bluuebunny/crossref_metadata_2025 huggingface dataset:
The impact of the capacity provisions in the Companies Act 71 of 2008 on the insolvency-remoteness of limited capacity special purpose vehicles used in securitisation schemes
Authors: Olivier Etienne A
Published: February 2021

The insolvency-remoteness of a special purpose vehicle (SPV) used in a securitisation scheme is of critical importance, because insolvency of the SPV can interrupt the payment streams due to the investors in such schemes. Several contractual methods are implemented to achieve insolvency-remoteness. In this article, it is argued that pacta de non petendo (non-petition clauses), limited recourse provisions, and subordination clauses, all common insolvency-remoteness provisions, do not violate public policy. It is also argued that the capacity provisions in the Companies Act 71 of 2008 (the Act) do not reduce the insolvency risk of a limited capacity SPV used in a securitisation scheme. The fact that ultra vires contracts concluded by limited capacity companies will be provisionally valid under the Act means that provisions in a company’s MOI that limit a company’s capacity will have very little external significance. It is argued that the right to restrain ultra vires contracts in terms of s 20(5) of the Act, in conjunction with the right to ratify such actions in terms of s 20(2), do not provide reliable legal certainty or protection to the investors in assets securitised through a limited capacity SPV.
Mitigation of political risks in infrastructural project finance in African countries
Authors: Gbede Olusegun , Oniemola Peter Kayode
Published: February 2021

Political risks have adversely affected project financing in African countries. There are instances of risks in the state hosting project that may negatively affect the bankability of the project. They include nationalisation of assets, spontaneous changes in laws and regulations by the government, wars, and terrorism etc. Investors require assurance to participate in project finance. Guarantee by the government on the stability of the polity is required. The government may also give assurances through legal measures, stabilisation clauses and guarantees to the effect that the regulatory environment of the project will be stable. This article examines infrastructural project finance in relation to political risks, with specific emphasis on African countries. Therefore, beyond assurance from the government, political risk mitigation instruments developed internationally can be employed. This article also elaborates the emergence of instruments/ mechanisms that have been developed internationally to mitigate political risks. These instruments include partial risk guarantees offered by international financial institutions, political insurance guarantees and export credit guarantees. It calls for the utilisation of these instruments and recommends that countries should tailor their regulatory regime to accommodate them. It contends that with the existence of these guarantees; the government has a role to play in creating a favourable legal regime and framework that will admit their utilisation within the legal system.
AN ANALYTICAL APPROACH TO THE NEURONAL MECHANISMS UNDERLYING THE MEMORY STORAGE IN THE BRAIN. (EXPERIMENTAL STUDY)
Authors: Koutsoukos Elias , Angelopoulos Elias , Maillis Anthony , Stefanis Costas
Published: February 2021

In the present in vivo experimental study, the dynamical properties of the electroencephalographic (EEG) activity recorded in the CA1 area and the dentate gyrous of rat hippocampus were investigated prior to the induction and during the maintenance phase of Long Term Potentiation (LTP). Our findings suggest that this form of brain plasticity can be reflected by the complexity of spontaneous EEG activity, as it is expressed by the reduction of the correlation dimension D2, indicates that different functional states of the brain are governed by different degrees of functional complexity.
NEW RECURSIVE DCT IMPLEMENTATIONS WITH GOERTZEL FILTERS
Authors: Demirsoy Suleyman Sirri , Beck Robert , Kale Izzet , Dempster Andrew G.
Published: February 2021

In this paper we report on a new recursive DCT architecture that is more efficient in terms of area and power in comparison to recently published recursive DCT architectures. Our approach here employs Type A, B and C Goertzel filters. These three different realizations of Goertzel filters together with multiplier­less implementation of loop multiplications are used so as to reduce the area, the multiplier delay, and undesirable transitions hence the power consumption. The newly proposed DCT structure has been compared with conventional recursive implementations at different transform lengths to observe that there are potential savings both in area and power.
GMSK NEURAL NETWORK BASED DEMODULATOR
Authors: Aiello Andrea , Grimaldi Domenico , Rapuano Sergio
Published: February 2021

In this paper, the pattern recognition characteristics of the Artificial Neural Net­ works are used to realise a real demodulator for Gaussian Minimum Shift­Keying signals, used in the GSM telecommunications. The demodulator utilises the Learning Vector Quantisation (LVQ) neural network. It offers both greater efficiency in demodulating and less sensitivity to noise. In order to solve the problem regarding input signal synchronisation, a pre­processing phase is organised. The demodulator prototype has been realised by implementing the pre­processing phase and the LVQ neural network on TMS320C30 Digital Signal Processor. The demodulator has been tested according to the European Telecommunication Standard Institute Recommendations.
LEARNING OF BIOLOGICAL BEHAVIOUR BY CLASSIFIER
Authors: Kanoh Hideaki , Hosokawa Akihiro
Published: February 2021

