Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

Emotional inference by means of Choquet integral and λ-fuzzy measurement in consideration of ambiguity of human mentality

Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.

Relating Maxwell's demon and quantitative analysis of information leakage for practical imperative programs

Shannon observed the relation between information entropy and Maxwell demon experiment to come up with information entropy formula. After that, Shannon＇s entropy formula is widely used to measure information leakage in imperative programs. But in the present work, our aim is to go in a reverse direction and try to find possible Maxwell＇s demon experimental setup for contemporary practical imperative programs in which variations of Shannon＇s entropy formula has been applied to measure the information leakage. To establish the relation between the second principle of thermodynamics and quantitative analysis of information leakage, present work models contemporary variations of imperative programs in terms of Maxwell＇s demon experimental setup. In the present work five contemporary variations of imperative program related to information quantification are identified. They are： （i） information leakage in imperative program, （ii） imperative multi- threaded program, （iii） point to point leakage in the imperative program, （iv） imperative program with infinite observation, and （v） imperative program in the SOA-based environment. For these variations, minimal work required by an attacker to gain the secret is also calculated using historical Maxwell＇s demon experiment. To model the experimental setup of Maxwell＇s demon, non-interference security policy is used. In the present work, imperative programs with one-bit secret information have been considered to avoid the complexity. The findings of the present work from the history of physics can be utilized in many areas related to information flow of physical computing, nano-computing, quantum computing, biological computing, energy dissipation in computing, and computing power analysis.

Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.

Design and analysis of a network coding algorithm for ad hoc networks

Network coding is proved to have advantages in both wireline and wireless networks. Especially, appropriate network coding schemes are programmed for underlined networks. Considering the feature of strong node mobility in aviation communication networks, a hop-by-hop network coding algorithm based on ad hoc networks was proposed. Compared with COPE-like network coding algorithms, the proposed algorithm does not require overhearing from other nodes, which meets confidentiality requirements of aviation communication networks. Meanwhile, it does save resource consumption and promise less processing delay. To analyze the performance of the network coding algorithm in scalable networks with different traffic models, a typical network was built in a network simulator, through which receiving accuracy rate and receiving delay were both examined.The simulation results indicate that, by virtue of network coding, the proposed algorithm works well and improves performance significantly. More specifically, it has better performance in enhancing receiving accuracy rate and reducing receiving delay, as compared with any of the traditional networks without coding. It was applied to both symmetric and asymmetric traffic flows and, in particular, it achieves much better performance when the network scale becomes larger. Therefore, this algorithm has great potentials in large-scale multi-hop aviation communication networks.

Performance Evaluation of IPv4/IPv6 Networks for Ubiquitous Home-Care Service

Abstract--- Because of rapid development in network technology, Internet usage has become widespread. It allows users with sensing devices to obtain medical data for healthcare, such as physiological signals, voice, and video streams from telemedicine systems, and to send the healthcare data to back-end database systems, creating a ubiquitous healthcare environment. However, this environment requires a widespread and suitable network. IPv6 （Internet protocol version 6） is the next-generation Internet protocol that will be the protocol of future networks; it improves many shortcomings of IPv4. In this paper, we propose an IPv6/IPv4 U-home-care test system and analyze the network＇s parameters though a series of tests by adjusting network parameters to find the optimal design for applications in the IPv6/IPv4 U-home-care service so as to assure good performance and high quality.

Design of a 3-tier smart middleware system for ubiquitous sensor networks

As ubiquitous sensor networks (USN) technologies and its middleware are still at its early stages, the system of the USN relies on the middleware and applications. The past sensor networks are assumed to be designed for specific applications, having data communication protocols tightly coupled to applications. To avoid these problems, several kinds of USN middleware have been researched and developed. However, most middleware of USN are still restricted by its own infrastructure so far. This paper proposes appropriate 3 tier Smart Middleware System (3SMS) for USN.

Analytical models of lateral power devices with arbitrary vertical doping profiles in the drift region

By solving the 2D Poisson＇s equation, analytical models are proposed to calculate the surface potential and electric field distributions of lateral power devices with arbitrary vertical doping profiles. The vertical and the lateral breakdown voltages are formulized to quantify the breakdown characteristic in completely-depleted and partially-depleted cases. A new reduced surface field （RESURF） criterion which can be used in various drift doping profiles is further derived for obtaining the optimal trade-off between the breakdown voltage and the on-resistance. Based on these models and the numerical simulation, the electric field modulation mechanism and the breakdown characteristics of lateral power devices are investigated in detail for the uniform, linear, Gaussian, and some discrete doping profiles along the vertical direction in the drift region. Then, the mentioned vertical doping profiles of these devices with the same geometric parameters are optimized, and the results show that the optimal breakdown voltages and the effective drift doping concentrations of these devices are identical, which are equal to those of the uniform-doped device, respectively. The analytical results of these proposed models are in good agreement with the numerical results and the previous experimental results, confirming the validity of the models presented here.

