Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review

The Internet of Things (IoT) is a new communication paradigm that can connect elements from various fields through the Internet. One of the most attractive IoT applications is in the modern healthcare area, as the traditional healthcare system has an increasing demand for social resources, including doctors, nurses, hospital beds, and health monitoring devices. In this article, the design of a compact wearable sensor patch is presented for measurements of different physiological signals, such as the electrocardiogram (ECG), photoplethysmography (PPG), and body temperature. As ECG and PPG sensors are integrated with the same device, the proposed sensor patch can be used to estimate blood pressure (BP) continuously based on the pulse arrival time (PAT) without extra wires and devices. The sensor patch consists of a center board for signal acquisition and processing, a power board for energy supply and charging batteries, and three sensors for vital signs monitoring. All the components are designed in a rigid-flex structure, which can be easily attached to the human body for remote health monitoring applications. The sensors can be detached from the center board for customized measurements of a certain physiological signal (e.g., ECG) to reduce power consumption. Experiments are conducted to validate the performance of the proposed sensor patch by comparison with a commercial reference device. With the integration of a miniaturized Bluetooth low-energy (BLE) module, the proposed sensor system can transmit physiological measurements wirelessly to a gateway. Data encryption is applied on both the sensor patch and gateways to protect data for privacy and security purposes during transmission. Both a mobile gateway (based on smartphones) and a fixed gateway (based on portable computers) are designed as the bridge between the wearable sensor system and the Internet cloud, where health data can be stored and further analyzed. The experimental results demonstrate the feasibility of the overall platform for IoT-connected healthcare applications.

A Rigid-Flex Wearable Health Monitoring Sensor Patch for IoT-Connected Healthcare Applications

Background: Noninvasive continuous blood pressure (BP) measurement has become an evolving topic in the field of remote healthcare. The classical noninvasive BP measurement techniques provide spontaneous values of systolic and diastolic BP. On the other hand, intrusive type BP measurement techniques provide continuous values of systolic and diastolic BP. However, these techniques are very painful, cannot be used for long-term monitoring, and are obtainable only in an intensive care unit environment. With the advancement of the remote healthcare industry, there is a growing demand for noninvasive continuous BP monitoring. Objective: The objective of this research was to present a compact literature review on the various prospective approaches of noninvasive continuous BP measurement techniques. Materials & Methods: The most contemporary and advanced technologies on noninvasive continuous BP measurement are Tactile Sensing, Vascular Unloading Technique, Pulse Transit Time, Photoplethysmography, Ultr...

A Literature Review on Current and Proposed Technologies of Noninvasive Blood Pressure Measurement.

An initial value problem is solved for the motion of an incompressible viscous conducting fluid with embedded small inert spherical particles bounded by an infinite rigid non-conducting plate. Both the plate and the fluid are in a state of solid-body rotation with constant angular velocity about an axis normal to the plate. The unsteady flow is generated in the fluid-particle system due to velocity tooth pulses subjected on the plate in presence of a transverse magnetic field. It is assumed that no external electric field is imposed on the system and the magnetic Prandtl number is very small. The operational method is used to derive exact solutions for the fluid and the particle velocities and the shear stress at the wall. Some limiting cases of these solutions including the steady-state results are discussed. The general solutions for the fluid velocity and the wall shear stress are examined numerically and the simultaneous effects of rotation, the magnetic field and the particles on them are determined. Finally, the present result for the fluid velocity has been compared numerically with that generated by an impulsively moved plate in a particular case when time is large.

/pdf/on-hydromagnetic-rotating-flow-of-a-dusty-fluid-near-a-3lx9h331ja.pdf

On hydromagnetic rotating flow of a dusty fluid near a pulsating plate

Reduction of the binuclear superoxocomplex of cobalt(III) [Co 2 (O 2 )(NH 3 ) 10 ] 5+ by 2-aminocthanethiol is specifically catalyzed by dissolved Cu(II). Each superoxide cation oxidizes one RSH, yielding the disulfide R 2 S 2 and the peroxo complex, [Co 2 (O 2 )(NH 3 ) 10 ] 4+ . Although some kinetic profiles for this reaction, measured with the reductant (RedH 2 + ) in excess, are nearly exponential, most exhibit a prominent nearly linear component during the initial stagss of reaction;

Electron transfer. 117. Copper catalysis in thiol reduction of bound superoxide. A second mechanistic variation

Cloud computing can be considered as one of the leading-edge technological advances in the current IT industry. Cloud computing or simply cloud is attributed to the Service Oriented Architecture. Every organization is trying to utilize the benefit of cloud not only to reduce the cost overhead in infrastructure, network, hardware, software, etc., but also to provide seamless service to end users with the benefit of scalability. The concept of multitenancy assists cloud service providers to leverage the costs by providing services to multiple users/companies at the same time via shared resource. There are several cloud service providers currently in the market and they are rapidly changing and reorienting themselves as per market demand. In order to gain market share, the cloud service providers are trying to provide the latest technology to end users/customers with the reduction of costs. In such scenario, it becomes extremely difficult for cloud customers to select the best service provider as per their requirement. It is also becoming difficult to decide upon the deployment model to choose among the existing ones. The deployment models are suitable for different companies. There exist divergent criteria for different deployment models which are not tailor made for an organization. As a cloud customer, it is difficult to decide on the model and determine the appropriate service provider. The multicriteria decision making method is applied to find out the best suitable service provider among the top existing four companies and choose the deployment model as per requirement.

/pdf/implementing-fuzzy-topsis-in-cloud-type-and-service-provider-41wg14qdde.pdf

Implementing Fuzzy TOPSIS in Cloud Type and Service Provider Selection

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Sanchita Ghosh

Papers

A Literature Review on Current and Proposed Technologies of Noninvasive Blood Pressure Measurement.

On hydromagnetic rotating flow of a dusty fluid near a pulsating plate

Electron transfer. 117. Copper catalysis in thiol reduction of bound superoxide. A second mechanistic variation

Implementing Fuzzy TOPSIS in Cloud Type and Service Provider Selection

Call Admission Control in Mobile Cellular Networks