Robotics. Soft Computing

This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes' theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

In this paper a study of time frequency analysis of non stationary signals using Hilbert-Huang Transform (HHT) is presented. The conventional time frequency analysis or localization methods suffer the limitations caused by Heisenberg's uncertainty principle which causes difficult in further improvement of accuracy and efficient for the detection and parameter estimation of signals. Hilbert-Huang Transform (HHT) is being introduced for signal processing with intrinsic time scales rather than predefined basic functions to decompose non stationary signals sonar or radar signals. The motivation behind this method is that its ability to decompose the multi frequency component signals into Intrinsic Mode Functions (IMF) with the help of Empirical Mode Decomposition (EMD). The signal frequency component is detected by finding the locations of the peaks in the parameter domain.

Fever has different causes and types, but with similar symptoms. Therefore, making fever diagnosis with human physiological symptoms more complicated. This research project delves into the design of a web based expert multi-fever diagnosis system using a novel fuzzy symptom classifier with human self-observed physiological symptoms. Considering malaria, Lassa, dengue, typhoid and yellow fever. The fuzzy-symptom classifier has two stages. Fist stage is fever type confirmation using common fever symptoms, leading to five major fuzzy rules and the second phase is determining the level of infection (severe or mild) of the confirmed type of fever using unique fever symptoms. Furthermore, Case studies during the system implementation yielded data collected from 50 patients of having different types of fever. The analysis clearly shows the effectiveness and accuracy in the system performance through false result elimination. In addition, acceptability of the system was investigated through structured questionnaire administered to same 50 patients. This result clearly indicates that the system is well accepted, by users and considered fairly easy to use, time and cost saving. Index Terms-Fuzzy classifier, fever diagnosis, multi fever, expert fever diagnosis. received his masters in computer science degree from Vels University India, in 2016 may. While he obtained his bachelor's in computer science from Delta State University. His research interest are in artificial intelligence, Human computer Interaction, health and business information systems, Trust, Social and security computing, and computer vision application. He is currently the head of information technology department of Wetland Microfinance Bank Nigeria Ltd,

Reliability is one of the key attributes of software product quality. Capability for accurate prediction of reliability will allow software product industry to have better market acceptability and enable wider usage in high integrity or critical applications domains for their product. Software Reliability analysis is performed at various stages during software product development life cycle. Popular software reliability prediction models proposed in literature are targeted to specific phases of life cycle with certain identified parameters. However, these models seem to have certain limitations in predicting software reliability in an accurate and acceptable manner to the industry. A recent industrial survey performed by the authors identified several factors which practitioners perceived to have influence in predicting reliability. Subsequently we conducted a set of experiments involving diverse domains and technologies to validate the perceived influence of the identified paramete...

In recent times Massive Multiplayer Online Game has appeared as a computer game that enables hundreds of players from all parts of the world to interact in a game world (common platform) at the same time instance. Current architecture used for MMOGs based on the classic tightly coupled distributed system. While, MMOGs are getting more interactive same time number of interacting users is increasing, classic implementation architecture may raise scalability and interdependence issues. This requires a loosely coupled service oriented architecture to support evolution in MMOG application. Data flow architecture, Event driven architecture and client server architecture are basic date orchestration approaches used by any service oriented architecture. Real time service is hottest issue for service oriented architecture. The basic requirement of any real time service oriented architecture is to ensure the quality of service. In this paper we have proposed a service oriented architecture fo...

