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Wireless Body Area Networks (WBANs) consist of tiny Biomedical Wireless Sensors (BWSs) and a Gate Way (GW) to connect to the external databases in the hospital and medical centres. The GW could connect the BWSs, to a range of wireless telecomnumication networks. These wireless telecommunication networks could be either a mobile phone network, a standard telephone network a dedicated medical centre or using public Wireless Local Area Networks (WLANs) nodes also known a Wi-Fi system. The electrocardiogram (ECG) signals are widely used in health care systems because they are non-invasive mechanisms to establish medical diagnosis of heart diseases. The current ECG systems suffer from important limitations: limited patient's mobility, limited energy, limited on wireless applications. The main drawback of current EGG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why; there is a critical need to improve the current ECG systems to cover security handling and to achieve extended patient's mobility. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide a robust low sampling-rate approach for normal and abnormal ECG signals. Advanced WBANs based on our approach will be able to deliver healthcare not only to patients in hospital and medical centres; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results based on two proposed algorithms illustrate 15% incensement of Signal to Noise Ratio (SNR) and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices.

In computing, a network generally denotes devices, often referred to as nodes, connected by links. Networks that are modeled with diagrams consist of hundreds of symbols, images, pictures, and icons, such as a computer, a server rack, or a cloud-based storage system. Network representations provide valuable insights into understanding the systems' underlying structures and mechanisms. Nevertheless, this unusually large number of superficial symbols and icons reflects a need for more systematic representations of the interiority of nodes. To give uniformity to this cascade of notions of basic units of nodes in network diagrams, the authors propose adoption of a new modeling methodology, called a thinging (abstract) machine (TM) (abstract machine of things) that represents all notions as a single diagrammatic machine. Because of the large number of network types, in this paper, they specifically and without loss of generality focus on IP telephone (internet protocol telephone) networks to exemplify communication networks. A real case study of IP telephone networks is modeled using TM.

This paper presents an analysis of voice traffic in telephone networks, based on machine learning algorithms to detect frauds made by callers. Starting from the raw data set that includes information about the call date, destination number, duration and caller's number, in our approach we were able to identify fraudulent calls in early stages. For balance, the data set was split in 2 parts: one for training and one for testing. To obtain mean’s values from dataset, a standardization technique was applied in order to scale the data before the dimensionality reduction using Principal Component Analysis. Then, the first two components were used as inputs for Logistic Regression and Random Forest models, having the caller as target. Finally, the target was moved on the destination file so as to identify the caller and the moment when the call has started based on a vector representation of words.

Telemonitoring of Parkinson’s Disease (PD) has attracted considerable research interest because of its potential to make a lasting, positive impact on the life of patients and their carers. Purpose-built devices have been developed that record various signals which can be associated with average PD symptom severity, as quantified on standard clinical metrics such as the Unified Parkinson’s Disease Rating Scale (UPDRS). Speech signals are particularly promising in this regard, because they can be easily recorded without the use of expensive, dedicated hardware. Previous studies have demonstrated replication of UPDRS to within less than 2 points of a clinical raters’ assessment of symptom severity, using high-quality speech signals collected using dedicated telemonitoring hardware. Here, we investigate the potential of using the standard voice-over-GSM (2G) or UMTS (3G) cellular mobile telephone networks for PD telemonitoring, networks that, together, have greater than 5 billion subscribers worldwide. We test the robustness of this approach using a simulated noisy mobile communication network over which speech signals are transmitted, and approximately 6000 recordings from 42 PD subjects. We show that UPDRS can be estimated to withinless than 3.5 points difference from the clinical raters’ assessment, which is clinically useful given that the inter-rater variability for UPDRS can be as high as 4-5 UPDRS points. This provides compelling evidence that the existing voice telephone network has potential towards facilitating inexpensive, mass-scale PDsymptom telemonitoring applications.