The objective of this paper is to investigate biological features of a virtual creature on the computer. It is assumed that the creature does not have a complex judgment, a sophisticated detector and has simple basic actions. We call it a bug. As a lower animal, an insect does not have complex brain, while it is seemed to be able to do complicated works. Specifically complex behaviour of an insect is considered to be based on a reflex action for the external stimuli. The classifier system is used for the generation of bug's behaviour rules in a complicated environment made in the computer. The classifier system is used for the generation of bug's behaviour rules in a complicated environment made in the computer. A classifier system is a reflex system, which generates an action soon after receiving a stimulus from the environment. This is quite similar to the feature of lower animals function of processing the information. The bug is released at some spot in the virtual world where baits are randomly located and there are obstacles and enemies. The classifier is rewarded only when the bug succeeds to obtain the bait. The more the bug learned, the more new classifier rules emerge: such as a rule to take a step forward or another rule to change the direction towards bait. At this stage, the bug learns the way to obtain the bait avoiding the obstacles and enemies by itself. The results of the observation of simulation experiments show that the chain of the continuous action which consists of multiple classifiers is arising.
ИТЕРАТИВНЫЙ АЛГОРИТМ ДИСКРЕТНОГО ОРТОГОНАЛЬНОГО ПРЕОБРАЗОВАНИЯ СИГНАЛОВ В БАЗИСЕ ДВУМЕРНЫХ ФУНКЦИЙ
Authors: Дудкин А. А. , Мачнев Мачнев Г. , Селиханович А. М.
Published: February 2021

Предлагается параллельный алгоритм дискретного ортогональ­ного преобразования двумерных сигналов, который отличается от известных меньшим числом операций умножений и сложений и тем, что коэффициенты преобразования вычисляются за один раз без использования одномерных преобразо­ваний. Кроме того, алгоритм удобен для СБИС-­реализации. На базе этого алгоритма преложены алгоритмы выделения контуров на полутоновых изображениях.
ИСПОЛЬЗОВАНИЕ КОРРЕЛЯЦИОННО-РЕГРЕССИОННОГО АНАЛИЗА ДЛЯ РЕШЕНИЯ ЗАДАЧИ КОММИВОЯЖЕРА
Authors: Шуть Василий , Прожерин Игорь
Published: February 2021

Рассматривается задача о коммивояжере и ее решение методом корреляционно­-регрессионного анализа. Приводится математическая модель данного метода, который позволяет решить задачу при использовании минимального объёма памяти n2+3n и имеет малую трудоёмкость ~o(n).
КОМПОНЕНТНІ МАГНІТНІ Й ЕЛЕКТРИЧНІ ДАВАЧІ ДЛЯ ДІАГНОСТИКИ СТОХАСТИЧНИХ ЕЛЕКТРОМАГНІТНИХ ПОЛІВ
Authors: Нічога Віталій , Дікмарова Людмила , Дуб Петро , Качмарек Здзіслав
Published: February 2021

Розглянуто систему давачів апаратури, що застосовується для визначення параметрів стохастичних електромагнітних полів. Система призначена для підводного електромагнітного зондування на морському шельфі. Описані нові високочутливі індукційні давачі, розроблені для цих цілей. Запропонована нова методика визначення орієнтації давачів на морському дні.
METADATA - A KEY TO DATA ACQUISITION AND INFORMATION RETRIEVAL
Authors: Reusch Peter J. A.
Published: February 2021

Metadata have a long tradition in several areas. Metadata help to structure data or documents and to classify the contents. Today it is important to recognize the power of existing metadata (thesauri, classifications, ...) and to use such metadata and to expand such metadata for advanced data acquisition and information retrieval. To expand metadata modern learning sys­ tems should be used and an open communication model, that integrates different aspects of data and metadata. Results of an information retrieval project are described and a new multi­level model of metadata is introduced.
ПРИМЕНЕНИЕ НЕЙРОННОЙ СЕТИ “НЕОКОГНИТРОН” ДЛЯ ОБРАБОТКИ ИЗОБРАЖЕНИЙ ИНТЕГРАЛЬНЫХ СХЕМ
Authors: Ваткин М.Е. , Садыхов Р.Х.
Published: February 2021

Рассматриваются архитектура нейронной сети “неокогнитрон” в задачах поиска структурных элементов на полутоновых изображениях интегральных схем. Предлагается модифицированное правило активации нейронов инвариантное к искажениям яркости изображения. Сравнительные результаты применения нейронной сети показали преимущество использования нейросетевого подхода.
APPROACHES TO MODELING OF COGNITIVE EVOLUTION
Authors: Red’ko Vladimir G.
Published: February 2021

Approaches to modeling of cognitive evolution that is evolution of animal cognitive abilities are proposed and discussed. Backgrounds of models of cognitive evolution, that are developed in an area of researches “Adaptive behavior”, in which modeled “organisms” adapting to variable environment are studied, are outlined. Initial steps of modeling of cognitive evolution are characterized. The sketch program for future investigations of cognitive evolution is proposed.
THE METHOD OF SOFTWARE RELIABILITY EVALUATION AND PREDICTION BASED ON THE MODEL WITH DYNAMIC INDEX OF PROJECT SIZE
Authors: Seniv Maksym , Yakovyna Vitaliy , Chabanyuk Yaroslav , Fedasyuk Dmytro
Published: February 2021

