pellet production standard (ISO 17225-2:2014). Black locust bark seems to greatly increase the calorific value of the material. Empress tree wood had the lowest ash content, meeting the requirements of the best class (ENplus A1 -- residential use), while tree-of-heaven and poplar were classified into ENplus B class (third class of residential use). By using the appropriate proportions, all the materials examined could be utilized in pellet production. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
13. CONTRIBUTION AND COMBINATION OF DIFFERENT WOOD SECTIONS IN SPECIES RECOGNITION USING IMAGE TEXTURE ANALYSIS METHODS.
- Author
-
BARMPOUTIS, Panagiotis
- Subjects
- *
IMAGE analysis , *MATERIALS texture , *PATTERN perception , *COMPUTER vision , *IMAGE fusion - Abstract
The recognition of wood species is a laborious process, which is performed by experts, who attempt to distinguish the different species in different wood sections based on their macroscopic and microscopic characteristics. Most of these characteristics can be observed in the transverse or cross section of woods. According to experts, the next most important surface for wood species recognition is the tangential section while significant information can also be obtained from the radial section of woods. Based on the recent advances in the area of computer vision and pattern recognition, most researchers have proposed imagebased approaches attempting to address the problem either in microscopic or macroscopic scale. The main limitation is that in many cases there are some features that are not visible in each wood section. Firstly, we examine the contribution of each section in wood species recognition using two different computer-based texture analysis methods. Furthermore, we compare wood species recognition methods for both grayscale images and colorscale images. Finally, we propose a novel fusion method and we demonstrate that wood species recognition accuracy can be increased by fusing features from different wood sections. For the evaluation of the proposed method, a dataset, namely "WOOD-AUTH", consisting of more than 4272 wood images of twelve common wood species, was used. [ABSTRACT FROM AUTHOR]
- Published
- 2017
14. Classification of Nuclei in Follicular Lyphoma Tissue Sections Using Different Stains and Bayesian Networks.
- Author
-
Dimitropoulos, Kosmas, Barmpoutis, Panagiotis, Koletsa, Triantafyllia, Kostopoulos, Ioannis, and Grammalidis, Nikos
- Published
- 2016
- Full Text
- View/download PDF
15. Farklı tiplerde yüz maskesi ile tedavi edilmiş olan sınıf III olgularda yumuşak doku analizi
- Author
-
Barmpoutis, Panagiotis, Biren, Sibel, and Ortodonti Anabilim Dalı
- Subjects
Diş Hekimliği ,Dentistry ,Tedavi ,Ortodonti - Abstract
ÖZETMigreni olan kişiler migreni olmayan kişilere oranla farklı görsel ve işitsel bilgioluştururlar. Migren atakları arasında kognitif etkilenmeyi gösteren kortikal fonksiyonlardaelektrofizyolojik ve fonksiyonel değişiklikler saptanırken bazı çalışmalarda kortikaldeğişiklikler veya kognitif performansta azalma saptanmamıştır. Çalışmamızda migreninkognitif işlevler üzerine etkisinin olup olmadığı eğer varsa hangi işlevlerin etkilendiğiniaraştırmak amacıyla dikkat, bellek, dil, yürütücü işlevler, görsel ve mekansal işlevlerideğerlendiren nöropsikolojik testler ile görsel olaya ilişkin potansiyeller (OİEP) tetkikindekayıtlanan N2 ve P3 dalga latansları arasındaki ilişkiyi değerlendirmeyi amaçladık.Çalışmaya yaşları 16-45 arasında, migren tanısı alan 32 hasta (26 kadın, 6 erkek).alındı. Kontrol grubu olarak yaş ve eğitim düzeyi uyumlu 20 sağlıklı olgu seçildi. Beckdepresyon envanteri uygulanarak depresyonu olanlar çalışmaya alınmadı. OİEP tetkikindekontrol grubu ile karşılaştırıldığında migren grubunda Fz, Cz ve Pz `den kayıtla N2 (p=0.000)ve P3 (p=0.000) latansları kontrol grubuna oranla uzun bulunmuştur. En uzun N2 ve P3latansı Fz'den kayıtla elde edilmiş olup arkadan öne doğru gelindiğinde latansın uzadığısaptanmıştır. Hastalar migren tiplerine göre incelendiğinde auralı ve aurasız gruplara ait N2ve P3 latans ortalama değerleri açısından istatistiksel anlamlı fark saptanmamıştır (p0.05).Sonuç olarak migrende kognitif