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The paper presents the research aimed at setting up and developing a software system for the printing process parameters identification based on modern computer and software systems, algorithmic principles, principles of expert systems construction and advanced learning. Thus, the possibilities of application of contemporary software tools were investigated, which facilitates the process and forms the program structure of the model that uses programming languages based on the expert systems construction principles and tools for the development of system model based on the principles of modern learning. For complex model development, concepts of process knowledge bases with influential process parameters of printing technique have been developed through modelling and construction based on the logic of expert systems with the presentation, use and involvement of experts knowledge in decision making with the evaluation of the impact of individual parameters. In addition to this approach, a module was developed using modern software tools based on an algorithmic principle and a module for identifying printing process parameters using modern platforms based on advanced learning. Sophisticated software model has been made through the research and developed with databases of process parameter identification systems based on modern software tools. This tool enables a significant expedition of the solution resolving, thus improving the graphical production process and the processes of acquiring and expanding knowledge. The model is based on integrative modules: a printing process parameters identification system based on algorithmic program structure systems, a printing process parameters identification system based on expert system building principles, and a printing process parameter identification system based on modern learning systems.

Noninvasive contactless methods for electric power line monitoring based on magnetic field measurement have become an interesting topic for researchers and the electric power industry since introduction of the Smart Grid concept. By measuring and analyzing magnetic field originating from currents in power line conductors, it is possible to detect faults in the network. In medium voltage distribution networks, where a variety of different pole geometries are present, different criteria for fault detection have to be employed for each geometry, which complicates detection and influences accuracy. This paper proposes a novel approach for fault detection in medium voltage distribution networks which is based on processing of signals measured by low cost contactless magnetic field sensors. In order to create a generalized method for fault detection, a sequence of mathematical transformations of the measured magnetic field components is applied. A novel geometric transformation which eliminates influence of pole geometry is introduced, providing signals from which steady-state symmetrical components of the rotating magnetic field are computed. Those components are used as general fault detection criteria. The proposed approach is confirmed to be applicable for different fault types by a set of experiments on three-phase overhead power line model scaled to laboratory conditions.

Pachyrhynchini and Doliops constitute Batesian model-mimic system of Coleopteran (beetles) mimicry endemic to the Philippines archipelago. Doliops longhorn beetles are extremely rare and interesting group of weevil mimicking species and there should be a broad interest to save them from extinction. The awareness of the existence of these amazing and unique examples of mimicry would help to open international conservation initiatives. However, currently there are no efficient tools or methods available to measure or to quantify the degree of visual similarity between the model and the mimic species. This was the motivation to develop the image processing technique, as the first step in the larger project, for the purpose of colored aposematic pattern detection and two-objects visual similarity quantification. Although developed on peculiar beetles, this approach could have broad applications.

In recent years, the number of requests for connection of PV systems to the medium voltage distribution network has been significantly increasing. In order to approve a connection, it is necessary to comprehensively consider the integration impacts on the grid. In this paper, a two-step quasi-dynamic network simulation in DIgSILENT PowerFactory automated by Python scripting is proposed. In the first step, based on statistics of meteorological parameters and consumer load profiles, long-term hourly sequences of PV system power production and load of all consumers were artificially generated. In the second step, a Monte Carlo simulation applied to IEEE 33-bus system with an integrated PV system was performed. As illustrative results, the voltage profile and active power losses are shown and discussed.

Similarities in the spectral contents of lung sounds (LS) and heart sounds (HS) hinder identification of the origin of particular acoustic indicators and during standard auscultation might be difficult to decide if they are of pulmonary or cardiovascular origin. Therefore to minimize this uncertainty a two-channel stethoscope was utilized with one channel being focused on lung sounds acquisition and another one on heart sounds signals knowing of existing crosstalk. In the next stage, a separation of sources was attempted utilizing Independent Component Analysis (ICA). However, as the outcome was not fully satisfactory, an Empirical Mode Decomposition (EMD) was applied. In particular, the EMD outputs were processed with Gaussian Mixed Models (GMM) to select which components corresponded to HS and which to LS, leading to classification accuracy of 70%. The developed methodology promises to be useful in performing initial cardiopulmonary diagnoses assisted by computerized monitoring, especially in the absence of experienced personnel.

This paper proposes the system for ship outfitting support based on the augmented reality. Considering the practical applicability of the elaborated procedure as the highest priority, the intention was to develop a sufficiently robust method for model registration that could be applied in extremely demanding environments such as the interior of the ship during construction. The approach presented in this paper is based on QR markers consisting the code value of the model from the database thus enabling the interactive mapping between the CAD 3D model, reality and digitized technical documentation The application has been developed in Unity 3D game engine and shows promising potential to support interactive ship outfitting procedures during construction in the shipyard.