The software reliability evaluation and prediction method based on the model with dynamic index of project size is examined in this article. The support decision making procedure during software production which is based on the adequacy criterion of software testing is developed. The example of developed method based on testing data of industrial software product is proposed.
AN IMPROVED SELECTION TECHNIQUE FOR FAST PACKET ROUTING IN COMPUTER NETWORK
Authors: Akinwale A.T. , Adebayo A.A. , Folorunso O. , Adebayo G.A.
Published: February 2021

Contention Awareness Input Selection (CAIS) technique was recently compared with traditional input selection techniques such as First Come First Serve (FCFS) and Round Robin (RR) as a way of improving routing techniques and proved to outperform the two techniques but not widely accepted because of packet starvation problem. In this paper, Contention-Age Input Selection (CAGIS) technique was designed to correct the shortcoming of CAIS. CAIS was modified by including flattening algorithm, treap and queueing theory to improve packet access and control contention age level in the input buffer. The design involved three phases of setting up the network topology using the network objects, initiating of an event scheduler and traffic sources to start/stop transmitting packet through the event scheduler which was implemented using object-oriented tool commands language of script simulation program. The paper concluded that whilst deterministic XY routing scheme and the adaptive Odd-Even routing algorithm were used with CAGIS and CAIS to compare their performance, it was observed that CAGIS performed better under the three synthetic traffic patterns of uniform, transpose and hotspot. The results has improved significantly on contention age level of packets in the input buffer by uniformly distributing them to output ports.
ENERGY EFFICIENT BANDWIDTH CONSTRAINED QOS ENABLED MULTIPATH ROUTING FOR MANETs
Authors: Sumathi N. , Selvadoss Thanamani Antony
Published: February 2021

In mobile adhoc networks, node has a finite and decreasing energy. Energy saving mechanism is important for the efficient operation of the battery powered network. When a node is transmitting a packet, all the neighboring nodes overhear. Overhearing improves routing efficiency but consumes more energy. Overhearing is caused by the fact that when a unicast transmission is carried out in a node’s immediate neighborhood, it does not have any mechanism for not to receive that packet. Energy consumption during overhearing is same as that during reception. Goal of this work is to develop an energy conserving mechanism due to overhearing. To minimize energy, probability method is applied to randomly select number of overhearing nodes. This probability based overhearing is incorporated into log based pipelined ABM (Available Bandwidth Measurement) and integrated with AOMDV routing protocol. This proposed approach is implemented in NS2 simulator. Simulation results are presented to demonstrate the performance metrics such as throughput, packet delivery ratio, end-end latency, routing overhead, energy and bandwidth consumption of the network.
HYBRID INDOOR TRACKING OF HUMANS IN HAZARDOUS ENVIRONMENTS
Authors: Fink Andreas , Beikirch Helmut
Published: February 2021

The reliable tracking of humans and materials in indoor scenarios is an ongoing research issue. For example, the monitoring of humans in partially hazardous environments – like the surroundings of an underground longwall mining infrastructure – is crucial to save human lives. A centroid location estimation technique based on received signal strength (RSS) readings offers a well known and low-cost tracking solution in such a rough environment where many other systems with optical, magnetical or ultrasound sensors fail. Due to signal fading the RSS values alone cannot ensure a precise tracking. The sensor fusion of the RSS-based localization with an inertial navigation system (INS) leads to a more precise tracking. The long-term stability of the RSS-based localization and the good short- term accuracy of the INS are combined using a Kalman filter. The experimental results on a motion test track show that a tracking of humans in multipath environments is possible with low infrastructural costs.
AUTOMATED NETWORK PROTOCOL EVALUATION – THE POTSDAM WIRELESS TESTBED
Authors: Fudickar Sebastian J.F. , Schnor Bettina
Published: February 2021

The Potsdam Wireless Testbed supports validation and evaluation of Wi-Fi radio stacks and wireless applications in environments with heterogeneous hardware. In contrast to simulators, wireless testbeds support the network stack validation with specific radio chipsets and radio signal propagations. Furthermore, wireless testbeds unburden programmers from manually updating software on nodes. Scheduled test-runs are executed automatically for a defined duration including compilation and deployment of the protocols and measurement scripts as well as collection of measurement results and log files. The testbed supports heterogeneous processor architectures and radio chipsets via internal cross compilation. The developer can overview the visualized results of its validation and therefore can focus on the code and the results. Next to the support of several device and processor architectures, the Potsdam Wireless Testbed is intended to support additional radio frequency ranges as well as mobile device.
RFID BASED APPLICATIONS IN CULTURE, MEDIA AND CREATIVE INDUSTRIES
Authors: Bergemann Bergemann , Kuehn Eileen , Reinhardt Jens , Sieck Juergen
Published: February 2021