işlevler etkilenmektedir. OİEP tetkiki nörokognitiftestlerle kanıtlanan migrendeki kognitif etkilenmeyi destekler bulgular ortaya koymaktadır.OİEP migren için tek başına tanısal test olmamasına karşın özellikle kognitif süreci izlemekiçin önemli, uygulaması kolay, noninvaziv bir tekniktir .Anahtar Kelimeler: Migren, görsel olaya ilişkin potansiyeller, kognitif işlevler SUMMARYVISUAL EVENT RELATED POTENTIALS IN MIGRANEOUS PATIENTSIndividuals who experience migraine process visual and auditory informationdifferently from those without migraine. Functional and electrophysiologic alterations incortical functioning have been found during the migraine interval. However, not all studieshave found such cortical alterations or cognitive performance decrements. In our study, wehave aimed to examine whether migraine is associated with cognitive efficiency, if so, tocorrelate the neuropsychological tests of attention, memory, language, cognitiveefficiency,and visuospatial abilities with the latencies of N2 and P3 recorded on visualevent-related potentials (ERP).Thirty-two patients with the diagnosis of migraine ( 26 female, 6 male) age rangedbetween 16-45 and 20 controls with the similar age and educational state were included in thestudy. Patients with depression that was examined by Beck depression inventory wereexcluded. Migraine group had significantly longer latencies for N2 (p=0.000) and P3(p=0.000) waves recorded by Fz, Cz, and Pz electrodes when compared with the controlgroup. The longest latencies were recorded on Fz and the latencies were observed to be longerfrom back to the front. There was no statistically difference between the patients with andwithout aura (p>0.05)Neuropsychological battery was applied for both of the groups. The scores ofattention, memory, language, cognitive efficiency, and verbal paired associates in themigraine group were significantly lower compared with the control group and there was anegative correlation between the cognitive tests and the latencies of N2 and P3 (p=0.000).There was no statistically difference between the patients with and without aura (p>0.05).In conclusion, cognitive function is effected in migraine. ERP supports the cognitiveimpairment in the migraine that has been observed by the neuropsychological tests. ThoughERP is not a major test for the diagnosis of migraine, it is important for the follow-up of thecognitive abilities, and is a noninvasive technique that can be applied easily.Key words: Migraine, visual event-related potentials, cognitive functions 86
- Published
- 2006
16. Grading of invasive breast carcinoma through Grassmannian VLAD encoding.
- Author
-
Dimitropoulos, Kosmas, Barmpoutis, Panagiotis, Zioga, Christina, Kamas, Athanasios, Patsiaoura, Kalliopi, and Grammalidis, Nikos
- Subjects
- *
BREAST cancer , *CANCER invasiveness , *GRASSMANN manifolds , *LINEAR dynamical systems ,CANCER histopathology - Abstract
In this paper we address the problem of automated grading of invasive breast carcinoma through the encoding of histological images as VLAD (Vector of Locally Aggregated Descriptors) representations on the Grassmann manifold. The proposed method considers each image as a set of multidimensional spatially-evolving signals that can be efficiently modeled through a higher-order linear dynamical systems analysis. Subsequently, each H&E (Hematoxylin and Eosin) stained breast cancer histological image is represented as a cloud of points on the Grassmann manifold, while a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. To evaluate the efficiency of the proposed methodology, two datasets with different characteristics were used. More specifically, we created a new medium-sized dataset consisting of 300 annotated images (collected from 21 patients) of grades 1, 2 and 3, while we also provide experimental results using a large dataset, namely BreaKHis, containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results have shown that the proposed method outperforms a number of state of the art approaches providing average classification rates of 95.8% and 91.38% with our dataset and the BreaKHis dataset, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
