Distributed video coding in wireless multimedia sensor network for multimedia broadcasting

Recently the development of Distributed Video Coding (DVC) has provided the promising theory support to realize the infrastructure of Wireless Multimedia Sensor Network (WMSN), which composed of autonomous hardware for capturing and transmission of quality audio-visual content. The implementation of DVC in WMSN can better solve the problem of energy constraint of the sensor nodes due to the benefit of lower computational encoder in DVC. In this paper, a practical DVC scheme, pixel-domain Wyner-Ziv(PDWZ) video coding, with slice structure and adaptive rate selection(ARS) is proposed to solve the certain problems when applying DVC into WMSN. Firstly, the proposed slice structure in PDWZ has extended the feasibility of PDWZ to work with any interleaver size used in Slepian-wolf turbo codec for heterogeneous applications. Meanwhile, based on the slice structure, an adaptive code rate selection has been proposed aiming at reduce the system delay occurred in feedback request. The simulation results clearly showed the enhancement in R-D performance and perceptual quality. It also can be observed that system delay caused by frequent feedback is greatly reduced, which gives a promising support for WMSN with low latency and facilitates the QoS management

Dynamic Rate and Channel Selection in Cognitive Radio Systems

In this paper, we investigate dynamic channel and rate selection in cognitive radio systems which exploit a large number of channels free from primary users. In such systems, transmitters may rapidly change the selected (channel, rate) pair to opportunistically learn and track the pair offering the highest throughput. We formulate the problem of sequential channel and rate selection as an online optimization problem, and show its equivalence to a {\it structured} Multi-Armed Bandit problem. The structure stems from inherent properties of the achieved throughput as a function of the selected channel and rate. We derive fundamental performance limits satisfied by {\it any} channel and rate adaptation algorithm, and propose algorithms that achieve (or approach) these limits. In turn, the proposed algorithms optimally exploit the inherent structure of the throughput. We illustrate the efficiency of our algorithms using both test-bed and simulation experiments, in both stationary and non-stationary radio environments. In stationary environments, the packet successful transmission probabilities at the various channel and rate pairs do not evolve over time, whereas in non-stationary environments, they may evolve. In practical scenarios, the proposed algorithms are able to track the best channel and rate quite accurately without the need of any explicit measurement and feedback of the quality of the various channels.Comment: 19 page

VENDOR RATE SELECTION SYSTEM APPLIED WITH DATA MINING

In business world, making a good and correct decision is essential but for a person alone to make the decision, the result will be incorrect as human being will include their emotion while making the decision. In order to fix this problem, a business system that fully utilized their previous business's data and pattern should be developed. The scope of this report is to give an overview about how this system being developed and worked provided with some related research and discussion. In this project, it will use data mining technique in order to fmd the most suitable logistics vendor to be used in a company. In order to get the pattern, there are several factors that will be considered for examples the rate given, destination, services provided and etc. The main objective of this project is to analyze past vendor's data set from a company by using data mining technique, thus, it will help the user to make a better decision. By using the technique, the set of data will be group into vendor's company classes and the most frequent vendor that has been used before will be selected and the system will prompt the user. The methodology used in this project is Extreme Programming (XP) methodology because there are many advantages such as this methodology can apply to changes. This project will help a collaboration company to improve their decision making and business performance

Wi-Fi Rate Selection Using Machine Learning Models

When a Wi-Fi device tries to connect to access points, it picks a channel, rate, and a particular access point (AP) out of a potentially large number of selections. Different selections result in different quality of user experiences, e.g., as measured by throughput and latency. In many circumstances, the device has a past history of connecting to various access points in or near its current location. With user permission, this disclosure uses trained machinelearning models and the past history to select an optimal channel, rate, and access point. The selections made using this technique can result in superior throughput, latency, and can improve user experience

Real Estate Development Feasibility and Hurdle Rate Selection

The main findings are that most developers use specific ‘go/no-go’ hurdle rate mechanisms irrespective of primary real estate type, with the majority using margin on development cost (MDC) or internal rate of return (IRR); the boundaries between traditional speculative development and real estate investment through the use of securitisation methods have become blurred; many developers use both quantitative metrics, with qualitative methods and specific structural checks to manage the risks involved; and the two most frequent methods of determining site value prior to acquisition are the residual land value and DCF methods. Most place a heavy reliance on industry‐accepted heuristics and do not have a predetermined process and method for altering or adapting the chosen hurdle rates and benchmarks