The authors explain how to combine agile methods with traditional project management, which components of traditional project management should be maintained, and how they should be combined with agile project management methods. Purely agile or purely traditional project management methods are great, but under certain circumstances or in certain situations they cannot be used in their pure forms and should be combined with other approaches. This is where hybrid project management steps in and fills in the gaps.

Optimal sizing of standalone hybrid renewable power supply for mobile telephony base stations is considered in this paper. This task is very complex due to stochastic nature of input variables such as wind speed, solar irradiance, ambient temperature and electric load. Further concern is modeling of the power consumed by cooling devices which is also uncertain variable dependent both on ambient conditions and power dissipation of telecommunication equipment. The realistic behavior of the station both in electrical and thermal terms can be analyzed only by a comprehensive probabilistic simulator. The basic idea of this paper is to systematically vary the main input parameters (rated powers of wind turbine and photovoltaic system along with battery capacity) and to run a Monte Carlo simulation for each considered variant observing the technical and economic indicators. Based on the minimum annual costs, it is possible to choose the best available investment solution. Simple optimization can be extended to multi-objective by adding some additional goals, such as minimizing the operating time of a diesel backup generator. The optimization method is tested on an illustrative example of a base station located in the Mediterranean area.

Bifacial photovoltaic modules have gained increasing attention in the last decade due to their potential to achieve higher annual energy yield in comparison to conventional monofacial modules. Since the higher energy production is also accompanied by higher investment costs, it is necessary to conduct a careful techno-economic analysis in order to provide the investors an answer about their accurate cost efficiency. The achievable energy output of a bifacial photovoltaic power plant is influenced by many factors such as module geometry, row spacing, orientation of the modules and ground albedo. Since the methodology for prediction of the energy yield has not yet been standardized, the main target of this paper was to create one version of a comprehensive standalone energy calculator that would serve as a useful performance assessment tool for designers and investors. The developed software tool was tested on several characteristic scenarios and the obtained results were compared with the results provided by two freely available online calculators.

This paper discusses the problem of powering a remote rural mobile base station using a standalone hybrid renewable energy system. A wind turbine and photovoltaic system are employed as the complementary power generation technologies, while the diesel generator serves as a backup power supply. A battery is required to reduce the impact of intermittency of renewable sources. On the consumption side, along with telecommunication electronic equipment, the consumption of cooling devices as a result of the ambient temperature, is also taken into account. The behavior of the base station in electrical and thermal terms is tested using the sequential Monte Carlo simulation. Adequate models have been used to generate wind, irradiance, and temperature input series, using the monthly averages for calibration, as the statistic information that is widely available in meteorological atlases, even for remote rural locations. The developed software provides all the variables of interest either in the form of chronological diagrams or probability histograms. The simulation platform can also be incorporated as a module of an algorithm for selection of optimal capacity of the generating system elements and for the optimal control of the cooling devices.

The paper presents an online web-oriented system named SOLARS, which is aimed at calculating the feasibility of building the photovoltaic (PV) systems. SOLARS currently enables potential investors to calculate the technical and financial feasibility of building the PV systems in the Republic of Srpska (Bosnia and Herzegovina). Very intuitive GUI design enables investors to obtain feasibility calculations in three simple steps: (i) selection of a geographical location, (ii) specification of technical parameters, and (iii) specification of financial parameters. A usage scenario is illustrated by a real feasibility calculation example.

This paper presents a laboratory setup for testing and experimental verification of fault detection methods based on contactless measurement of power line magnetic field. The setup consists of a physical power line model scaled to laboratory conditions with two orthogonal measuring coils installed on one support pole underneath phase conductors. Voltages induced in the coils are amplified and filtered out by using analog electrical circuits and are fed to a digital data acquisition board. The sampled voltages are digitally processed by employing a generalized fault detection method previously developed by the authors. In this paper the main steps for designing the measuring coils, analog amplifying and filtering circuits are described in detail. The developed laboratory setup is verified by experiments for different shunt and series faults.

Wireless devices have a plethora of technologies at their disposal to connect to the Internet and other services. Management and control of each technology are traditionally isolated, and coordination between technologies is nearly non-existent. This isolation leads to poor resource usage, which in turn reduces performance and service guarantees. To satisfy growing user demands, we need to leverage the different service guarantees offered by each technology. Additionally, we need to improve orchestration between technologies to increase performance and flexibility while offering a more extensive range of service guarantees and maximizing resource utilization across networks and users. In this work, we present the general challenges one encounters when managing heterogeneous wireless networks. We argue that the primary challenge is the heterogeneity itself, the number of different devices and technologies, the different service requirements, and the increasing complexity as a consequence. However, technology abstraction can overcome these challenges. We provide an overview of state of the art commercial and scientific solutions and show their strengths and weaknesses. Based on this, we discuss the current status and what future challenges still await to provide full seamless heterogeneous wireless network management.