17. Higher Order Linear Dynamical Systems for Smoke Detection in Video Surveillance Applications.
- Author
-
Dimitropoulos, Kosmas, Barmpoutis, Panagiotis, and Grammalidis, Nikos
- Subjects
- *
LINEAR dynamical systems , *TEXTURE analysis (Image processing) , *SPATIOTEMPORAL processes , *VIDEO surveillance , *SMOKE - Abstract
In this paper, we consider the problem of multi-dimensional dynamic texture analysis, and we introduce a new higher order linear dynamical system (h-LDS) descriptor. The proposed h-LDS descriptor is based on the higher order decomposition of the multidimensional image data and enables the analysis of dynamic textures by using information from various image elements. In addition, we propose a methodology for its application to video-based early warning systems that focus on smoke identification. More specifically, the proposed methodology enables the representation of video subsequences as histograms of h-LDS descriptors produced by the smoke candidate image patches in each subsequence. Finally, to further improve the classification accuracy, we propose the combination of multidimensional dynamic texture analysis with the spatiotemporal modeling of smoke by using a particle swarm optimization approach. The ability of the h-LDS to analyze the dynamic texture information is evaluated through a multivariate comparison against the standard LDS descriptor. The experimental results that use two video datasets have shown the great potential of the proposed smoke detection method. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
18. Image tag recommendation based on novel tensor structures and their decompositions.
- Author
-
Barmpoutis, Panagiotis, Kotropoulos, Constantine, and Pliakos, Konstantinos
- Published
- 2015
- Full Text
- View/download PDF
19. INFLUENCE OF STEM DIAMETER AND BARK RATIO OF EVERGREEN HARDWOODS ON THE FUEL CHARACTERISTICS OF THE PRODUCED PELLETS.
- Author
-
BARMPOUTIS, Panagiotis, LYKIDIS, Charalampos, and BARBOUTIS, Ioannis
- Subjects
- *
PLANT stems , *BARK , *HARDWOODS , *WOOD pellets , *ERICAS - Abstract
Forest biomass originating from Mediterranean forest vegetation could be a potential source of renewable energy in the form of pellets, butdue to its diversity there is a need for better understanding and detailed examination of its main fuel characteristics. The aim of this work was the evaluation of the impact that bark percentage and barked stem diameter have on the ash content and heating value of thefollowing evergreen Mediterranean hardwood species: Arbutus unedo, Erica arborea, Quercus coccifera, Quercus ilex and Phillyrea latifolia. For all the above species, the barked diameter, bark thickness, bark percentage as well as the ash content and higher heating value of bark and wood have been determined. In all cases the ash content of bark was higher than that of wood and also higher than the requirement of the related EN standard. Therefore the bark of tested species could be used in the production of pellets provided that its ratio would be in a level corresponding to the maximum allowed ash content values. Taking into account the results of the determinations, the equations and graphs were used in order to calculate the minimum of the stem diameter requirements in order to meet with the ash content restrictions. The effect of stem diameter on the HHV (higher heating value) was also evaluated. Among the tested species, Erica arborea proved to be the most appropriate for the production of pellets showing the highest HHV and the lowest ash content. [ABSTRACT FROM AUTHOR]
- Published
- 2015
20. CORRELATION BETWEEN THE CHANGES OF COLOUR AND MECHANICAL PROPERTIES OF THERMALLY-MODIFIED SCOTS PINE (PINUS SYLVESTRIS L.) WOOD.
- Author
-
KAMPERIDOU, Vasiliki and BARMPOUTIS, Panagiotis
- Subjects
- *
SCOTS pine , *COLOR of wood , *TREE mechanics , *HEAT treatment , *WOOD bending - Abstract
In this study, Scots pine wood (Pinus sylvestris L.) was thermally treated at 200oC, for three different time periods of 4, 6 and 8 hours. The Bending strength (MOR) and Impact Bending strength of treated and untreated pine wood specimens were determined and measurements of wood colour were implemented before and after each thermal treatment, in order to evaluate the colour changes coming from the treatment processes. An attempt was made to correlate this colour change with the change of the mechanical properties of treated wood. The results indicated that MOR decreased as the intensity of the treatment increased, recording decrease percentages of between 0.34-25.9%, compared to untreated wood, while the impact bending strength values of treated specimens marked 0.69-22.34% lower strength. The noteworthy is that a strong and significant relationship was recorded between the colour change and the mechanical properties change. Based on the results of this study, it could be claimed that the mechanical strength values of treated pinewood could be sufficiently estimated by the corresponding colour change values that have been measured. Therefore, the measurement of colour coordinates and the calculation of the total colour change values offer the opportunity to estimate automatically, quite precisely and through a non-destructive way the mechanical properties of wood. [ABSTRACT FROM AUTHOR]
- Published
- 2015
21. Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition.
