In this report we present an overview of the approaches and techniques that are used in the task of automatic audio segmentation. Audio segmentation aims to find changing points in the audio content of an audio stream. Initially, we present the basic steps in an automatic audio segmentation procedure. Afterwards, the basic categories of segmentation algorithms, and more specific the unsupervised, the data-driven and the mixed algorithms, are presented. For each of the categorizations the segmentation analysis is followed by details about proposed architectural parameters, such us the audio descriptor set, the mathematical functions in unsupervised algorithms and the machine learning algorithms of data-driven modules. Finally a review of proposed architectures in the automatic audio segmentation literature appears, along with details about the experimenting audio environment (heading of database and list of audio events of interest), the basic modules of the procedure (categorization of the algorithm, audio descriptor set, architectural parameters and potential optional modules) along with the maximum achieved accuracy.

This paper is a contribution for development of a high level of security for the Programmer Logic Controller (PLC). Many industrial adopt the redundant PLC architecture (or Standby PLC) designed to replace the failed (out of order) PLC without stopping associated automated equipments. We propose a formal method to choose a Standby PLC based on probability study, by comparing normal functioning to misbehavior one leading to residue generation process. Any generated difference reveals a presence of anomaly. The proposed method begins by listing all PLC components failures leading to their stopping according to failures criticalities. Two models; functional and dysfunctional are obtained by using formal specifications. Probability’s calculus of dysfunction of each Standby PLC is obtained by the sum of the probabilities of dysfunction of its critical components. These probabilities are allocated each transition which leads to the dysfunction in the dysfunctional model. The dysfunctional model is obtained by using the FMECA method (Failure Modes, Effects and Criticality Analysis). We shall see that this global vision of functioning of the whole PLC leads to a higher level of security where the chosen Standby PLC works continuously.

Swift advances in mobile communication technology have spawned almost unlimited new mobile applications. Mobile application development is an extremely well growing industry across the globe that created new opportunities of modern businesses and pioneered new technologies in the area. In order to build high quality mobile applications, it is imperative to understand the key characteristics that define mobile applications, which if wisely considered and implemented, can facilitate the delivery of truly exceptional, valuable and user friendly mobile apps that satisfy users’ needs. Only few scientific publications can be found which specifically identify the key characteristics and what makes mobile applications different from traditional software. For this purpose, we conducted an online survey from the mobile research and development community. The survey questions covered the entire mobile application development lifecycle starting from inception to the maintenance stage. This paper presents the survey results by classifying the key characteristics that differentiate mobile applications from traditional ones into three categories: Hardware, Software (application interaction, application development, and application security) and Communication. The study contributes towards a greater understanding of mobile software and the current trends in the mobile application development. It also highlights various features and attributes that assist in developing high quality mobile software applications.

Paddy drying process depends upon ambient conditions, paddy quality, temperature and mass of hot drying air. Existing techniques of paddy drying process are highly nonlinear. In this paper, a neural network based automated controller for paddy drying is designed. The designed controller manages the steam temperature and blower motor speed to achieve constant paddy drying time. A Layer recurrent neural network is adopted for the controller. Atmospheric conditions such as temperature and humidity along with the size of the paddy are used as input to the network. Experimental results show that the developed controller can be used to control the paddy drying process. Implementation of developed controller will help in controlling the drying time at almost constant value which will definitely improve the quality of rice.

The Marksheet Generator is flexible for generating progress mark sheet of students. This system is mainly based in the database technology and the credit based grading system (CBGS). The system is targeted to small enterprises, schools, colleges and universities. It can produce sophisticated ready-to-use mark sheet, which could be created and will be ready to print. The development of a marksheet and gadget sheet is focusing at describing tables with columns/rows and sub-column sub-rows, rules of data selection and summarizing for report, particular table or column/row, and formatting the report in destination document. The adjustable data interface will be popular data sources (SQL Server) and report destinations (PDF file). Marksheet generation system can be used in universities to automate the distribution of digitally verifiable mark-sheets of students. The system accesses the students’ exam information from the university database and generates the gadget-sheet Gadget sheet keeps the track of student information in properly listed manner. The project aims at developing a marksheet generation system which can be used in universities to automate the distribution of digitally verifiable student result mark sheets. The system accesses the students’ results information from the institute student database and generates the mark sheets in Portable Document Format which is tamper proof which provides the authenticity of the document. Authenticity of the document can also be verified easily.