This article presents two different approaches to visualise information from culture, media and creative industries by using RFID based tracking and identification. Besides the required RFID backend, the paper also introduces the information system built on top of the backend. The first approach is based on passive RFID whereas the second uses active RFID. In particular, the differences in the processing of system events, delivery of needed information and the implemented infrastructure will be discussed and evaluated.
GATEWAY ARCHITECTURES FOR HOME CARE APPLICATIONS USING WIRELESS SENSOR NETWORKS
Authors: Sikora Axel , Braun Nathan , Jaeckel Steffen , Jaeckle Daniel
Published: February 2021

Home Care Applications and Ambient Assisted Living become increasingly attractive. This is caused as well by market pull, as the number of elderly people grows monotonously in highly-developed countries, as well as by technology push, as technological advances and attractive products pave the way to economically advantageous offerings. However, a significant number of challenges remain for real-life applications. Those include the lack of sufficiently standardized and interoperable solutions and thus, the necessity of gateways for integrated solutions, restrictions of the energy budgets, and scalability of solutions with regard to cost and network size. This paper presents the experience from the inCASA project (Integrated Network for Completely Assisted Senior Citizen’s Autonomy), where architectures for heterogeneous physical and logical communication flow are examined.
NONLINEAR DATA CODING IN WIRELESS SENSOR NETWORKS
Authors: Yatskiv Vasyl , Jun Su , Yatskiv Nataliya , Sachenko Anatoly
Published: February 2021

A proposed nonlinear method of sensors data coding allows to increase the useful capacity of the data transmission protocols for Wireless Sensor Networks in 3 – 5 times by the integration of sensors data with different digits capacity and reducing the transmissions number.
INFLUENCE LEARNING FOR MULTI-AGENT SYSTEM BASED ON REINFORCEMENT LEARNING
Authors: Kabysh Anton , Golovko Vladimir , Lipnickas Arunas
Published: February 2021

This paper describes a multi-agent influence learning approach and reinforcement learning adaptation to it. This learning technique is used for distributed, adaptive and self-organizing control in multi-agent system. This technique is quite simple and uses agent’s influences to estimate learning error between them. The best influences are rewarded via reinforcement learning which is a well-proven learning technique. It is shown that this learning rule supports positive-reward interactions between agents and does not require any additional information than standard reinforcement learning algorithm. This technique produces optimal behavior of multi-agent system with fast convergence patterns.
SELF-ORGANIZING MAP BASED VISUALIZATION TECHNIQUES AND THEIR ASSESSMENT
Authors: Sirola Miki , Talonen Jaakko
Published: February 2021

Our research group has been studying data-analysis based techniques in decision support and visualization. We had a long industrial research project in co-operation with a Finnish nuclear power plant Olkiluoto. We developed many decision support schemes based on Self-Organizing Map (SOM) method combined with other methodologies. Also several visualizations based on variou s data-analysis methods were developed. Data from the Olkiluoto plant and training simulator was used in the analysis. In this paper some of these visualizations are presented, analyzed, and assessed with a psychological framework. Measuring the information value of the visualizations is a real challenge. The developed visualizations and visualization techniques are also compared with some existing visualizations and techniques in current plants and research laboratories. The visualizations and the visualization techniques are developed further, and completely new visualizations and techniques are developed. We point out what additional value the new visualization techniques can produce. A detailed test case of using Self-Organizing Map (SOM) method with Olkiluoto plant data is presented. With this practical example the information value of this method is shown, and it is also pointed out how it can be assessed, and what are the most reliable criteria in this assessment.
SIMULATIONS OF PROCESSORS FOR FORMING ANDDIGITAL SIGNAL PROCESSING IN COMPUTER SYSTEMS WITH OPEN OPTICAL CHANNELS
Authors: Grynchyshyn Taras , Nykolaychuk Yaroslav
Published: February 2021

In this article new methodology of unsurplus signals code of bit-oriented informative streams is offered with the use of codes of the field of Galois, that considerably optimizes and improves the known methods of the digital working of data with an exposure and correction of errors, and effectively realized on the basis of the offered structures of the special processors of digital treatment of signals.
THE METHOD OF WAVE LETS CONSTRUCTION BY TRANSFORMATION OF A GRAPH OF POWER FUNCTION FOR EDGE DETECTION
Authors: Polyakova Marina , Krylov Victor , Volkova Natalya
Published: February 2021

In this paper the method to construct the improved wavelets by transformation of a graph of power function is developed for the edge detection.
INTEGRATED EFFECT OF DATA CLEANING AND SAMPLING ON DECISION TREE LEARNING OF LARGE DATA SETS
Authors: Patil Dipak V. , Bichkar Rajankumar S.
Published: February 2021