- Author
-
Barmpoutis, Panagiotis, Dimitropoulos, Kosmas, and Grammalidis, Nikos
- Published
- 2014
22. Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection.
- Author
-
Dimitropoulos, Kosmas, Barmpoutis, Panagiotis, and Grammalidis, Nikos
- Subjects
- *
CAMCORDERS , *FIRE detectors , *MODELS & modelmaking , *IMAGE processing , *IMAGING systems , *IMAGE analysis - Abstract
Every year, a large number of wildfires all over the world burn forested lands, causing adverse ecological, economic, and social impacts. Beyond taking precautionary measures, early warning and immediate response are the only ways to avoid great losses. To this end, in this paper we propose a computer vision approach for fire-flame detection to be used by an early-warning fire monitoring system. Initially, candidate fire regions in a frame are defined using background subtraction and color analysis based on a nonparametric model. Subsequently, the fire behavior is modeled by employing various spatio-temporal features, such as color probability, flickering, spatial, and spatio-temporal energy, while dynamic texture analysis is applied in each candidate region using linear dynamical systems and a bag-of-systems approach. To increase the robustness of the algorithm, the spatio-temporal consistency energy of each candidate fire region is estimated by exploiting prior knowledge about the possible existence of fire in neighboring blocks from the current and previous video frames. As a final step, a two-class support vector machine classifier is used to classify the candidate regions. Experimental results have shown that the proposed method outperforms existing state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
23. Real time video fire detection using spatio-temporal consistency energy.
- Author
-
Barmpoutis, Panagiotis, Dimitropoulos, Kosmas, and Grammalidis, Nikos
- Published
- 2013
- Full Text
- View/download PDF
24. Estimation of extent of trees and biomass infestation of the suburban forest of Thessaloniki (Seich Sou) using UAV imagery and combining R-CNNs and multichannel texture analysis.
- Author
-
Osten, Wolfgang, Nikolaev, Dmitry P., Barmpoutis, Panagiotis, Kamperidou, Vasiliki, and Stathaki, Tania
- Published
- 2020
- Full Text
- View/download PDF
25. Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images.
- Author
-
Osten, Wolfgang, Nikolaev, Dmitry P., Ulku, Irem, Barmpoutis, Panagiotis, Stathaki, Tania, and Akagunduz, Erdem
- Published
- 2020
- Full Text
- View/download PDF
26. A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing.
- Author
-
Barmpoutis, Panagiotis, Papaioannou, Periklis, Dimitropoulos, Kosmas, and Grammalidis, Nikos
- Subjects
- *
OPTICAL remote sensing , *FIRE detectors , *FOREST fires , *FIRE alarms , *NATURAL disasters - Abstract
The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
27. Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures.
- Author
-
Barmpoutis, Panagiotis, Stathaki, Tania, Dimitropoulos, Kosmas, and Grammalidis, Nikos
- Subjects
- *
FIRE detectors , *CONVOLUTIONAL neural networks , *REMOTE sensing , *FOREST fires , *DETECTORS , *DRONE aircraft - Abstract
The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the "Fire detection 360-degree dataset", consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