The paper proposes a novel planning approach for optimal sizing of standalone photovoltaic-wind-diesel-battery power supply for mobile telephony base stations. The approach is based on integration of a comprehensive probabilistic sequential Monte Carlo simulator and a black-box optimizer using DIRECT (DIviding RECTangles) method. The main property of the simulator is that input variables are modeled as correlated random processes rather than independent random variables without a time index. By taking into account autocorrelation and mutual correlation, temporal properties of all input variables are kept realistic, such as sunrise and sunset times in irradiance model, daily and seasonal changes in temperature model, and consumption of cooling devices and electronic equipment which depends on the ambient temperature and setting of the parameters of the control devices. The optimization target is to select rated capacities of major system components and to tune the main control parameters for achieving minimum total annual costs without compromising system reliability. The proposed algorithm is tested on planning a typical 2 kW potential base station located on a windy and sunny hill in the Mediterranean region.

Cardiopulmonary auscultation is a diagnostic procedure that has a challenging task since the components of heart rate and lung sounds overlap. There were many approaches to quantify the characteristics of these signals, and one of the newest is the voice activity detection (VAD) and the Gaussian Mixture Models (GMM). Considering the lung and heart sounds as acoustic events, this paper proposes a novel assessment methodology of these diagnostic indicators. Here, VAD-GMM was applied to detect and extract the main events in lung sound and heart sounds. VAD- GMM results were compared with other VAD methodology based on statistical approach, and it was found that VAD-GMM give more definite results. Since Mel Frequency Cepstral coefficients (MFCC) and Quartiles feature vectors, were already successful in pattern recognition, VAD-GMM was carried out using this kind of acoustic vectors. Therefore, this method could add in a transition from qualitative traditional auscultation to quantitative assessment and assisted computerized diagnosis by identifying abnormal acoustic indicators. Diagnosis by computerized detection promises to be a more efficient method than traditional methods, which are limited by the auditory capability and experience of a medical professional.

These studies demonstrate a combination of tested methods to separate lung and heart sound signals’ (LS-HS) principal components recorded using a digital dual stethoscope. Initially, when these raw signals are acquired, their principal components intertwine in the characteristics of the frequency and time domains. Applying Independent Component Analysis (ICA) combined with Empirical Mode Decomposition allows separation of their origin. Further, Gaussian Mixture Models (GMM) were utilized to determine to which specific class of components they belong. Each set of recorded signals was evaluated and the outcomes were compared against the verified reference group. As cardiopulmonary diseases represent a major global health concern, this can be helpful in an early screening for distinct cardiopulmonary problems manifested by peculiar acoustic indicators.

An important aspect of managing multi access point (AP) IEEE 802.11 networks is the support for mobility management by controlling the handover process. Most handover algorithms, residing on the client station (STA), are reactive and take a long time to converge, and thus severely impact Quality of Service (QoS) and Quality of Experience (QoE). Centralized approaches to mobility and handover management are mostly proprietary, reactive and require changes to the client STA. In this paper, we first created an Software-Defined Networking (SDN) modular handover management framework called HuMOR, which can create, validate and evaluate handover algorithms that preserve QoS. Relying on the capabilities of HuMOR, we introduce ABRAHAM, a machine learning backed, proactive, handover algorithm that uses multiple metrics to predict the future state of the network and optimize the load to ensure the preservation of QoS. We compare ABRAHAM to a number of alternative handover algorithms in a comprehensive QoS study, and demonstrate that it outperforms them with an average throughput improvement of up to 139, while statistical analysis shows that there is significant statistical difference between ABRAHAM and the rest of the algorithms.

One of the effective strategies for increasing reliability of the distribution networks is to perform a faster fault localization. The common techniques for accelerating the process of finding the faults are based on application of fault locators and fault passage indicators. The goal of this study is to assess the performance of both techniques, either considered separately or in combination with each other. Since the performance of both concepts depends on various stochastic variables, a comprehensive assessment methodology developed in this paper is based on the non-sequential Monte Carlo simulation.

In this paper a methodology for determining proper number and positions of fault passage indicators (FPIs) in medium voltage distribution networks with installed fault locators (FLs) is proposed and discussed. The main goal is to achieve the techno-economic balance, by obtaining maximum improvement of the reliability indices while using the minimum number of FPIs. The method is verified by Monte Carlo simulation on a real combined urban/rural distribution network in Bosnia and Herzegovina. The purpose of the simulation tests is to assess performance of the proposed methodology, as well as to discuss the results and propose potential actions required to improve their reliability for several possible scenarios: the use of FLs only, the use of FPIs only and the combined use of FLs and FPIs.