The advances and use of technology in all walks of life results in tremendous growth of data available for data mining. Large amount of knowledge available can be utilized to improve decision-making process. The data contains the noise or outlier data to some extent which hampers the classification performance of classifier built on that training data. The learning process on large data set becomes very slow, as it has to be done serially on available large datasets. It has been proved that random data reduction techniques can be used to build optimal decision trees. Thus, we can integrate data cleaning and data sampling techniques to overcome the problems in handling large data sets. In this proposed technique outlier data is first filtered out to get clean data with improved quality and then random sampling technique is applied on this clean data set to get reduced data set. This reduced data set is used to construct optimal decision tree. Experiments performed on several data sets proved that the proposed technique builds decision trees with enhanced classification accuracy as compared to classification performance on complete data set. Due to use of classification filter a quality of data is improved and sampling reduces the size of the data set. Thus, the proposed method constructs more accurate and optimal sized decision trees and it also avoids problems like overloading of memory and processor with large data sets. In addition, the time required to build a model on clean data is significantly reduced providing significant speedup.
GENERAL MODEL FOR ORGANIZING INTERACTIONS IN MULTI-AGENT SYSTEMS
Authors: Kabysh Anton , Golovko Vladimir
Published: February 2021

In this article we describe a model for finding optimal for learning the behavior of a group of agents in a collaborative multiagent setting. This model contains a set of scalable techniques that organize behavior of a multi- agent system. As a basis we use the framework of coordination graphs which exploits the dependencies between agents to decompose the global payoff function into a sum of local terms. To estimate a quality of interactions between agents we are using the concepts of the influence value learning paradigm. In last section we present the implementation of the considered model via reinforcement learning and experimental results of the use of this paradigm.
FORMATION AND PROCESSING OF ENTROPY-MANIPULATED CORRECTING SIGNAL CODES IN WIRELESS SENSOR NETWORKS
Authors: Voronych Artur , Nykolaychuk Yaroslav , Hladyuk Volodymyr
Published: February 2021

It is described a systematization of theoretical bases and analytical expressions for evaluation of entropy measures. It is showed a characteristic of signal corrective codes of Galois field. It is described the use of entropic manipulation and corrective signal codes in wireless sensor networks. It is founded that entropy approach of applying the corrective codes of Galois field is the most effective and promising for the formation and processing of signals during transmission in wireless sensor networks.
THE METHOD OF BUILDING OF BEHAVIOR MODEL OF NON-MARKOV COMPLEX SYSTEMS AS A GRAPH OF STATES AND TRANSITIONS
Authors: Volochiy Bohdan Y. , Ozirkovskyy Leonid D. , Kulyk Ihor W.
Published: February 2021

The goal of the paper is the construction of behavior models of discrete-continuous stochastic systems of non-Markov type by the method of Erlang’s phases. The improved method of formalized construction of behavior models as a graph of states and transitions is developed. Since this method is a part of technology modeling discrete-continuous stochastic systems, now the process of construction of behavior systems of non-Markov type is automated.
MONALISA: A MONITORING FRAMEWORK FOR LARGE SCALE COMPUTING SYSTEMS
Authors: Dobre Ciprian , Voicu Ramiro , Legrand Iosif C.
Published: February 2021

The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks (such as network monitoring, resource accounting) using real time monitoring information. MonALISA includes a coherent set of network management services to collect in near real-time information about the network topology, the main data flows, traffic volume and the quality of connectivity. A set of dedicated modules were developed in the MonALISA framework to periodically perform network measurements tests between all sites. We developed global services to present in near real-time the entire network topology used by a community. The time evolution of global network topology is shown in a dedicated GUI. Changes in the global topology at this level occur quite frequently and even small modifications in the connectivity map may significantly affect the network performance. The global topology graphs are correlated with active end-to-end network performance measurements, done using the Fast Data Transfer application, between all sites. Access to both real-time and historical data, as provided by MonALISA, is also important for developing services able to predict the usage pattern, to aid in efficiently allocating resources globally. For resource accounting, MonALISA collects information regarding the amounts of resources consumed by the users, which represent virtual organizations in a large scale distributed system. Besides providing statistical information, an accounting system can also be the base for managing distributed resources upon an economic model. In the MonALISA monitoring framework we developed modules that provide accounting facilities, collecting information from cluster managers like Condor, PBS, LSF and SGE. The usage statistic s is used for an intelligent management of the resources.
INTERACTIVE ENVIRONMENT FOR MASSIVE NEUROSCIENCE SIMULATIONS IN GRID
Authors: Salnikov Andrii , Sudakov Oleksandr , Levchenko Roman , Sliusar Ievgen , Savchenko Anton
Published: February 2021

End-user oriented system for massive computations in grid is proposed and implemented. The system provides support of user interfaces for input and output data staging, asynchronous jobs submission and control, tasks status and results monitoring. The main system components include web-portal, authentication components and jobs submitter that interact with grid infrastructure. The main advantages of described grid-portal are flexibility in computations back-ends support and possibility to interactively handle thousands of jobs. The proposed integrated environment was implemented in Ukrainian National Grid (U NG) infrastructure for massive simulations of non-linear dynamics in neuroscience.
INTEGRATION OF BACNET OPC UA-DEVICES USING A JAVA OPC UA SDK SERVER WITH BACNET OPEN SOURCE LIBRARY IMPLEMENTATION
Authors: Anantalapochai Naksit , Sikora Axel , Eberlein David , Kunz Dominique-Stephan
Published: February 2021

