“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Development and Deployment of Wireless Sensor Network in Paddy Fields of Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the development and deployment of wireless sensor network for crop monitoring in the paddy fields of Kuttanad, a region of Kerala, the southern state of India.

MicroRNA–mRNA interaction network using TSK-type recurrent neural fuzzy network

MicroRNAs are short non-coding RNAs that can regulate gene expression during various crucial cell processes such as differentiation, proliferation and apoptosis. Changes in expression profiles of miRNA play an important role in the development of many cancers, including CRC. Therefore, the identification of cancer related miRNAs and their target genes are important for cancer biology research. In this paper, we applied TSK-type recurrent neural fuzzy network (TRNFN) to infer miRNA–mRNA association network from paired miRNA, mRNA expression profiles of CRC patients. We demonstrated that the method we proposed achieved good performance in recovering known experimentally verified miRNA–mRNA associations. Moreover, our approach proved successful in identifying 17 validated cancer miRNAs which are directly involved in the CRC related pathways. Targeting such miRNAs may help not only to prevent the recurrence of disease but also to control the growth of advanced metastatic tumors. Our regulatory modules provide valuable insights into the pathogenesis of cancer

RMAC-M: Extending the R-MAC Protocol for an Energy Efficient, Delay Tolerant Underwater Acoustic Sensor Network application with a mobile data mule node

One of the major applications of underwater acoustic sensor networks (UWASN) is ocean environment monitoring. Employing data mules is an energy efficient way of data collection from the underwater sensor nodes in such a network. A data mule node such as an autonomous underwater vehicle (AUV) periodically visits the stationary nodes to download data. By conserving the power required for data transmission over long distances to a remote data sink, this approach extends the network life time. In this paper we propose a new MAC protocol to support a single mobile data mule node to collect the data sensed by the sensor nodes in periodic runs through the network. In this approach, the nodes need to perform only short distance, single hop transmission to the data mule. The protocol design discussed in this paper is motivated to support such an application. The proposed protocol is a hybrid protocol, which employs a combination of schedule based access among the stationary nodes along with handshake based access to support mobile data mules. The new protocol, RMAC-M is developed as an extension to the energy efficient MAC protocol R-MAC by extending the slot time of R-MAC to include a contention part for a hand shake based data transfer. The mobile node makes use of a beacon to signal its presence to all the nearby nodes, which can then hand-shake with the mobile node for data transfer. Simulation results show that the new protocol provides efficient support for a mobile data mule node while preserving the advantages of R-MAC such as energy efficiency and fairness.

A Generic Software Architecture for a Domain Specific Distributed Embedded System

In this paper, we have evolved a generic software architecture for a domain specific distributed embedded system. The system under consideration belongs to the Command, Control and Communication systems domain. The systems in such domain have very long operational lifetime. The quality attributes of these systems are equally important as the functional requirements. The main guiding principle followed in this paper for evolving the software architecture has been functional independence of the modules. The quality attributes considered most important for the system are maintainability and modifiability. Architectural styles best suited for the functionally independent modules are proposed with focus on these quality attributes. The software architecture for the system is envisioned as a collection of architecture styles of the functionally independent modules identified

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Optically transparent magnetic nanocomposites based on encapsulated Fe3O4 nanoparticles in a sol–gel silica network

Composite Fe3O4–SiO2 materials were prepared by the sol–gel method with tetraethoxysilane and aqueous-based Fe3O4 ferrofluids as precursors. The monoliths obtained were crack free and showed both optical and magnetic properties. The structural properties were determined by infrared spectroscopy, x-ray diffractometry and transmission electron microscopy. Fe3O4 particles of 20 nm size lie within the pores of the matrix without any strong Si–O–Fe bonding. The well established silica network provides effective confinement to these nanoparticles. The composites were transparent in the 600–800 nm regime and the field dependent magnetization curves suggest that the composite exhibits superparamagnetic characteristics

Spectroscopic and photoluminescence studies on optically transparent magnetic nanocomposites based on sol–gel glass: Fe3O4

Sol–gel glasses with Fe3O4 nanoparticles having particle sizes laying in the range 10–20 nm were encapsulated in the porous network of silica resulting in nanocomposites having both optical and magnetic properties. Spectroscopic and photoluminescence studies indicated that Fe3O4 nanocrystals are embedded in the silica matrix with no strong Si–O–Fe bonding. The composites exhibited a blue luminescence. The optical absorption edge of the composites red shifted with increasing concentration of Fe3O4 in the silica matrix. There is no obvious shift in the position of the luminescence peak with the concentration of Fe3O4 except that the intensity of the peak is decreased. The unique combinations of magnetic and optical properties are appealing for magneto–optical applications.