Voice over Internet Protocol (VoIP) has become mainstream in terms of the usability due to two key factors: flexibility and low cost, all of this towards classic PSTN (Public as well Service Telephone Network) based on several studies and the practical approach because your calls are no longer routed along a dedicated wire to a local exchange, but instead provider implements packet switching and the direct line needed is now performed by the VoIP provider. However, there are several concerns over security, [13] which is quite understandable. The rising issues have naturally come over the efficiency communication VoIP offers and with this being a main key, that’s where companies and businesses have invested, leaving things in the open over security. This study tries to exploit the vulnerabilities VoIP shows in its classic scenario and give a few suggestions on what to do regarding the problems raised during the way. All the work was based on previous knowledge regarding VoIP as studied in Computer Networks.

ICTs in Education refers to the development of information and communications technology specifically for teaching/learning purposes, while the ICTs in education involves the adoption of general components of information and communication technologies in the teaching learning process. The National Mission on Education through Information and Communication Technology (NME-ICT), launched in 2009 by the Central Government. Let’s see how Information and Communication Technology (ICT) evolved the Higher Education system: The role of ICT in higher education, what students learn, The role of ICT in Higher Education, how Students Learn, The role of ICT in Higher Education, when students learn, The role of ICT in higher education, where students learn. Online courses, development of e-content, e-learning, digital libraries, online encyclopaedias, journals, and books would promote learning and make knowledge available to all irrespective of the distance or location or financial resources. Government intervention is necessary so that ICT can be made successful in higher education. Information and Communications Technology (ICT) has the proven power to change the world. This acronym refers to the merging of audiovisual and telephone networks with the computer single unified system of cabling.

Telephony technologies (mobile, VoIP, and fixed) have potentially improved the way we communicate in our daily life and have been widely adopted for business and personal communications. At the same time, scammers, criminals, and fraudsters have also find the telephony network an attractive and affordable medium to target end-users with the advertisement, marketing of legal and illegal products, and bombard them with the huge volume of unwanted calls. These calls would not only trick call recipients into disclosing their private information such as credit card numbers, PIN code which can be used for financial fraud but also causes a lot of displeasure because of continuous ringing. The fraudsters, political campaigners can also use telephony systems to spread malicious information (hate political or religious messages) in real-time through audio or text messages, which have serious political and social consequences if malicious callers are not mitigated in a quick time. In this context, the identification of malicious callers would not only minimize telephony fraud but would also bring peace to the lives of individuals. One way to classifies users as a spammer or legitimate is to get feedback from the call recipients about their recent interactions with the caller, but these systems not only bring inconvenience to callees but also require changes in the system design. The call detail records extensively log the activities of users and can be used to categorize them as the spammer and non-spammer. In this paper, we utilize the information from the call detailed records and proposed a spam detection framework for the telephone network that identifies malicious callers by utilizing the social behavioral features of users within the network. To this extent, we first model the behavior of the users as the directed social graph and then analyze different features of the social graph i.e. the Relationship Network and Call patterns of users towards their peers. We then used these features along with the decision tree to classify callers into three classes i.e. human, spammer and call center. We analyzed the call record data-set consisting of more than 2 million users. We have conducted a detailed evaluation of our framework which demonstrates its effectiveness by achieving acceptable detection accuracy and extremely low false-positive rate. The performance results show that the spammers and call center numbers not only have a large number of non-repetitive calls but also have a large number of short duration calls. Similarly, on the other hand, the legitimate callers have a good number of repetitive calls and most of them interacted for a relatively long duration.

Scholars have posed different hypotheses on the impact of global telecommunications on value orientations. We analyze and characterize the global telecommunication network and test a series of hypotheses on the relationship between gender values and three types of telephone connections: ties with the global society, ties with Western nations, and ties within groups of nations sharing similar cultural, religious, political, or geographical traits. We use multilevel models and data on two levels, between-country telecommunications network data from TeleGeography, and individual-level data ( N = 70,225) on people living in 47 countries from the World Value Survey, waves III and IV. Countries with high degrees of communication insulation, measured as a high percentage of within-group ties of all global telephone links, hold less egalitarian attitudes toward gender equality. This negative effect of group insulation depresses the egalitarian effects of younger birth cohort, college education, and higher income. Embeddedness in a localized information diffusion network and isolated from global communication is associated with less egalitarian attitude toward gender equality. But neither global ties nor ties with Western countries are linked with gender attitudes.