The variety of technologies used in modern Building Automation Systems (BAS) calls for methods to support interoperability of the devices from different technologies and vendors. OLE for Process Control Unified Architecture (=OPC UA) provides the possibility to enable secure inter operability of devices with platform independence and efficient information model features. However, OPC has not found broad space in the world of building automation, yet. In this paper, results and experiences from a project are presented, where BACnet devices are mapped to OPC UA standard models. The values and controls are presented by the OPC UA server running on an embedded device. In this paper, we map the BACnet information models into the corresponding OPC UA information models. The actual data (in OPC UA form) of the BACnet devices can be accessed by connecting an OPC UA Clients to the OPC UA Server. This objective was be pursued by using as many available open-source projects as possible.
USE OF CONTINUOUS CELLULAR AUTOMATA FOR SIMULATION OF THERMAL CONDUCTIVITY IN SYSTEMS WITH FIRST ORDER PHASE TRANSITION
Authors: Zhikharevich Vladimir , Shumуlyak Liliya
Published: February 2021

This paper is devoted to the use of cellular automata model for the study of some underlying physical processes. The basic approach and general methodology for the development of cellular automata models are examined through the example of heat transfer processes. It is shown that these models can be an alternative to the use of classical differential equations. It is proven that the model as a system of cellular automata is a good tool for the study of nonlinear problems of heat transfer and can describe a very complex behaviour of a system, despite the simplicity of its description.
OPTIMIZATION OF ASSOCIATION RULES FOR TUBERCULOSIS USING GENETIC ALGORITHM
Authors: Asha T. , Natarajan S. , Murthy K.N.B.
Published: February 2021

Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium Tuberculosis which usually spreads through the air and attacks low immune bodies. Human Immuno deficiency Virus (HIV) patients are more likely to be attacked by TB. It is an important health problem around the world including India. Association Rule Mining is the process of discovering interesting and unexpected rules from large sets of data. This approach results in huge quantity of rules where some are interesting and others are repetitive. It also limits the quality of rules to only two measures support and confidence. In this paper we try to optimize the rules generated by Association Rule Mining for Tuberculosis using Genetic Algorithm. Our approach is to extract only a small set of high quality Tuberculosis rules among the larger set using Genetic Algorithm. In the current approach datatypes such as discrete, continuous and categorical items have been handled. The proposed experimental result includes a small set of converged TB rules that helps doctors in their diagnosis decisions. The main motivation for using Genetic Algorithms in the discovery of high-level prediction rules is that they are robust, use adaptive search techniques that perform a global search on the solution space and cope better with attribute interaction than the greedy rule induction algorithms often used in data mining.
A PARALLEL TEMPLATE FOR IMPLEMENTING FILTERS FOR BIOLOGICAL CORRELATION NETWORKS
Authors: Dempsey Kathryn , Ufimtsev Vladimir , Bhowmick Sanjukta , Ali Hesham
Published: February 2021

High throughput biological experiments are critical for their role in systems biology – the ability to survey the state of cellular mechanisms on the broad scale opens possibilities for the scientific researcher to understand how multiple components come together, and what goes wrong in disease states. However, the data returned from these experiments is massive and heterogeneous, and requires intuitive and clever computational algorithms for analysis. The correlation network model has been proposed as a tool for modeling and analysis of this high throughput data; structures within the model identified by graph theory have been found to represent key players in major cellular pathways. Previous work has found that network filtering using graph theoretic structural concepts can reduce noise and strengthen biological signals in these networks. However, the process of filtering biological network using such filters is computationally intensive and the filtered networks remain large. In this research, we develop a parallel template for these network filters to improve runtime, and use this high performance environment to show that parallelization does not affect network structure or biological function of that structure.
MULTIRESOLUTION RENDERING BASED ON GPGPU COMPUTING
Authors: Lamas-Rodríguez Julián , Argüello Francisco , Heras Dora B.
Published: February 2021

The problem of visualizing large volumetric datasets is appealing for computation on the GPU. Nevertheless, the design of GPU volume rendering solutions must deal with the limited available memory in a graphics card. In this work, we present a system for multiresolution volume rendering which preprocesses the dataset dividing it into bricks and generating a compressed version by applying different levels of compression based on wavelets. The compressed volume is then stored in the GPU memory. For the later visualization process by texture mapping each brick of the volume is decompressed and rendered with a different resolution level depending on its distance to the camera. This approach computes most of the tasks in the GPU, thus minimizing the data transfers among CPU and GPU. We obtain competitive results for volumes of size in the range between 64 and 256.
SPOT PRICE PREDICTION FOR CLOUD COMPUTINGUSING NEURAL NETWORKS
Authors: Turchenko Volodymyr , Shults Vladyslav , Turchenko Iryna , Wallace Richard M. , Sheikhalishahi Mehdi , Vazquez-Poletti Jose Luis , Grandinetti Lucio
Published: February 2021

Advances in service-oriented architectures, virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on such systems results in commodity bidding for computing time. Amazon institutionalizes this bidding for its Elastic Cloud Computing (EC2) environment. Similar bidding methods exist for other cloud-computing vendors as well as multi–cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we will discuss vendors who provide spot pricing and bidding and present the predictive models for future short-term and middle-term spot price prediction based on neural networks giving users a high confidence on future prices aiding bidding on commodity computing.
ABSTRACTION CHECKPOINTING LEVELS: PROBLEMS AND SOLUTIONS
Authors: Meroufel Bakhta , Belalem Ghalem
Published: February 2021