Assessment of Lung Sounds (LS) and Heart sounds (HS), and documentation of their characteristics is a part of routine cardiopulmonary diagnostic procedures. Peculiar acoustic components associated with inspiratory, expiratory, S1 and S2 phases of cardiopulmonary function such as for example asthmatic wheezes serve as useful diagnostic indicators. Supplementing traditional auscultation with automatic detection and categorization of characteristic signals provides opportunities of enhanced long term monitoring which is indispensable in COPD or other cardiac origin abnormalities. Also, due to age related or other sensorial limitations of a health care professional, automatic event detection of LS-HS could significantly enhance these aspects of medical diagnosis. In particular, proposed novel method is based on the elements of VAD (Voice Activity Detection), associated with similar to LS-HS frequency components, and utilization of Gaussian Mixture Models (GMM). The automatic detection with GMM is accompanied by Mel Frequency Cepstral Coefficients (MFCC) and Hilbert transform. This technique allows detecting and classifying peculiar events of LS and HS, and obtain a higher classification rates when compared with other VAD forms. The efficiency classification achieved with parallel Hidden Markov Models (HMM) was 95% for LS and 92% for HS, documenting their viable clinical suitability.

Modern connected devices are equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Current network management solutions fail to provide a fine-grained, coordinated, and transparent answer to this heterogeneity, while the lower layers of the OSI stack simply ignore it by providing full separation of layers. To address this, we propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless networks and introduce capabilities such as packet-level dynamic and intelligent handovers (both inter- and intra-technology), load balancing, replication, and scheduling. The framework is the first of its kind in providing a fine-grained packet-level control across different technologies by introducing a fully transparent virtual medium access control layer and an software-defined networking-like controller with global intelligence. Furthermore, we present a novel optimization problem formulation that can be solved to optimally configure the network. We provide a thorough evaluation through simulations and a prototype implementation. We show that our framework enables, in a real-life setting, transparent and real-time inter-technology handovers and that coordinated load balancing can double the network-wide throughput across different scenarios.

The density of Wi-Fi at professional environments, such as offices, is resulting in increased strain on the management of Wi-Fi networks, of which handovers are an essential part of. The centralized approach of SDN allows taking more intelligent decisions, as opposed to per device optimizations. There is thus an increased interest in SDN-based Wi-Fi management, especially for handovers. Several proposals for SDN-based handover algorithms are being investigated and there is a need for actually validating them. In this demo, we will showcase a Wi-Fi SDN framework, on top of which a number of decision-making handover algorithms can be used. This framework is used in a real-life, large scale Wi-Fi testbed. The framework allows for different decision-making handover algorithms to be created and validated on large scale.

Current mobile consumer devices are equipped with the ability to connect to the Internet using a variety of heterogeneous wireless network technologies (e.g., Wi-Fi and LTE). These devices generally opt to statically connect using a single technology, based on predefined priorities. This static behavior does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of for example throughput and reliability, grow. Multipath TCP (MPTCP) is a solution that allows the simultaneous use of multiple network interfaces. However, it does this uncoordinated for a single connection between two endpoints. Therefore, this paper proposes a Software-Defined Networking (SDN) architecture to enable coordinated multi-path routing across the several networks for mobile devices. Moreover, we propose a novel weighted MPTCP scheduler that allows the transmission of certain controllable percentages of data per network interface. The proposed idea is evaluated through a real-life prototype implementation with a smartphone.

This paper proposes a fast and efficient strategy to determine the number and locations of fault indicators (FI) in medium voltage distribution networks. The objective function takes into account the most important characteristics of the network such as topology, non-uniform failure rates of the lines as well as the power demand and number of customers. For seeking the minimum of the objective function, a straightforward algorithm based on the Pattern Search (PS) is developed. The methodology is tested on a real distribution network, showing its great potential to improve the reliability indices at the lowest investment costs.

Alzheimer's represents progressively increasing healthcare problems especially in aging population affecting memory, overall thinking process and general behavior. Symptoms usually develop slowly and get worse over time. Alzheimer's disease accounts for 60 to 80 percent of dementia cases. It is caused by abnormal deposits of proteins that form amyloid plaques and tau tangles throughout the brain, and once- healthy neurons stop functioning, lose connections with other neurons, and die. They manifest neurodegeneration. Therefore, we propose neuron morphology algorithm that automatically routinely distinguishes healthy from degenerated neurons. This algorithm derives numerical indicators that represent abnormalities in neuron morphology examination.