This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes' theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

Hepatitis is a soreness of the liver tissue. The term hepatitis originates from the Greek words: hêpar (ἧπαρ), which means "liver"; and -itis (-ῖτις), connoting "inflammation" [1]. Though some persons with hepatitis may not show signs or symptoms, some may develop symptoms like yellow staining of the skin and whites of the eyes, poor desire for food, diarrhea, abdominal pain, vomiting and tiredness. Hepatitis disease could be serious if

This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes ’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

There is an increasing community of researchers trying to draw attention to the importance of evaluation issues. Some approaches can be followed to evaluate the quality of software, but gives no guidance on how to construct a high quality software product and on how to use in practice this model across evaluation process. IS are built upon Software, and the quality of Software effects, if not determine the quality of the IS. There are several difficulties of developing a comprehensive model of IS quality. The concepts, models and measures that work in other fields might be usefully applied to the IS field, but careful analysis and consideration should be given. This paper describes a practical method that can be used to evaluate the expected quality of IS. Our approach is focused on a direct evaluation of the quality of an IS. Our proposed IS quality model is considered as structured set of properties (such as reliability, maintainability, and so on). These properties are usually presented as a hierarchy of statements. The uppermost ones represent the most general properties, suitable to be understood by the widest variety of people, but very difficult to measure directly. The deeper we sink into this hierarchy, the more technical-audience-oriented and measurable properties are found. These finals properties need to be measured using metrics. For this purpose a metrics measurement-based framework is linked to the defined quality model.

Automatic Speech Recognition (ASR) is a thriving technology, which permits an application to detect the spoken words or the speaker using computer. This work isfocused on speech recognition for children with Asperger's Syndrome who belongs to the age group of five to ten. Asperger's Syndrome (AS) is one of the spectrum disorders in Autism Spectrum Disorder (ASD) in which most of affected individuals have high levelof IQ and sound language skills. But thespectrum disorder restricts for communication and social interaction. The implementation of this workaccomplished through two different stages. In the first stage, an attempt is made to develop an Automatic Speech Recognition system, to identify the articulation disorder inchildren with Aspergers' Syndrome (AS) on the basis of 14 Malayalam vowels. A deep autoencoder is used for pre-training in an unsupervised manner with 27,300 features that includes Mel-Frequency Cepstral Coefficients (MFCC), Zero Crossing, Formants 1, Formants 2 and Formants 3. An additional layer is added and fine-tuned to predict the target vowel sounds, which enhance the performance of the auto-encoder. In the second stage, Learning Assistive System for Autistic Child (LASAC)is envisioned to provide speech training as well as impaired speech analysis for Malayalam vowels. A feature vector is used to create the acoustic model and the Euclidean Distance formula is used to analyze the percentage of impairment in AS student's speech by comparing their speech features against the acoustic model.LASAC provides an interactive interface that offers speech training, speech analysis and articulation tutorial. Most of the autistic students have difficulty to memorize words or letters but they are able to recollect pictures. This serves as a learning assistive system for Malayalam letters with their corresponding images and words. The DNN with auto-encoder has achieved an average accuracy of 65% with the impaired (autistic) dataset.

Lassa fever is an acute viral haemorrhagic fever caused by the Lassa virus and first discovered in 1969 in Lassa, a town in Borno State, Nigeria though it has been in existence since 1950s. Lassa virus belong to a member of the Arenaviridae family, a single stranded Ribonucleic Acid virus (RNA), has characteristic similar to Ebola virus. Lassa fever has killed thousands of people in West Africa, most especially in Nigeria, many of whose lives could have been saved if rapid diagnostic test was available; people who could have received treatment early and also who could have been isolated early enough to reduce spread of Lassa fever. Recently many people have died in the rural areas due to late detection or delay access to proper medical attention. This is due to the fact that most medical centres equipped with handling Lassa fever cases are situated far away from the rural communities. Hence, it would be of great necessity to provide a computerized system that will provide a compleme...

A fractional-order controller will be proposed to regulate the inlet oxygen into the heart-lung machine. An analytical approach will be explained to satisfy some requirements together with practical implementation of some restrictions for the first time. Primarily a nonlinear single-input single-output (SISO) time-delay model which was obtained previously in the literature is introduced for the oxygen generation process in the heart-lung machine system and we will complete it by adding some new states to control it. Thereafter, the system is linearized using the state feedback linearization approach to find a third-order time-delay dynamics. Consequently classical PID and fractional order PI λ D μ controllers are gained to assess the quality of the proposed technique. A set of optimal parameters of those controllers are achieved through the genetic algorithm optimization procedure through minimizing a cost function. Our design method focuses on minimizing some famous performance criterions such as IAE, ISE, and ITSE. In the genetic algorithm, the controller parameters are chosen as a random population. The best relevant values are achieved by reducing the cost function. A time-domain simulation signifies the performance of PI λ D μ controller with respect to a traditional optimized PID controller.