28. Salient Object Detection Combining a Self-Attention Module and a Feature Pyramid Network.
- Author
-
Ren, Guangyu, Dai, Tianhong, Barmpoutis, Panagiotis, and Stathaki, Tania
- Subjects
COMPUTER vision ,MARKOV random fields ,FORECASTING ,PYRAMIDS - Abstract
Salient object detection has achieved great improvements by using the Fully Convolutional Networks (FCNs). However, the FCN-based U-shape architecture may cause dilution problems in the high-level semantic information during the up-sample operations in the top-down pathway. Thus, it can weaken the ability of salient object localization and produce degraded boundaries. To this end, in order to overcome this limitation, we propose a novel pyramid self-attention module (PSAM) and the adoption of an independent feature-complementing strategy. In PSAM, self-attention layers are equipped after multi-scale pyramid features to capture richer high-level features and bring larger receptive fields to the model. In addition, a channel-wise attention module is also employed to reduce the redundant features of the FPN and provide refined results. Experimental analysis demonstrates that the proposed PSAM effectively contributes to the whole model so that it outperforms state-of-the-art results over five challenging datasets. Finally, quantitative results show that PSAM generates accurate predictions and integral salient maps, which can provide further help to other computer vision tasks, such as object detection and semantic segmentation. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
29. A novel image reconstruction algorithm based on texture aware multiscale GAN for veneer defects.
- Author
-
Ge, Yilin, Sun, Liping, and Wang, Di
- Subjects
IMAGE reconstruction algorithms ,SIGNAL-to-noise ratio ,IMAGE reconstruction ,GENERATIVE adversarial networks ,EXCAVATING machinery - Abstract
Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Wood species recognition through multidimensional texture analysis.
- Author
-
Barboutis, Ioannis, Lefakis, Panagiotis, Barmpoutis, Panagiotis, Dimitropoulos, Kosmas, and Grammalidis, Nikos
- Subjects
- *
WOOD , *TEXTURE analysis (Image processing) , *LINEAR dynamical systems - Abstract
Wood recognition is a crucial task for wood sciences and industries, since it leads to the identification of the anatomical features and physical properties of wood. Traditionally, the recognition process relies almost exclusively on human experts, who are based on various characteristics of wood, such as color, structure and texture. However, there are numerous types of wood species in the nature that are difficult to be identified even by experienced scientists. Towards this end, in this paper we propose a novel approach for automated wood species recognition through multidimensional texture analysis. By taking advantage of the fact that static wood images contain periodic spatially-evolving characteristics, we introduce a new spatial descriptor considering each wood image as a collection of multidimensional signals. More specifically, the proposed methodology enables the representation of wood images as concatenated histograms of higher order linear dynamical systems produced by vertical and horizontal image patches. The final classification of images, i.e., histogram representations, into wood species, is performed using a Support Vector Machines (SVM) classifier. For the evaluation of the proposed method, a dataset, namely “WOOD-AUTH”, consisting of more than 4200 wood images (from cross, radial and tangential sections of normal wood structure) of twelve common wood species existing in Greek territory, was created. Experimental results presented in this paper show the great potential of the proposed methodology, which, despite a small number of misclassification cases with regards to both anatomically similar and different species, outperforms a number of state of the art approaches, yielding a classification rate of 91.47% in wood cross sections. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
31. Acknowledgment to the Reviewers of Algorithms in 2022.
- Subjects
ALGORITHMS ,SCHOLARLY publishing - Abstract
High-quality academic publishing is built on rigorous peer review. I Algorithms i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
32. Acknowledgment to the Reviewers of Mathematics in 2022.
- Subjects
MATHEMATICS ,SCHOLARLY publishing - Abstract
Thanks to the efforts of our reviewers in 2022, the median time to first decision was 17 days and the median time to publication was 37 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Mathematics i : those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). I Mathematics i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
33. Acknowledgment to Reviewers of Applied Sciences in 2020.
- Subjects
APPLIED sciences - Abstract
The editors would like to express their sincere gratitude to the following reviewers for their precious time and dedication, regardless of whether the papers were finally published: Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that I Applied Sciences i maintains its standards for the high quality of its published papers. [Extracted from the article]
- Published
- 2021
- Full Text
- View/download PDF
34. Frame Difference Method to Detect Fire and Compare the Accuracy and Precision with Vibe Method.
- Author
-
Niharika, D. and Mohana, J.