It is estimated that over 200 million people worldwide experience a decrease in bone strength associated with osteoporosis. This causes increased susceptibility to fracture which represents one of the critical challenges of aging population. Therefore, novel approaches in assessing overall bone integrity are introduced. These approaches included the use of micro CT scan of skull bone images of mouse which allow to study a new mouse model with gap junction protein mutation, and compare normal and diseased animals. We propose skull bone detection algorithm capable of successfully detecting skull bone density. The proposed method has two functions: visual and numerical. Visual function is expressed through detection and indication of the potential bone tissue by marking it in a different color and distinguishing it from the background. Numerical function enables quantification of the amount of detected skull bone by calculating its numerical content equivalent.

The current local area networks (LANs) are occupied by a large variety of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different network technologies (e.g., Ethernet, 2.4 and 5GHz Wi-Fi). Nevertheless, devices generally opt to statically connect using a single technology, based on predefined priorities. This static behaviour does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of latency and throughput, grow. In this paper we present a real-life SDN-based implementation of our previously proposed algorithm that addresses this problem.

Thoracic auscultatory signs are commonly evaluated as a part of standard diagnostic procedures which might be focused on cardiac (HS) or pulmonary (LS) origin indicators depending on evaluator's aims. However, due similarities of these sounds spectral components and their extremely small intensity especially in the presence of common external noise, a separation of their origin components can be difficult even for an experienced practitioner. This is specially challenging if he/she is presented with prior recordings of these sounds as a part of related auscultatory patient's record review. Differences in periodicity of these cardiac and pulmonary origin sounds assist in qualitative characterization while combined with proposed methods may lead to improved quantitative characterization and origin depended separation of their characteristics. This proposed and verified technique emphasizes initial localization of the HS related components and their subsequent extraction. Though, an examination of both HS and LS components is conducted concurrently as a part of an overall thoracic sounds analysis. In particular, as experimentally optimized, in the case of HS components assessment the Hilbert Transform (HT) is used to detect extreme points; while, in the LS components evaluation Voice Activity Detection techniques (VAD) and the calculation of thresholds of some components of acoustic vectors MFCC, are useful to detect and localize the events of particular examiner's interest. Also the inspiratory and expiratory phases can be differentiated by means of the sixth component of Cepstral Coefficient of Mel Frequency (MFCC). In order to evaluate the efficiency of this technique, it was decided to apply Hidden Markov Models with Mixed Gaussian Models (HMM-GMM). The events extracted from manual and automatic detection were used to calculate HMMGMM models and perform classification. Overall, this approach allows easier detection of any anomalies and their possible origin in complex cardiopulmonary disorders.

Local area networks (LANs) are employed by a plethora of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different wireless network technologies. Existing solutions and the lower layers of the OSI stack are unfit to cope with this heterogeneity. For instance, dynamical inter-technology switching is user-of application-based. We propose the ORCHESTRA framework to manage the different devices in heterogeneous wireless local area networks (WLANs) and introduce capabilities such as packet-level dynamic and intelligent handovers (both inter-and intra-technology), load balancing, replication, and scheduling. The framework consists of a controller that is capable of communicating with both existing Software-Defined Networking (SDN) and Network Function Virtualization (NFV) controllers and with devices containing a newly introduced virtual Medium Access Control (MAC) layer. We show that the virtual MAC enables transparent and real-time inter-technology handovers and that our solution scales up to two thousands of clients.

Diabetic retinopathy is the progressive pathological alterations in the retinal microvasculature that very often causes blindness. Because of its clinical significance, it will be helpful to have regular cost-effective eye screening for diabetic patients by developing algorithms to perform retinal image analysis, fundus image enhancement, and monitoring. The two cost-effective algorithms are proposed for exudates detection and optic disk extraction aimed for retinal images classification and diagnosis assistance. They represent the effort made to offer a cost-effective algorithm for optic disk identification, which will enable easier exudates extraction, exudates detection and retinal images classification aimed to assist ophthalmologists while making diagnoses. The proposed algorithms apply mathematical modeling, which enables light intensity levels emphasis, easier optic disk and exudates detection, efficient and correct classification of retinal images. The algorithm is robust to various appearance changes of retinal fundus images and shows very promising results. Fundus images are classified into those that are healthy and those affected by diabetes, based on the detected optic disk and exudates. The obtained results indicate that the proposed algorithm successfully and correctly classifies more than 98% of the observed retinal images because of the changes in the appearance of retinal fundus images typically encountered in clinical environments. Copyright © 2014 John Wiley & Sons, Ltd.