A common approach to guarantee an acceptable level of fault tolerance in scientific computing is the checkpointing. In this strategy: when a task fails, it is allowed to be restarted from the recently checked pointed state rather than from the beginning, which reduces the system loss and ensures the reliability. Several systems use the checkpointing to ensure the fault tolerance such as HPC, distributed discrete event simulation and Clouds. The literature proposes several classifications of checkpointing techniques using different metrics and criteria. In this paper we focus on the classification based on abstraction level. In this classification the checkpointing is categorized into two principal types: application level and system level. Each of these levels has its advantages and suffers from many problems. The difference between our present paper and the others surveys proposed in the literature is that: in this paper we will study each level in details. We will also study and analyze some works that propose solutions to solve the problems and exceed the limits of each abstraction level.
PRECISE SYNCHRONISATION IN INDUSTRIAL SYSTEM USING IEEE 1588 PRECISE TIME PROTOCOL
Authors: Haasz Vladimir , Roztocil Jaroslav , Breuer Jan , Vigner Vojtěch
Published: February 2021

Nowadays there is a frequent demand to increase the quality of synchronization in industrial distributed systems. Node synchronization can be implemented in the Ethernet network using time protocols, e.g. IEEE 1588 Precise Time Protocol (PTP). However, active network components like switches and routers have negative influence on achieved accuracy of synchronization because they affect a packet delay variation in the network. To suppress this effect, special (but expensive) industrial switches, with a packet propagation delay correction, can be used. Another way using either low cost GPS receivers for synchronization of local clocks or a special timing for data transfer is described in the paper. It will allow a cheaper solution that preserves high quality of synchronization. Besides, a device for packet delay measurement (PTP tester) was designed and developed to be possible to evaluate the proposed methods.
A NOVEL SIGNAL PROCESSING METHOD BASED ON FREQUENCY MODALITY FOR INTRA-BODY MEDICAL INSTRUMENT TRACKING
Authors: Fotiadis Dimitrios A. , Papathanasiou Kostas , Astaras Alexandros , Bamidis Panagiotis D. , Kalfas Anestis
Published: February 2021

Measuring the position of a medical instrument inside the human body can be performed with various methods. One option is to measure the phase shift of the signal originating from a transmitter embedded into the tip of the medical instrument, determining its displacement with respect to a set of stationary receivers. The phase shift is converted into a low frequency voltage with the use of a Phased Locked Loop (PLL). This voltage can subsequently be converted into displacement, providing the position of the medical instrument in one (1D), two (2D) and three (3D) dimensions using trilateration. The instrument’s displacement can be defined in either the time or frequency domain. This paper presents a novel method for constant velocity displacement of the transmitter, using either the Locally Weighted Scatter-Plot Smoothing (LOWESS) curve fitting method or a Lomb-Scargle periodogram. The Lomb-Scargle periodogram is based on the least-squares power spectrum and can be used instead of waveform smoothing and measurement into the time domain, providing more precise and accurate measurement results as compared to LOWESS curve fitting method.
TOWARDS AUTOMATIC EPILEPTIC SEIZURE DETECTION IN EEGS BASED ON NEURAL NETWORKS AND LARGEST LYAPUNOV EXPONENT
Authors: Golovko Vladimir , Artsiomenka Svetlana , Kisten Volha , Evstigneev Victor
Published: February 2021

Over the past few decades, application of neural networks and chaos theory to electroencephalogram (EEG) analysis has grown rapidly due to the complex and nonlinear nature of EEG data. We report a novel method for epileptic seizure detection that is depending on the maximal short-term Lyapunov exponent (STLmax). The proposed approach is based on the automatic segmentation of the EEG into time segments that correspond to epileptic and non-epileptic activity. The STL-max is then computed from both categories of EEG signal and used for classification of epileptic and non-epileptic EEG segments throughout the recording. Neural network techniques are proposed both for segmentation of EEG signals and computation of STLmax. The data set from hospital have been used for experiments performing. It consists of 21 records during 8 seconds of eight adult patients. Furthermore the publicly available data were used for experiments. The main advantages of presented neural technique is its ability to detect rapidly the small EEG time segments as the epileptic or non-epileptic activity, training without desired data set about epileptic and non-epileptic activity in EEG signals. The proposed approach permits to detect exactly the epileptic and non-epileptic EEG segments of different duration and shape in order to identify a pathological activity in a remission state as well as detect a paroxysmal activity in a preictal period.
BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY
Authors: Gupta Sunanda , Arora Sakshi
Published: February 2021