Re-Engineering Towed Arrays for Quality Enhancement - Network Based Towed Array

The Towed Array electronics is a multi-channel simultaneous real time high speed data acquisition system. Since its assembly is highly manpower intensive, the costs of arrays are prohibitive and therefore any attempt to reduce the manufacturing, assembly, testing and maintenance costs is a welcome proposition. The Network Based Towed Array is an innovative concept and its implementation has remarkably simplified the fabrication, assembly and testing and revolutionised the Towed Array scenario. The focus of this paper is to give a good insight into the Reliability aspects of Network Based Towed Array. A case study of the comparison between the conventional array and the network based towed array is also dealt with

A Compiler Integrated Assistance for Optimum Data Allocation in Banked Memory Embedded Processors

Bank switching in embedded processors having partitioned memory architecture results in code size as well as run time overhead. An algorithm and its application to assist the compiler in eliminating the redundant bank switching codes introduced and deciding the optimum data allocation to banked memory is presented in this work. A relation matrix formed for the memory bank state transition corresponding to each bank selection instruction is used for the detection of redundant codes. Data allocation to memory is done by considering all possible permutation of memory banks and combination of data. The compiler output corresponding to each data mapping scheme is subjected to a static machine code analysis which identifies the one with minimum number of bank switching codes. Even though the method is compiler independent, the algorithm utilizes certain architectural features of the target processor. A prototype based on PIC 16F87X microcontrollers is described. This method scales well into larger number of memory blocks and other architectures so that high performance compilers can integrate this technique for efficient code generation. The technique is illustrated with an example

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features

Framework for middleware executed on mobile devices

Die ubiquitäre Datenverarbeitung ist ein attraktives Forschungsgebiet des vergangenen und aktuellen Jahrzehnts. Es handelt von unaufdringlicher Unterstützung von Menschen in ihren alltäglichen Aufgaben durch Rechner. Diese Unterstützung wird durch die Allgegenwärtigkeit von Rechnern ermöglicht die sich spontan zu verteilten Kommunikationsnetzwerken zusammen finden, um Informationen auszutauschen und zu verarbeiten. Umgebende Intelligenz ist eine Anwendung der ubiquitären Datenverarbeitung und eine strategische Forschungsrichtung der Information Society Technology der Europäischen Union. Das Ziel der umbebenden Intelligenz ist komfortableres und sichereres Leben. Verteilte Kommunikationsnetzwerke für die ubiquitäre Datenverarbeitung charakterisieren sich durch Heterogenität der verwendeten Rechner. Diese reichen von Kleinstrechnern, eingebettet in Gegenstände des täglichen Gebrauchs, bis hin zu leistungsfähigen Großrechnern. Die Rechner verbinden sich spontan über kabellose Netzwerktechnologien wie wireless local area networks (WLAN), Bluetooth, oder UMTS. Die Heterogenität verkompliziert die Entwicklung und den Aufbau von verteilten Kommunikationsnetzwerken. Middleware ist eine Software Technologie um Komplexität durch Abstraktion zu einer homogenen Schicht zu reduzieren. Middleware bietet eine einheitliche Sicht auf die durch sie abstrahierten Ressourcen, Funktionalitäten, und Rechner. Verteilte Kommunikationsnetzwerke für die ubiquitäre Datenverarbeitung sind durch die spontane Verbindung von Rechnern gekennzeichnet. Klassische Middleware geht davon aus, dass Rechner dauerhaft miteinander in Kommunikationsbeziehungen stehen. Das Konzept der dienstorienterten Architektur ermöglicht die Entwicklung von Middleware die auch spontane Verbindungen zwischen Rechnern erlaubt. Die Funktionalität von Middleware ist dabei durch Dienste realisiert, die unabhängige Software-Einheiten darstellen. Das Wireless World Research Forum beschreibt Dienste die zukünftige Middleware beinhalten sollte. Diese Dienste werden von einer Ausführungsumgebung beherbergt. Jedoch gibt es noch keine Definitionen wie sich eine solche Ausführungsumgebung ausprägen und welchen Funktionsumfang sie haben muss. Diese Arbeit trägt zu Aspekten der Middleware-Entwicklung für verteilte Kommunikationsnetzwerke in der ubiquitären Datenverarbeitung bei. Der Schwerpunkt liegt auf Middleware und Grundlagentechnologien. Die Beiträge liegen als Konzepte und Ideen für die Entwicklung von Middleware vor. Sie decken die Bereiche Dienstfindung, Dienstaktualisierung, sowie Verträge zwischen Diensten ab. Sie sind in einem Rahmenwerk bereit gestellt, welches auf die Entwicklung von Middleware optimiert ist. Dieses Rahmenwerk, Framework for Applications in Mobile Environments (FAME²) genannt, beinhaltet Richtlinien, eine Definition einer Ausführungsumgebung, sowie Unterstützung für verschiedene Zugriffskontrollmechanismen um Middleware vor unerlaubter Benutzung zu schützen. Das Leistungsspektrum der Ausführungsumgebung von FAME² umfasst: • minimale Ressourcenbenutzung, um auch auf Rechnern mit wenigen Ressourcen, wie z.B. Mobiltelefone und Kleinstrechnern, nutzbar zu sein • Unterstützung für die Anpassung von Middleware durch Änderung der enthaltenen Dienste während die Middleware ausgeführt wird • eine offene Schnittstelle um praktisch jede existierende Lösung für das Finden von Diensten zu verwenden • und eine Möglichkeit der Aktualisierung von Diensten zu deren Laufzeit um damit Fehlerbereinigende, optimierende, und anpassende Wartungsarbeiten an Diensten durchführen zu können Eine begleitende Arbeit ist das Extensible Constraint Framework (ECF), welches Design by Contract (DbC) im Rahmen von FAME² nutzbar macht. DbC ist eine Technologie um Verträge zwischen Diensten zu formulieren und damit die Qualität von Software zu erhöhen. ECF erlaubt das aushandeln sowie die Optimierung von solchen Verträgen.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of the systems. We consider their underlying data structures – socalled folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures – so-called folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag cooccurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.