Fault passage indicators (FPIs) and fault locators (FLs) are employed in modern distribution networks in order to enhance the process of fault localization, thus resulting in reduction of interruption time and improving the reliability of power supply. In this paper, a novel probabilistic techno-economic optimization method is proposed for determining the number and positions of FPIs that lead to maximum reduction of interruptiontime and investment costs in medium voltage (MV) distribution networks with and without FLs. The proposed method is basedon a probabilistic non-sequential Monte Carlo simulation model of the real network, which is a proper compromise between complicated sequential simulation models and too simplified analytical models. The main goal of the method is to obtain maximum improvement of the network reliability indices while using the minimum number of FPIs. The method is tested on a combinedurban/rural MV distribution network in Bosnia and Herzegovina and results are thoroughly discussed.

The paper presents research related to thermochromic inks printed on textile materials. These types of materials are smart materials, they act as packaging indicators, and we use them to develop a software application on mobile devices. That application identifies the most significant changes in parameters of the goods contained in the packaging, such as temperature changes during the storage and transport (goods packaged in packaging that is sensitive to heat), or such as pharmaceuticals and frozen food, etc. This paper aims to demonstrate the possibility of connecting thermochromic materials and Augmented Reality technology through the development of an application for identifying thermochromic materials changes. The tested samples were printed on the textile with a screen printing technique with conventional and thermochromic leuco inks on various textile materials. The Augmented Reality platform Vuforia was used for developing a software application for mobile devices. The application was developed for the iOS mobile platform. The result of the research is the functional application that actively monitors and displays changes in thermochromic materials due to temperature changes, which identifies the effect of heat on the goods in textile packaging.

The acoustic cardiopulmonary signals contain complex but quantifiable essential diagnostic signs. Particularly, the lung sounds associated with inhalation and exhalation, and the heart sounds as particularly characterized by their principal components S1 and S2 when quantitatively utilized could improve the classification accuracy of diverse disease conditions. This project is related to the events classification (inhalation, exhalation, S1 and S2) with Gaussian Mixture Models (GMM) representing states in Hidden Markov Models (HMM), which were further enhanced by applying Principal Component Analysis (PCA) on Mel Frequency Cepstral Coefficients (MFCC) and Quartiles acoustic vectors. These classification outcomes drive the implementation of events quantification techniques, and also the implementation of automated auscultation for enhanced medical diagnosis. In particular, the experiments were carried out and achieved more than 88% in efficiency classification. The use of PCA showed that it could improve the outcomes classifications of inhalation-exhalation signals when noise was present, but PCA did not improve the efficiency classification of noise free S1-S2 signals. The GMM-HMM performed better applying MFCC vectors in both cases, inhalation-exhalation and S1-S2 events. The clusters analysis with Quartile vectors identifies more clusters in inhalation-exhalation signals than in S1-S2 signals. These classification outcomes drive the implementation of events modeling techniques, and also the implementation of automated auscultation with a simple laptop, which could be found in most medical offices, even in rural areas, and utilized for assisted diagnosis and documentation.

Adipose tissue, known also as fat, is a loose connective tissue that fills up space between the organs and provides structural and metabolic support. The classical functions of adipose tissue are the storage of energy in the form of triglycerides, and thermal insulation. Historically, adipose tissue has been subdivided into two main types: white fat that stores energy, and brown fat, that induce thermogenesis and produce heat. Brown fat was previously regarded as a special type of fat relevant only for newborns and hibernating animals defending against cold. Recently has been shown that brown fat is physiologically present and active in adult humans. However, brown fat is extremely difficult to visualize and even more difficult to quantify. We have found that levels of brown fat are dramatically increased in mammary tissue of the BRCA1 mutant mice compared to the normal, wild-type mice. We attempted to quantify the amount of brown fat in histological sections of mammary tissues from these mice. We initially presented a mathematical model and intelligent software capable of automatically detecting and quantifying the content of fat adipose tissue, with the goal of defining the mammary adipose microenvironment. The proposed algorithm showed itself to be very successful in detecting brown adipose tissue in images that contain pure mammary tissue. We propose here improved adipose tissue detection algorithm capable of successfully detecting brown fat in the whole mammary gland image that contains extra information like arteries, background and other surrounding elements. The proposed method has two fold function: visual and numerical. Visual function is expressed through detection and indication of the potential brown fat in the tissue microscopic images by marking it in a different color and distinguishing it from the rest of the tissue. Numerical function enables quantification of the amount of detected brown fat by calculating its numerical content equivalent.

To benefit from the performance gains and cost savings enabled by elasticity in cloud IaaS environments, effective automated mechanisms for scaling are essential. This automation requires monitoring system status and defining criteria to trigger allocation and deallocation of resources. While these criteria are usually based upon resource utilization, this may be inadequate due to the impossibility of identifying the actual amount of required resources. Consequently, most systems update resource allocation in small increments, e.g., one VM at a time, which may negatively affect performance and cost. In this paper, we propose a novel approach in which the system monitors workload, instead of utilization, and, by means of a scalability model, it makes predictions of resource demand and updates the allocated resources accordingly. We provide an implementation of this approach and describe experimental results that show its effectiveness.