The diagnosis of Typhoid Fever (TF) is often complicated due to the significant number of vague variables involved. As a result of this complexity, several lives have been lost while others are living with deteriorated health status. This research proposes a Hybrid Intelligent System which provides an efficient means of handling the complexity associated with the diagnosis of TF. The proposed system consists of a Fuzzy Logic (FL) component which handles imprecise and incomplete medical data, a Neural Network (NN) component which automatically generates the parameters that drive the Membership Functions of TF diagnosis variables for the Fuzzy Inference System. The attributes of TF diagnosis serve as the core input parameters to the FL and NN components. The proposed system provides a hybrid platform otherwise known as Adaptive Neuro Fuzzy Inference System (ANFIS) that employs back propagation and least square estimation learning techniques, and uses Sugeno's Inference Mechanism to provide accurate, timely, cost effective, and valid results regarding patient diagnosis. Experimental study of the proposed system was conducted using medical records of TF patients and the results of the study were found to be within the range of predefined limit as examined by medical experts. An evaluation of the proposed system using standard statistical methods proved its efficiency in providing accurate diagnosis.

A natural way to adjust or correct the behavior over time of a dynamic system output, the controlled variable, is by using an actuating input computed on the basis of the comparison of the actual output with its desired value: the feedback error. This is, by means of closed-loop control.

It is widely acknowledged that in software engineering, the usage of metrics at the initial phases of the object oriented software can help designers to make better decisions. The quality of class diagrams could be a major determinant for the quality of the software product that is finally delivered. Quantitative measurements are useful to assess class diagram quality. Following this innovative thinking, two UML class diagrams are taken to measure the complexity and size. A set of metrics of complexity measures are used to measure the class diagrams. Seven known complexity measures are evaluated and compared for inheritance and interface usage in object oriented programming. Two UML class diagrams are introduced with possible interfaces and measured the complexity metrics and a comparison has been made between the class inheritance and class interface usage through complexity measurements.

In this paper, we have designed a signal tracking microwave notch filter at X-band (8 GHz-12.4 GHz) which is tunable. The experimental notch frequency is 9.42 GHz and a 3-dB bandwidth of this filter is 105 MHz. The theoretical response agrees well with the experimental response of this notch filter. The frequency can be tuned by tuning the Gunn oscillators. The Gunn oscillators being injection-locked to the input signal make the notch filter tracking in character.

It is widely acknowledged that in software engineering, the usage of metrics at the initial phases of the object oriented software can help designers to make better decisions. The quality of class diagrams could be a major determinant for the quality of the software product that is finally delivered. Quantitative measurements are useful to assess class diagram quality. Following this innovative thinking, two UML class diagrams are taken to measure the complexity and size. A set of metrics of complexity measures are used to measure the class diagrams. Seven known complexity measures are evaluated and compared for inheritance and interface usage in object oriented programming. Two UML class diagrams are introduced with possible interfaces and measured the complexity metrics and a comparison has been made between the class inheritance and class interface usage through complexity measurements.

