In heterogeneous environments with performance variations present, multiple applications compete and share a limited amount of system resources, and su#er from variations in resource availability. These complex applications are desired to adapt themselves and to adjust their resource demands dynamically. On one hand, current adaptation mechanisms built within an application cannot preserve global properties such as fairness; on the other hand, adaptive resource management mechanisms built within the operating system are not aware of data semantics in the application. In this paper, we present a novel Middleware Control Framework to enhance the e#ectiveness of QoS adaptation decisions by dynamic control and reconfiguration of internal parameters and functionalities of a distributed multimedia application. Our objective is to satisfy both system-wide properties (such as fairness among concurrent applications) and application-specific requirements (such as preserving the critical performance criteria). The framework is modeled by the Task Control Model and the Fuzzy Control Model, based on rigorous results from the control theory, and verified by the controllability and adaptivity of a distributed visual tracking application. The results show validation of the framework, i.e., critical application quality parameter can be preserved via controlled adaptation.

In this paper, packet scheduling with maximum delay constraints is considered with the objective to minimize average transmit power over Gaussian channels. The main emphasis is on deriving robust schedulers which do not rely on the knowledge of the source arrival process. Towards that end, we first show that all schedulers (robust or otherwise) which guarantee a maximum queuing delay for each packet are equivalent to a time-varying linear filter. Using the connection between filtering and scheduling, we study the design of optimal power minimizing robust schedulers. Two cases, motivated by filtering connection, are studied in detail. First, a time-invariant robust scheduler is presented and its performance is completely characterized. Second, we present the optimal time-varying robust scheduler, and show that it has a very intuitive time water-filling structure. We also present upper and lower bounds on the performance of power-minimizing schedulers as a function of delay constraints. The new results form an important step towards understanding of the packet time-scale interactions between physical layer metric of power and network layer metric of delay.

In many blind watermarking proposals, the unwatermarked host data is viewed as unavoidable interference. Recently, however, it has been shown that blind watermarking corresponds to communication with side information (i.e., the host data) at the encoder. For a Gaussian host data and Gaussian channel, Costa showed that blind watermarking can theoretically eliminate all interference from the host data. Our previous work presented a practical blind watermarking scheme based on Costa's idea and called "scalar Costa scheme" (SCS). SCS watermarking was analyzed theoretically and initial experimental results were presented. This paper discusses further practical implications when implementing SCS. We focus on the following three topics: (A) high-rate watermarking, (B) low-rate watermarking, and (C) restrictions due to finite codeword lengths. For (A), coded modulation is applied for a rate of 1 watermark bit per host-data element, which is interesting for information-hiding applications. For (B), low rates can be achieved either by repeating watermark bits or by projecting them in a random direction in signal space (spread-transform SCS). We show that spread-transform SCS watermarking performs better than SCS watermarking with repetition coding. For (C), Gallager's random-coding exponent is used to analyze the influence of codeword length on SCS performance.

model of an incoherent scatter radar. This model is implemented in a hierarchical software system, which serves to isolate hardware and low-level software implementation details from higher levels of the system. Inherent in this is the idea that implementation details can easily be changed in response to technological advances. MIDAS is an evolutionary system, and the MIDAS hardware has, in fact, evolved while the basic software model has remained unchanged. From the earliest days of MIDAS, it was realized that some functions implemented in specialized hardware might eventually be implemented by software in a generalpurpose computer. MIDAS-W is the realization of this concept. The core component of MIDAS-W is a Sun

For good classification preprocessing is a key step. Good preprocessing reduces the noise in the data and retains most information needed for classification. Poor preprocessing on the other hand makes classification almost impossible. In this paper we try to find good preprocessing for a special type of outlier detection problem, machine diagnostics in which we try to distinguish between a target class and all other possible patterns. We will consider measurements on a water pump under both, normal and abnormal conditions. We use a novel data domain description method to get an indication of the complexity of the normal class in this data set and how well it is expected to be distinguishable from the abnormal data.

Ultrasonic strain imaging is usually based on displacement estimates computed using finitelength sections of the RF ultrasound signal. Amplitude variations in the ultrasound are known to cause a perturbation in the location at which the displacement estimate is valid. If this goes uncorrected, it is an important source of estimation noise, which is amplified when the displacement field is converted into a strain image. We present a study of this effect based on theoretical analysis and practical experiments. A correction method based on the analysis is tested on phase and correlation coefficient strain imaging, and compared to the log compression technique from an earlier study. The performance is also compared against adaptive strain estimation. Results indicate that the new correction yields a substantial reduction in estimation noise. 1

Abstract not found

Most search engines return alW of unwanted information. A more thorough filrough process can be performed on this information to sort out therelL ant documents. A new methodcal)x Frequency Domain Scoring (FDS), which is based on the Fourier Transform is proposed.

Animal visual systems have solved the problem of limited resources by allocating more processing power to central than peripheral vision. Foveation considerably reduces the amount of data per image by progressively decreasing the resolution at the periphery while retaining a sharp center of interest. This strategy has important applications in the design of autonomous systems for navigation, tracking and surveillance. Central to foveation is a space-variant Gaussian filtering scheme that gradually blurs out details as the distance to the image center increases. Unfortunately Gaussian convolution is a computationally expensive operation, which can severely limit the real-time applicability of foveation. In the space-variant case, the problem is even more difficult as traditional techniques such as the fast Fourier transform cannot be employed because the convolution kernel is different at each pixel. We show that recursive filtering, which was introduced to approximate Gaussian convolution, can be extended to the spacevariant case and leads to a very simple implementation that makes it ideal for that application. Three main recursive algorithms have emerged, produced by independent derivation methods. We assess and compare their performance in traditional filtering applications and in our specific space-variant case. All three methods drastically cut down the cost of Gaussian filtering to a limited number of operations per pixel that is independent of the scale selected. In addition we show that two of those algorithms have excellent accuracy in that the output they produce differs from the output obtained performing real Gaussian convolution by less than 1%. r 2003 Elsevier Ltd. All rights reserved. 1.

This paper presents a new method for sound synthesis. The method consists in mapping the histograms sequence of a cellular automata evolution onto a sound spectrogram. The data obtained from the histograms are in the form of sound spectral structures evolving in time in a natural fashion. The main problem of cellular automatas is the difficulty of control due to its unpredictability property. This mapping offers significant controllability characteristics which allow flexible processes for sound and instrument design. The sounds obtained with this mapping present natural behaviour and are capable of simulate acoustic instruments and other real sounds. 1.