- Subjects
HOME security measures ,VIDEO processing ,FIRE detectors ,COMPUTATIONAL complexity ,ARTIFICIAL intelligence - Abstract
Aim: In this paper, the main aim is to detect fire using a novel frame difference method and compare it with conventional method. This is based on video processing and computational methods to reduce the computational complexity. Materials and method: The method was performed over a sample size of 20. Same samples were applied for both the control group and experimental group. Improved accuracy detection was obtained using the proposed method. Results: The Accuracy and precision was found (94.03, 64.62) and (86.24,57.19) was obtained for the frame difference method and conventional method. It also shows a significance of 0.048 for accuracy and 0.018 for precision which is less than 0.05. Conclusion: It would be concluded that the frame difference method is producing high accuracy and precision when compared with the Vibe method. It is applicable for monitoring systems and home security. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. BackMatter.
- Published
- 2016
36. Author index.
- Published
- 2014
37. Author index.
- Published
- 2013
- Full Text
- View/download PDF
38. Acknowledgment to Reviewers of Electronics in 2021.
- Subjects
SCHOLARLY publishing - Published
- 2022
- Full Text
- View/download PDF
39. Acknowledgment to Reviewers of Land in 2021.
- Subjects
- ABBAS, Farhat, AGUILOS, Maricar, AGUMASSIE, Tena
- Published
- 2022
- Full Text
- View/download PDF
40. Front Matter: Volume 11433.
- Author
-
Osten, Wolfgang and Nikolaev, Dmitry P.
- Published
- 2020
- Full Text
- View/download PDF
41. Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.
- Author
-
Zhen, Zhijun, Chen, Shengbo, Yin, Tiangang, Chavanon, Eric, Lauret, Nicolas, Guilleux, Jordan, Henke, Michael, Qin, Wenhan, Cao, Lisai, Li, Jian, Lu, Peng, Gastellu-Etchegorry, Jean-Philippe, and Panagiotis, Barmpoutis
- Subjects
LEAF area index ,NORMALIZED difference vegetation index ,SOILS - Abstract
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
42. Acknowledgment to Reviewers of Sensors in 2020.
- Subjects
DETECTORS - Published
- 2021
- Full Text
- View/download PDF
43. Acknowledgement to Reviewers of Sensors in 2018.
- Subjects
SCHOLARLY peer review - Published
- 2019
- Full Text
- View/download PDF
44. Actionable Science of Global Environment Change : From Big Data to Practical Research
- Author
-
Ziheng Sun and Ziheng Sun
- Subjects
- Environmental sciences--Data processing, Climatic changes--Data processing
- Abstract
This volume teaches readers how to sort through the vast mountain of climate and environmental science data to extract actionable insights. With the advancements in sensing technology, we now observe petabytes of data related to climate and the environment. While the volume of data is impressive, collecting big data for the sake of data alone proves to be of limited utility. Instead, our quest is for actionable data that can drive tangible actions and meaningful impact.Yet, unearthing actionable insights from the accumulated big data and delivering them to global stakeholders remains a burgeoning field. Although traditional data mining struggles to keep pace with data accumulation, scientific evolution has spurred the emergence of new technologies like numeric modeling and machine learning. These cutting-edge tools are now tackling grand challenges in climate and the environment, from forecasting extreme climate events and enhancing environmental productivity to monitoringgreenhouse gas emissions, fostering smart environmental solutions, and understanding aerosols. Additionally, they model environmental-human interactions, inform policy, and steer markets towards a healthier and more environment-friendly direction.While there's no universal solution to address all these formidable tasks, this book takes us on a guided journey through three sections, enriched with chapters from domain scientists. Part I defines actionable science and explores what truly renders data actionable. Part II showcases compelling case studies and practical use scenarios, illustrating these principles in action. Finally, Part III provides an insightful glimpse into the future of actionable science, focusing on the pressing climate and environmental issues we must confront.Embark on this illuminating voyage with us, where big data meets practical research, and discover how our collective efforts move us closer to a sustainable and thriving future. This book is an invitation to unlock the mysteries of our environment, transforming data into decisive action for generations to come.
- Published
- 2023
45. Advances in Environmental Research. Volume 95
- Author
-
Justin A. Daniels and Justin A. Daniels
- Subjects
- Environmental quality, Environmental protection, Environmental degradation, Pollution
- Abstract
This book focuses on the latest developments in environmental research.