Requirement satisfaction is one of the most important factors to success of software. All the requirements that are specified by the customer should be satisfied in every phase of the development of the software. Satisfaction assessment is the determination of whether each component of the requirement has been addressed in the design document. The objective of this paper is to implement two methods to identify the satisfied requirements in the design document. To identify the satisfied requirements, similar words in both of the documents are determined. The methods such as Naive satisfaction assessment and TF-IDF satisfaction assessment are performed to determine the similar words that are present in the requirements document and design documents. The two methods are evaluated on the basis of the precision and recall value. To perform the stemming, the Porter’s stemming algorithm is used. The satisfaction assessment methods would determine the similarity in the requirement and design documents. The final result would give a accurate picture of the requirement satisfaction so that the defects can be determined at the early stage of software development. Since the defects determines at the early stage, the cost would be low to correct the defects.

The unabated growth of the World Wide Web in the last decade and the increasing size of the websites have resulted in significant amount of research activity to improve the link structure of the websites. A website can be considered as a directed graph with webpages as nodes and hyperlinks as edges referred to as the Webgraph. Website structure optimization or reorganization is also considered as a graph optimization problem. The researchers have developed few models to optimize the website link structure. It is observed that the heuristic and mathematical models cannot optimize webgraphs of large size and are also time consuming. This paper presents an Ant Colony and local search based hybrid metaheuristic model for the newly emerged website structure optimization (WSO) problem. The developed hybrid model is also compared with Ant colony method and it is observed that the model performs better than Ant Colony System based approach.

The main idea of this work is object tracking using real time video processing. For this purpose we designed an embedded system that performs the object tracking algorithm for accurate tracking of defined object. The theme may be implemented for the security companies, sports and the armed forces to make them more equipped and advanced. The heart of the system is a Field Programmable Gate Arrays development kit. It controls the whole system by receiving the video signal from camera, processes it and sends the video signal to the Liquid Crystal Display or monitor. After receiving video of intended object, target selection is performed to select the target to track and then the tracking algorithm is implemented using image processing algorithms implemented using Field Programmable Gate Arrays development kit. We also interfaced the DC gear motor to control the movement of the camera in order to track the selected object. In order to design the standalone application we transformed our algorithm in Field Programmable Gate Array kit.

Data mining has become a process of significant interest in recent years due to explosive rate of the accumulation of data. It is used to discover potentially valuable implicit knowledge from the large transactional databases. Association rule mining is one of the well known techniques of data mining. It typically aims at discovering associations between attributes in the large databases. The first and the most influential traditional algorithm for association rule discovery is Apriori. Multiple scans of database, generation of large number of candidates item set and discovery of interesting rules are the main challenging issues for the improvement of Apriori algorithm. Therefore in order to decrease the multiple scanning of database, a new method of association rule mining using pattern generation is proposed in this paper. This method involves three steps. First, patterns are generated using items from the transaction database. Second, frequent item set is obtained using these patterns. Finally association rules are derived. The performance of this method is evaluated with the traditional Apriori algorithm. It shows that behavior of the proposed method is much more similar to Apriori algorithm with less memory space and reduction in multiple times scanning of database. Thus it is more efficient than the traditional Apriori algorithm.

Feature selection is one of the most important preprocessing steps in data mining and knowledge Engineering. In this short review paper, apart from a brief taxonomy of current feature selection methods, we review feature selection methods that are being used in practice. Subsequently we produce a near comprehensive list of problems that have been solved using feature selection across technical and commercial domain. This can serve as a valuable tool to practitioners across industry and academia. We also present empirical results of filter based methods on various datasets. The empirical study covers task of classification, regression, text classification and clustering respectively. We also compare filter based ranking methods using rank correlation.