PurposeThe purpose of this paper is to take a novel approach to help users better understand and be more aware of what third parties are learning about them as they browse the web.Design/methodology/approachThe approach the authors take is to personalize the awareness by using JavaScript embedded in a web page to examine portions of a user's web‐browser history in order to ascertain web sites that the user has visited. The authors then personalize information reported to the user about what third‐party sites are tracking the user's behavior along with demographic information these sites may be inferring from these visited sites and the user's geographic location.FindingsIt was found that 63 percent of users agreed with a statement of concern for third parties monitoring activities, about half of the respondents agreed with a concern for knowledge about a user's location and a little more than half agreed to concern about inference of demographic information. It was found that females are more concerned about these issues than males. In terms of possible actions, a majority of users report using an ad blocker tool and even more delete cookies at least some amount of time. Using an opt‐out mechanism or removing browser history is done by less than 20 percent of users. Despite expressing more concern for information known by third parties, females are not significantly more likely to take actions that may limit what is leaked to these third parties. A contributor to this discrepancy is that females were much less likely to know their settings for many of the actions, indicating less familiarity with them.Social implicationsWeb privacy is an important social issue so helping users to better understand tracking of their actions, along with what actions can be taken to limit tracking, is important.Originality/valueThe paper takes an original approach to helping users understand what third parties learn about them and follows up that approach with a survey of user attitudes and actions on this important topic.

This paper presents integrated Hidden Markov and Gaussian Mixture Models (HMM-GMM) to classify lung sounds (LS) and heart sounds (HS) characteristics. In order to optimize the models' size, several methodologies encompassing dendrograms, silhouettes and the Bayesian Information Criterion (BIC) were applied. The experiments were carried out extracting features from the LS and HS with MFCC (Mel-Frequency Cepstral Coefficients) vectors and Quantile vectors, specifically Quartiles. The merged HMM-GMM architecture for the signals using Quartiles, overall offered consistent classification results. In both types of vectors, a high degree of classification efficiency was obtained reaching up to 96% for the studied sets of signals. For MFCC the classification results were not conclusive. An assessment of the number of clusters using dendrograms, silhouettes, and BIC linked with the models' size. Consequently this allows to enhance efficiency of merged HMM-GMM models in diagnostic classification of cardiopulmonary acoustic signals.

Nanoparticles represent material particles in which one dimension measures ∼100 nanometers or less. When processed into nanoparticles, the properties of many conventional organic and inorganic materials change. Since nanoparticles can be made from organic or inorganic substances, they are versatile in potential technological applications, from delicate electronics to revolutionary medical procedures. While an average structure and size is clear from characterization measurements and observations there is always a dispersity in size and shape of the final nanoparticle system. Even though microscopy is an excellent technique in capturing direct images of the nanoparticle morphology, it is difficult to assess the dispersity in size and shape of the nanostructure simply by observation of the microscopy data. This is why we propose a computer-assisted tool developed for the purpose of facilitated nanoparticle detection and its morphology identification.

This paper describes an implementation of a SAT2IP server extension that supports DTCP protocol. This protocol protects distributed digital content during its transfer from the SAT2IP server to the consumer device. The implementation consists of two modules: authentication and key exchange module, and content encryption module. The main advantage of this approach is its modularity. The test and verification procedure of proposed implementation is also described. Directions for further work are also discussed.

This paper explores feature selection and combining classifiers when binary features are used. The concept of Non-Reducible Descriptors (NRDs) for binary features is introduced. NRDs are descriptors of patterns that do not contain any redundant information. The underlying mathematical model for the present approach is based on learning Boolean formulas which are used to represent NRDs as conjunctions. Starting with a description of a computational procedure for the construction of all NRDs for a pattern, a two-step solution method is presented for the feature selection problem. The method computes weights of features during the construction of NRDs in the first step. The second step in the method then updates these weights based on repeated occurrences of features in the constructed NRDs. The paper then proceeds to present a new procedure for combining classifiers based on the votes computed for different classifiers. This procedure uses three different approaches for obtaining the single combined classifier, using majority, averaging, and randomized vote.

Different logotypes represent significant cues for video annotations. A combination of temporal and spatial segmentation methods can be used for logo extraction from various video contents. To achieve this segmentation, pixels with low variation of intensity over time are detected. Static backgrounds can become spurious parts of these logos. This paper offers a new way to use several segmentations of logos to learn new logo models from which noise has been removed. First, we group segmented logos of similar appearances into different clusters. Then, a model is learned for each cluster that has a minimum number of members. This is done by applying a linear inverse diffusion filter to all logos in each cluster. Our experiments demonstrate that this filter removes most of the noise that was added to the logo during segmentation and it successfully copes with misclassified logos that have been wrongly added to a cluster.