Multi Dimensional Knapsack problem is a widely studied NP hard problem requiring extensive processing to achieve optimality. Simulated Annealing (SA) unlike other is capable of providing fast solutions but at the cost of solution quality. This paper focuses on making SA robust in terms of solution quality while assuring faster convergence by incorporating effective fitness landscape parameters. For this it proposes to modify the ‘Acceptance Probability’ function of SA. The fitness landscape evaluation strategies are embedded to Acceptance Probability Function to identify the exploitation and exploration of the search space and analyze the behavior on the performance of SA. The basis of doing so is that SA in the process of reaching optimality ignores the association between the search space and fitness space and focuses only on the comparison of current solution with optimal solution on the basis of temperature settings at that point. The idea is implemented in two different ways i.e. by making use of Fitness Distance Correlation and Auto Correlation functions. The experiments are conducted to evaluate the resulting SA on the range of MKP instances available in the OR library.
DATA ACQUISITION AND DATA PROCESSING CHALLENGES IN HEAVY METAL MEASUREMENTS
Authors: Dias Pereira José Miguel , Salgado Ricardo Manuel Nunes
Published: February 2021

Water quality is a key factor to preserve human life quality, as well as, environmental and biological ecosystems. This paper highlights specific issues related with the acquisition and data processing of heavy metal measurement systems. Between the main challenges related with this kind of measurements, such as the ones related with the very low signal amplitudes to be measured, since very low concentrations, in the order of tens of p.p.b., of some heavy metals, can be very dangerous for human life and for ecosystems sustainability. Additional challenges, that are associated with online heavy metals measurements, are related with the capability to obtain accurate results using a low number of measurement points. Thus, the main goal of this paper is a comparison of different data processing algorithms that can be used to improve heavy metal measurement accuracy when a segmented voltammetric voltage scan is performed with a low number of measurement points. Regarding data processing of the measurement data, B-Spline, Gaussian and artificial neural network based techniques are compared with traditional least mean square techniques based on polynomial curve fitting. The performance of each technique is evaluated in terms of the required number of measurement points, for a given root mean square deviation between curve fitted and experimental data. A brief comparison of the different techniques, in terms of insensitivity to errors caused by measurement data outliers, is also presented.
DESIGN DETAILS OF A LOW COST AND HIGH PERFORMANCE ROBOTIC VISION ARCHITECTURE
Authors: Vourvoulakis John V. , Kalomiros John A. , Lygouras John N.
Published: February 2021

The implementation of an advanced real-time, low cost video processing platform capable of supporting a variety of demanding robotic applications is presented. The system is designed as an open project, accessible in full detail and has the potential to grow. It is based on a FPGA plus MCU architecture, allowing the implementation of combined fixed-point and 32-bit floating-point applications with optimized resource allocation. The presented platform is optimally integrated with appropriate controllers, like video-input frame grabbers for multiple camera applications, external SDRAM, as well as USB and VGA interfaces. The processing and interfacing capabilities of the proposed system are illustrated by implementing basic feature extraction and preprocessing tasks, achieving the display of processed video frames at a rate of 30 fps with resolution 640x480. The proposed architecture is evaluated in terms of resource usage, power consumption and cost. Potential applications are also discussed.
ADVANCED EXPERIMENTAL BIFURCATION ANALYSIS: BASELINE SCENARIO OF DYNAMICS EVOLUTION
Authors: Kolokolov Yury V. , Monovskaya Anna V.
Published: February 2021

The paper develops the advanced experimental bifurcation analysis, where the extended data acquisition, the intelligent data processing, and the modified bifurcation diagrams are combined to get useful information for different specified purposes. In particular, here we focus on how to estimate experimentally the nearest limit of the operating process stability loss without expert opinions. So, we consider the baseline scenario of dynamics evolution, where the peculiarity of the estimation is connected with the possibility to determine the uncertainty zone about the corresponding bifurcation points. The algorithms of both data acquisition and data processing are illustrated. The procedures of the algorithms are founded on the methods of the experimental researches of different nonlinear phenomena under uncertainties. The presented results can be applied to engineering design.
CANNY-BASED METHOD OF IMAGE CONTOUR SEGMENTATION
Authors: Zahorodnia Diana , Pigovsky Yuriy , Bykovyy Pavlo
Published: February 2021

The paper presents modification of the image contour segmentation method based on Canny method. A distinctive feature of proposed approach is the usage of wavelet-functions as underlining transformation. It allows to obtain a sequence of hierarchical object contour preparations with an adjusted detailing. This approach is a part of the object recognition information technology that significantly reduces the size of information processing of video surveillance system.
Classification of Mixed Odors Using A Layered Neural Network
Authors: Omatu Sigeru
Published: February 2021

Odor classification has been forcused due to one of five senses of human being. If we could establish the odor classification technology, we would expect various new technology since human being requires five sences to acheive higher quality information processing and sophistcated decision making. For example, we could expect the odor classification and odor synthesis, which enable us to achieve odor communication technology. Furthermore, the odor classification would be applicable to keep the society safe by detecting the dangerous odors and to make our life more satisfactory by using additional odor information. In this paper we develop an electronic nose using a neural network. The neural network is a multi-layered neural network based on the gradient method. After classifying the various odors, we consider the classification in case that mixed odors are measured. To improve the classification accuracy we adopt a genetic algorithm to find a reduction factor to separate two mixed odors.