Relationship between symptoms of traditional Chinese medicine and indicator of western medicine about liver cirrhosis

Traditional Chinese medicine (TCM) is one of the safe and effective methods to treat liver cir-rhosis. The practitioners of TCM assess hepatic function in term of syndrome. But the course of syndrome differentiation is subjectivity. At pre-sent most of all the researches are focused on the relationship between the syndrome and the Western medicine objective indicators such as child-pugh grade. In fact syndrome is the syn-thesis of signs and symptoms and collecting signs, symptoms is easy than syndrome differ-entiation. We try to explore the relationship be-tween the objective Western medicine standard such as child-pugh grade, decompensation or compensation stage, active or inactive period and the signs and symptoms of TCM by using the data mining method. We use the information gain method to assess the attributes and use five typical classifiers such as logistic, Bayes-Net, NaiveBayes, RBF and C4.5 to obtain the classification accuracy. After attribute selection, we obtain the main symptoms and signs of TCM relating to the stage, period and child-pugh grade about liver cirrhosis. The experiment re-sults show the classification accuracy is im-proved after filtering some symptoms and signs.

Design and Development of E-Governance Model for Service Quality Enhancement

Governance is a process of establishing policies or laws and continuously monitoring their implementation in the public. Citizens are the main stakeholders in this process. To make the governance process cost effective, easy and convenient to the citizens, information and communication technologies (ICTs) are playing a vital role. Using ICTs in governance domain can be referred as e-governance. It helps citizen to participate not only in policy making but also in service quality improvements and getting government services over network. Web technology is one of the major technologies that have been used in e-governance as a major contributor. Before invention of web, citizens used to write their opinions and send to concerned body via post mail or local suggestion box for improvements of services. After the invention of the web, it became possible to post or send the opinions/comments over organization’s web sites or portals. As the number of comments increases over the web, it is very difficult to have relevant information for furthering the decision making processes. In these study, text processing and data mining techniques have been used. The prototype system classifies citizen’s opinions in one of six service categories with the precision of 87.3% and recall of 85.8%. Further, the system determines opinions as appreciation with the precision of 100% for both depression and appreciation, and average recall of 90.85% for both depression and appreciation. The results obtained from the prototype system indicate that the techniques that have been applied are promising for real world application in e-governance.

The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection

Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods.

Recognition of Bangla Handwritten Number Using Combination of PCA and FIS with the Aid of DWT

The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: “one and nine” and “five and six”. We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.

A Study of Stellar Model with Kramer’s Opacity by Using Runge Kutta Method with Programming C

In this paper, we have made an investigation on a stellar model with Kramer’s Opacity and negligible abundance of heavy elements. We have determined the structure of a star with mass , i.e. the physical variables like pressure, density, temperature and luminosity at different interior points of the star. We have discussed about some equations of structure, mechanism of energy production in a star and energy transports in stellar interior in a star and then we have solved radiative envelope and convective core by the matching or fitting point method and Runge-Kutta method by C Programming language. In future, it will help us to know about the characteristics of new stars.

Imperfection of Domain Knowledge and Its Formalization in Context of Design of Robust Software Systems

In this paper, it is emphasized that taking into consideration of imperfection of knowledge, of the team of the designers/developers, about the problem domains and environments is essential in order to develop robust software metrics and systems. In this respect, first various possible types of imperfections in knowledge are discussed and then various available formal/mathematical models for representing and handling these imperfections are discussed. The discussion of knowledge classification & representation is from computational perspective and that also within the context of software development enterprise, and not necessarily from organizational management, from library & information science, or from psychological perspectives.

The Perspective of Acquired Immunity to Combat against Infectious Diseases: An Overview

There is a long ritual of acquired immunity using physical exercise, a balanced diet, and pharmaceutical medication to generate immunity against a particular disease insight into the human body. This paper has extensively reviewed the impact of exercise, daily life practice, food selection, and several other issues to improve the immune system that combat infection. Studying the effect of exercise in varying degrees on the immunity system of humans is well developed and exhibit in this study. It investigates the prevention of pandemics due to herd immunity and finds the perfect amount of exercise to boost immunity to its maximum. Besides the life practice, it is also explored that vaccination can improve and optimize herd immunity.

Quantifying Reusability of Object Oriented Design: A Testability Perspective

The quality factor of class diagram is critical because it has a significant influence on overall quality of the product, delivered finally. Testability analysis, when done early in the software creation process, is a criterion of critical importance to software quality. Reusability is an important quality factor to testability. Its early measurement in object oriented software especially at design phase, allows a design to be reapplied to a new problem without much extra effort. This research paper proposes a research framework for quantification process and does an extensive review on reusability of object oriented software. A metrics based model “Reusability Quantification of Object Oriented Design” has been proposed by establishing the relationship among design properties and reusability and justifying the correlation with the help of statistical measures. Also, “Reusability Quantification Model” is empirically validated and contextual significance of the study shows the high correlation for model acceptance. This research paper facilitates to software developers and designer, the inclusion of reusability quantification model to access and quantify software reusability for quality product.

Rank-Me: A Java Tool for Ranking Team Members in Software Bug Repositories

In this paper a team member ranking technique is presented for software bug repositories. Member ranking is performed using numbers of attributes available in software bug repositories, and a ranked list of developers is generated who are participating in development of software project. This ranking is generated from the contribution made by the individual developers in terms of bugs fixed, severity and priority of bugs, reporting newer problems and comments made by the developers. The top ranked developers are the best contributors for the software projects. The proposed algorithm can also be used for classifying and rating the software bugs using the ratings of members participating in the software bug repository.

Aspects of Replayability and Software Engineering: Towards a Methodology of Developing Games

One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understanding factors of success behind releasing a video game, we are interested in studying a factor known as Replayability. Towards a software engineering oriented game design methodology, we collect player opinions on Replayability via surveys and provide methods to analyze the data. We believe these results can help game designers to more successfully produce entertaining games with longer lasting appeal by utilizing our software engineering techniques.