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

Statistics for Spatial Data.

http://masou-keshtkar.persiangig.com/document/sdarticle.pdf

GSA: A Gravitational Search Algorithm

Theory of fuzzy integrals and its applications

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory

The system described in this invention can be used for monitoring and analyzing real time visual and non-visual information pertaining to the occupant, vehicle, and surroundings prior to, during and post eccentric operating conditions for a given period of time The system stores vehicle and occupant data from sensors throughout the vehicle and also makes use of existing vehicle sensors that may already be present in the vehicle The invention also includes a video recording module that makes use of a fish-eye camera to capture video from the inside and the outside of the vehicle Real-time data analysis is performed to detect and to recognize vehicle occupants, and recognize impending eccentric events Vehicle, occupant, and video data are stored in circular buffers When an eccentric event, a collision for example, has been detected, the device continues to record data and video for a fixed period of time Once this time has elapsed, the data is transferred from volatile to non-volatile memory for later retrieval A computer may be used to retrieve and display the vehicle and occupant data in a synchronization with the video data for the purposes of accident recreation, driver or vehicle monitoring

Vehicle visual and non-visual data recording system

A vehicle communication system facilitates hands-free interaction with a mobile device in a vehicle or elsewhere. Users interact with the system by speaking to it. The system processes text and processes commands. The system supports Bluetooth wireless technology for hands-free use. The system handles telephone calls, email, and SMS text messages. The user can customize the device via a user profile stored on an Internet web server.

Vehicle immersive communication system

A vehicle communication system includes a control unit in a vehicle, a vehicle data bus operable to transmit vehicle data to the control unit, and a mobile communication device having data network access. The mobile device has a self-contained mode in which the mobile communication device processes and displays the network data received via the data network and in which the control unit transmits the vehicle data to the mobile communication device. The mobile communication device also has an immersive mode in which the control unit processes the network data received via the data network and in which the control unit converts the vehicle data to audible speech and plays the audible speech in the vehicle.

Communication system with personal information management and remote vehicle monitoring and control features

A knowledge-based natural speech dialogue system includes: (i) a knowledge support system, (ii) a flexible dialogue control system, and (iii) a context information system. Flexibilities of the conversation structure, inherent in mixed-initiative mode for dealing with complex user request, are managed because the knowledge structures involved are represented by additional, powerful knowledge representation tools, and because the context information is retained by more specific data structures, which covers larger temporal scopes by the logic of the conversation, rather than by a fixed locality of the grammar flow. This system provides a simple yet reliable method to compensate for these factors to enable more powerful conversation engines with mixed-initiative capabilities.

Otman A. Basir

Papers

Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory

Vehicle visual and non-visual data recording system

Vehicle immersive communication system

Communication system with personal information management and remote vehicle monitoring and control features

Knowledge-based flexible natural speech dialogue system