Over 50 million persons worldwide are affected by epilepsy. It can affect equally young babies as well as old people. Epilepsy is a brain disorder characterized by the neurobiological, cognitive, psychological and social consequences. It is known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologists diagnose and determine the patient's treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that might correspond to epilepsy. This research focuses on developing new classification technique and the prediction of pre-ictal states that announce epileptic seizures, from the online EEG data analysis. The idea is to place electrodes on critical regions on the patient's head that would wirelessly communicate with the EEG recorder and the unit that performs online automated pre-ictal state detection based on obtained EEG signal. The patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.

In this paper we presented results of the performance evaluation of the live 3G/HSPA radio access network when loaded with multiplayer real-time games and M2M applications traffic. Traffic, for the emerging Machine Type Communication and online multiplayer games, was generated by an application running on 10 mobile phones in parallel using TCP or UDP protocols. In the previous work NodeB processing power and the number of simultaneous HS users were identified as major bottlenecks for system performances. Set of new measurements is conducted with increased processing power and the number of simultaneous HS users. The focus of the work was on the radio cell statistics, i.e. to evaluate potential impact of the additional traffic on the performance of radio access network.

Measuring Internet performance for home users can provide useful information for improving networks. Unfortunately, Internet measurements typically require users to install special software, a major impediment to use. To overcome this impediment, we designed and implemented several scripting techniques to predict Internet performance from within the tightly constrained sandbox environment of a Web browser. Our techniques are integrated into a Web site project called “How's My Network” that provides predictions for common Internet activities, with this paper concentrating on the performance of online news, social networks, and online shopping. The contributions of this work include the design, implementation and evaluation of new approaches for predicting Web browser performance.

The rapid growth of Internet users and the increasing range of data types which are exchanged over this network (video, audio, text messages…), emphasize the security problem that this way of communication has.

We describe two systems for automatically generating English sentences, and evaluate the suitability of their output for speaker verification. The first system, SUSGen, generates grammatical but semantically anomalous sentences of controlled length, vocabulary and phonetic content. The second system, SASGen, extends SUSGen to generate a greater variety of sentences and ones that are, for the most part, semantically acceptable. We demonstrate that sentences generated by SASGen are significantly more readable and meaningful than those generated by SUSGen. Sentences generated by SASGen are not as readable or meaningful as humangenerated sentences, but the additional control SASGen provides for sentence length, vocabulary and phonetic content make it more suitable for speaker verification and other voice collection purposes than harvesting from human-generated sentences.

In this paper we presented results of the performance evaluation of the WCDMA/HSPA radio access network when loaded with multiplayer real-time games and M2M applications traffic. Evaluation was done in a live network, on one Node B. Traffic, characteristic for the emerging Machine Type Communication and online multiplayer games, was generated by an application running on 10 mobile phones in parallel, all connected to the Node B under test. The focus of the work was on the RTT (Round Trip Time) and radio cell statistics. The main goal of the cell statistics analysis was to evaluate potential impact of the additional traffic on the performance of 3G/HSPA radio access network. It is concluded that system is designed more for continuous traffic patterns with high data rate in general, than for sporadic traffic patterns with very low data rate, like that of M2M or online gaming nodes. Therefore, the cost of signaling and control activity is dominant in comparison to the data rate.

The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels. The regular examination of diabetic patients can potentially reduce the risk of vision impairment and in the last instance blindness. Early diabetic retinopathy detection enables application of laser therapy treatment in order to prevent or delay loss of vision. The diagnostics and detection of diabetic retinopathy is performed by specialized ophthalmologists manually and represents expensive procedure. Automatic exudates detection and retina images classification would be helpful for reducing diabetic retinopathy screening costs and encouraging regular examinations. We proposed the automated algorithm that applies mathematical modeling which enables light intensity levels emphasis, easier exudates detection, efficient and correct classification of retina images. The proposed algorithm is robust to various appearance changes of retinal fundus images which are usually processed in clinical environments.

In this paper we present results of traffic modeling and simulation of multiplayer real-time games and M2M applications using TCP protocol over Telekom Serbia HSPA mobile network, performed within the FP7 LOLA project. In this experimentation, the RTT (Round Trip Time) and cell statistics are analysed. For this purpose, a traffic generation application is developed for Android phones to generate traffic pattern for emerging Machine Type Communication and online multiplayer games in mobile wireless network for different offset of tests. Application is activated by setting the corresponding parameters related to desired test case, i.e. duration of testing, size and frequency of the data packets that the application sends to the server. The main goal of the cell statistic analysis is to evaluate potential impact of additional simulated traffic in view of increasing data-centric users on the performance of mobile wireless radio network.