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

https://www.cs.montana.edu/courses/csci566/Ref/WMSN-Survey.pdf

A survey on wireless multimedia sensor networks

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

IEEE Transactions on Pattern Analysis and Machine Intelligence

Network Calculus.- Application of Network Calculus to the Internet.- Basic Min-plus and Max-plus Calculus.- Min-plus and Max-plus System Theory.- Optimal Multimedia Smoothing.- FIFO Systems and Aggregate Scheduling.- Adaptive and Packet Scale Rate Guarantees.- Time Varying Shapers.- Systems with Losses.

Network Calculus: A Theory of Deterministic Queuing Systems for the Internet

Video-based sensor networks can provide important visual information in a number of applications including: environmental monitoring, health care, emergency response, and video security. This article describes the Panoptes video-based sensor networking architecture, including its design, implementation, and performance. We describe two video sensor platforms that can deliver high-quality video over 802.11 networks with a power requirement less than 5 watts. In addition, we describe the streaming and prioritization mechanisms that we have designed to allow it to survive long-periods of disconnected operation. Finally, we describe a sample application and bitmapping algorithm that we have implemented to show the usefulness of our platform. Our experiments include an in-depth analysis of the bottlenecks within the system as well as power measurements for the various components of the system.

/pdf/panoptes-scalable-low-power-video-sensor-networking-1qon9crrsc.pdf

Panoptes: scalable low-power video sensor networking technologies

The intelligent transportation systems (ITS) use advanced communications and control technologies to improve current transportation systems. To achieve many ITS applications, the ability to exchange messages between vehicles is necessary. This is generally required as inter vehicle communication (IVC). IVC can be considered as a special case of ad hoc networks, where nodes only move along predefined road paths. We propose new broadcast protocols that make use of global positioning system (GPS) information to enhance the performance of broadcast service in IVC. The ability to efficiently broadcast messages is necessary for any communications in IVC (e.g, updating routing tables, etc). We propose two algorithms that effectively reduce the number of re-broadcast messages without affecting the number of hosts (vehicles) that receive the broadcast. The simulation results show up to several hundred percentage of bandwidth utilization improvement can be achieved by our proposed broadcast protocols.

GPS-based message broadcast for adaptive inter-vehicle communications

This paper describes the results of the first comprehensive analysis of a range of popular on-line, multiplayer, game servers. The results show that the traffic behavior of these servers is highly predictable and can be attributed to the fact that current game designs target the saturation of the narrowest, last-mile link. Specifically, in order to maximize the interactivity of the game itself and to provide relatively uniform experiences between players playing over different network speeds, on-line games typically fix their usage requirements in such a way as to saturate the network link of their lowest speed players. While the traffic observed is highly predictable, the traces also indicate that these on-line games provide significant challenges to current network infrastructure. As a result of synchronous game logic requiring an extreme amount of interactivity, a close look at the trace reveals the presence of large, highly periodic, bursts of small packets. With such stringent demands on interactivity, routers must be designed with enough capacity to quickly route such bursts without delay.

/pdf/a-traffic-characterization-of-popular-on-line-games-49w7egg6jk.pdf

A traffic characterization of popular on-line games

Intelligent Transportation Systems (ITS) have become a focus for many countries. To achieve ITS, Inter Vehicle Communication (IVC) is required for the exchange and distribution of data such as congestion or emergency information. If this communication can be done without fixed infrastructure, the systems can be deployed quickly and on a larger scale. Ad hoc networking technologies are one such technology to achieve IVC. However, if generic ad hoc network solutions are applied directly to IVC, performance can degrade quickly as the system scales particularly for broadcast type messages. In this paper we propose two new broadcast protocols that reduce bandwidth required for broadcast communication by taking advantage of a vehicle's highly directional movement and Global Positioning Information. To show the performance of our new protocols, we compare our approach with generic ad hoc broadcasting techniques. Our results show that it is possible to achieve several hundred percent improvement of bandwidth utilization with very slight sacrifice of reachability.

GPS-based message broadcasting for inter-vehicle communication

The transfer of prerecorded, compressed video requires multimedia services to support large fluctuations in bandwidth requirements on multiple time scales. Bandwidth smoothing techniques can reduce the burstiness of a variable-bit-rate stream by prefetching data at a series of fixed rates, simplifying the allocation of resources in video servers and the communication network. Given a fixed client-side prefetch buffer several bandwidth smoothing algorithms have been introduced that are provably optimal under certain constraints. This paper presents a collection of metrics for comparing these smoothing algorithms and evaluating their cost-performance trade-offs. Due to the scarcity of available trace data, we have constructed a video capture testbed and generated a collection of twenty full-length, motion-JPEG encoded video clips. Using these video traces and a range of client buffer sizes, we investigate the interplay between the performance metrics through simulation experiments. The results highlight the unique strengths and weaknesses of each algorithm.

Wu-chi Feng

Papers

Panoptes: scalable low-power video sensor networking technologies

GPS-based message broadcast for adaptive inter-vehicle communications

A traffic characterization of popular on-line games

GPS-based message broadcasting for inter-vehicle communication

A comparison of bandwidth smoothing techniques for the transmission of prerecorded compressed video