This research describes the development of an online thesis database system to assist novice researchers in a Malaysian university to identify research topics in local language major problems have been identified in the current system of keyword search. Firstly, student's lack of experience in querying often results in irrelevant search outcomes. Secondly, language barrier limits students' abilities to conduct keywor semantic search approach that applies ontology system framework design, its ontological development and sample queries are also presented to demonstrate how the system works

It is widely acknowledged that in software engineering, the usage of metrics at the initial phases of the object oriented software can help designers to make better decisions. The quality of class diagrams could be a major determinant for the quality of the software product that is finally delivered. Quantitative measurements are useful to assess class diagram quality. Following this innovative thinking, two UML class diagrams are taken to measure the complexity and size. A set of metrics of complexity measures are used to measure the class diagrams. Seven known complexity measures are evaluated and compared for inheritance and interface usage in object oriented programming. Two UML class diagrams are introduced with possible interfaces and measured the complexity metrics and a comparison has been made between the class inheritance and class interface usage through complexity measurements.

Background/Objectives: The objective of this paper is to design centralised controller for liquid level in the two
interacting conical tank system. Methods/Statistical analysis: Two Centralised Proportional and Integral (PI) controller
tuning method (Davison method and Tanttu and Liestehto method) are used for simulation and implemented in real
time process. Findings: Centralised PI controller reduces the interaction between the two interacting conical tank and
improves the system performance. The performances are compared in terms of Integral Square Error (ISE) and Peak
Overshoot. It is observed that Tanttu and Liestehto method produces better response than Davison method. Applications/
Improvements: Level control in two interacting conical tank is used in chemical industries and water treatment plants.

This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

Major retrieval issues on conventional online database search systems faced by novice researchers are identified and described. An ontology-based search framework is proposed. The framework consists of five elements: (1) user, (2) query, (3) query interface, (4) search process, and (5) result. The query interface is designed based on the ontology hierarchical structure in the ontology datastore and is represented as keywords/sub-keywords structures in the query interface. Ontology enables relationships between keywords and terms to be defined. Ontology allows desired information to be retrieved by sharing common vocabularies with an understanding of meaning of terms in the domain. A prototype system is developed based on the design science methodology selected to guide the system development. The system will process the search query once the query is sent. The search process will look for the matched documents and return results to the user. The results that are returned to the users will be reviewed and users have the option to repeat the process if the results do not fulfil the query made. The system development takes into account problems identification in the database search environment and motivation in improving and extending current limitations.

A new methodology is developed to analyse existing water quality monitoring networks. This methodology
incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental
health risk, the value of information, and redundancy reduction. The work starts with a formulation of a
conceptual framework for groundwater quality monitoring to represent the methodology’s context. This
work presents the development of Bayesian techniques for the assessment of groundwater quality. The
primary aim is to develop a predictive model and a computer system to assess and predict the impact of
pollutants on the water column. The process of the analysis begins by postulating a model in light of all
available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior
distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield
the current knowledge represented by the posterior distribution of model parameters. This process of
updating information about the unknown model parameters is then repeated in a sequential manner as
more and more new information becomes available.

The empirical mode decomposition (EMD) was a method pioneered by Huang et al as an alternative technique to the traditional Fourier and wavelet techniques for studying signals. It decomposes a signal into several components called intrinsic mode functions (IMF), which have shown to admit better behaved instantaneous frequencies via Hilbert transforms. In this paper we propose an alternative algorithm for empirical mode decomposition (EMD) based on iterating certain filters, such as Toeplitz filters. This approach yields similar results as the more traditional sifting algorithm for EMD. In many cases the convergence can be rigorously proved.

The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile's field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint.

In this work an adaptive control along with robust control have been developed to address the problem of system performance in the face of system uncertainty in control-system design without excessive reliance on system models. The direct MRAC control scheme with unknowns is designed with control law that is to be combined with an adaptive law. The adaptive laws are developed using the SPR-Lyapunov design approach and are driven by the estimation error for continuous-time systems guaranteeing asymptotic stability of the system states. CDM is a polynomial approach which was developed and introduced for a good transient response of the control systems. An MRAC is constructed in two-degree of-freedom (2DOF) control structure and the adaptation gains of controller parameters are found through CDM. Spherical tank level process is used for validating the design procedure of CDM-MRAC in real time and the performance of controller is compared with linear PI controller. Simulation is carried out using Mat Lab Simulink software.

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well.