- Published
- 2023
46. Cancer Prevention Through Early Detection : First International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
- Author
-
Sharib Ali, Fons van der Sommen, Bartłomiej Władysław Papież, Maureen van Eijnatten, Yueming Jin, Iris Kolenbrander, Sharib Ali, Fons van der Sommen, Bartłomiej Władysław Papież, Maureen van Eijnatten, Yueming Jin, and Iris Kolenbrander
- Subjects
- Image processing—Digital techniques, Computer vision, Machine learning, Computers, Application software
- Abstract
This book constitutes the refereed proceedings of the first International Workshop on Cancer Prevention through Early Detection, CaPTion, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022. The 16 papers presented at CaPTion 2022 were carefully reviewed and selected from 21 submissions. The workshop invites researchers to submit their work in the field of medical imaging around the central theme of early cancer detection, and it strives to address the challenges that are required to be overcomed to translate computational methods to clinical practice through well designed, generalizable (robust), interpretable and clinically transferable methods.
- Published
- 2022
47. Research Anthology on Ecosystem Conservation and Preserving Biodiversity
- Author
-
Information Resources Management Association and Information Resources Management Association
- Subjects
- Nature conservation, Biodiversity conservation, Ecosystem management
- Abstract
In today's rapidly evolving world, it has never been more critical to consider key environmental issues such as climate change, pollution, and endangered species. Society faces an unknown future where the fate of the environment is continuously in flux based on current preservation initiatives that governments develop. In order to ensure the world is protected moving forward, further study on the importance of securing environments, ecosystems, and species is necessary to successfully implement change. The Research Anthology on Ecosystem Conservation and Preserving Biodiversity considers the best practices and strategies for protecting our current ecosystems as well as the potential ramifications of failing to implement policies. Society is at a crossroads where if we continue to ignore the danger and warning signs brought about by environmental issues, we will be unable to maintain a healthy environment. Covering essential topics such as extinction, climate change, and pollution, this major reference work is ideal for scientists, industry professionals, researchers, academicians, policymakers, scholars, practitioners, instructors, and students.
- Published
- 2022
48. Advanced Concepts for Intelligent Vision Systems : 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings
- Author
-
Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders, Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, and Paul Scheunders
- Subjects
- Computer vision, Computer engineering, Computer networks, Application software, Machine learning, Education—Data processing, Social sciences—Data processing
- Abstract
This book constitutes the proceedings of the 20th INternational Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020, held in Auckland, New Zealand, in February 2020. The 48 papers presented in this volume were carefully reviewed and selected from a total of 78 submissions. They were organized in topical sections named: deep learning; biomedical image analysis; biometrics and identification; image analysis; image restauration, compression and watermarking; tracking, and mapping and scene analysis.
- Published
- 2020
49. XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 : MEDICON 2016, March 31st-April 2nd 2016, Paphos, Cyprus
- Author
-
Efthyvoulos Kyriacou, Stelios Christofides, Constantinos S. Pattichis, Efthyvoulos Kyriacou, Stelios Christofides, and Constantinos S. Pattichis
- Subjects
- Biomedical engineering, Biomedical engineering--Congresses, Medical technology--Congresses
- Abstract
This volume presents the proceedings of Medicon 2016, held in Paphos, Cyprus. Medicon 2016 is the XIV in the series of regional meetings of the International Federation of Medical and Biological Engineering (IFMBE) in the Mediterranean. The goal of Medicon 2016 is to provide updated information on the state of the art on Medical and Biological Engineering and Computing under the main theme “Systems Medicine for the Delivery of Better Healthcare Services”. Medical and Biological Engineering and Computing cover complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems. Research and development in these areas are impacting the science and technology by advancing fundamental concepts in translational medicine, by helping us understand human physiology and function at multiple levels, by improving tools and techniques for the detection, prevention andtreatment of disease. Medicon 2016 provides a common platform for the cross fertilization of ideas, and to help shape knowledge and scientific achievements by bridging complementary disciplines into an interactive and attractive forum under the special theme of the conference that is Systems Medicine for the Delivery of Better Healthcare Services. The programme consists of some 290 invited and submitted papers on new developments around the Conference theme, presented in 3 plenary sessions, 29 parallel scientific sessions and 12 special sessions.
- Published
- 2016
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.