Concept of frames discarding and multiplexing efficiency

At low bit rates speech is coded frame by frame, each frame of a size of 20-30ms. Perceptually, each frame may be different, depends upon the speech signal properties (voiced or unvoiced). The frames with lower perception may be one of the potential candidates to be dropped and reconstructed to maximize the bandwidth of the systems. This chapter explains to hO\v these frames might be identified and used for STDM designs

Energy ad a criterion for frame discarding and multiplexing efissiency

To design a STOM on the criterion of energies of each frame is one of the popUlar methods. In this each frames energy is calculated, a frame with lower energy is the best candidates among all users' speech frames. Dropping the frame with lmvest energy may degrade quality at its minimum as compared to the higher energy signals. This is also perceptually less important. Such frames of speech can be utilised to maximize the users on the link. This chapter talks about energy criterion as packet discarding for STDM design

LPC envelope difference criterion and multiplexing efficiency

Linear Predictive Coding (LPC) envelop difference is a technique that exploits envelop of each frame. This is a frequency domain measure in which for each active speech frame and its reconstructed frame is used to find out the differences in their envelops. This LPC difference measures can be utilised for the STDM design purpose. This chapter is dedicated for LPC envelop as a frame discarding criterion to maximize the bandwidt

Identification of vessel anomaly behavior using support vector machines and Bayesian networks

In this work, a model based on Support Vector Machines (SVMs) classification to identify vessel anomaly behavior have been proposed and implemented, and the result is compared to Bayesian Networks (BNs). The works have been done using the real world Automated Identification System (AIS) vesselreporting data. SVMs can achieve higher accuracy compared to BNs in both memory-test and blind-test. The effect of holdout method which is partitioned size of training and testing data set on the accuracy result were also investigated in this study. The proposed classifier demonstrated to be a viable tool for identifying the vessel anomaly behavior by its high accuracy

Cyclic frame discarding multiplexer design

The cyclic frame discarding is an approach in which at least for each user a packet is discarded before attempting to discard a second packet fonn the same. The idea is to avoid consecutive packet discarding from a single user when there is higher load. To do so, a slightly better perfonnance, in tenns of speech quality is achieved than a random packet dropping that suffers from consecutive packet losses. This chapter explains the benefit that can be achieved by controlling the packet discarding mechanism in a cyclic way

Anomaly detection in vessel tracking using Support Vector Machines (SVMs)

The paper is devoted to supervise method approach to identify the vessel anomaly behavior in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behavior. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behavior. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages

Networking issue

All communication networks use or going to use packet based communications that intrinsically use STDM for communication. No user is going to have a fixed time slot instead, a slot is allocated on a demand basis or activity basis. The packet discarding is a new' concept in networking, in this chapter a random packet discarding is explained

Clinic management system: business process re-engineering based on user experience (UX)

Clinic management systems function is to arrange and organize the inherent process in the health facility to be harmonized with the patient’s demand. The purpose of this paper is to analyse the common themes to be used in user interface of the application through identification of user specification based on the context. The user experience has been utilized to consider the critical aspect of successes of implementation related to human factor of mental model, which are utility, ease of use and efficiency. The contribution of this paper is twofold. Firstly, the design process should be done through tremendous re-design of primary business process to obtain excessive improvement in the capacity, capability and quality of the service. Secondly, the information presentation should be provided in alternative way involving the effort to have more platform and modes to increase accessibility and availability

Prediction analysis of the happiness ranking of countries based on macro level factors

Happiness is an essential universal human goal in their life that can improve the quality of life. Since the introduction of positive psychology, the primary consideration has been pointed out to the study of the role from certain factors in predicting the happiness, especially the advancement of technology that allows computer-mediated to be part of human interaction. It provides a multidimensional approach and indirect influence to the human expression and communication. The project investigates what it takes to build a happy country by analysing on the relationship between the happiness ranking of countries and their macro level factors. The World Happiness Report 2019 is used coupled with Python programming for visualizing and extracting information from the dataset to better understand the bigger picture

Machine learning classification model for identifying internet addiction among university students

In this era of globalization, Internet addiction is a concerning issue, especially among university students as they are required to use the internet for academic purposes. However, things might go wrong when they are addicted to the Internet as the Internet does not only provide knowledge but also entertainment such as music, videos, games, social media, etc. Internet addiction was exposed to the public when Young introduced Internet addiction in her study as well as an assessment for Internet addiction known as Young's Internet addiction test (IAT) which is a questionnaire. Nonetheless, there are some issues associated with the questionnaire regarding the integrity and literacy of the participants as well as the experience of the specialist which might introduce inconsistencies in the assessment of one's Internet addiction level. Hence, the machine learning algorithm is introduced to replace the conventional assessment method for Internet addiction. In this study, three machine learning models are developed and compared. The three models include convolutional neural network (CNN), K-nearest neighbours (KNN), and logistic regression (LR). The low Alpha power band of the EEG data is transformed into spectrograms and utilized as the input for the machine learning models. The spectrograms are presented as images and fed into the CNN model. On the other hand, as KNN and LR could not take in images as the input data, the magnitude of each frequency in every time segment of each spectrogram is computed and fed into the KNN and LR. The results show that CNN gives the best performance in terms of overall accuracy, precision, recall, and F1-score, while KNN gives the most consistent performance