We explore the idea of objects that, under some contextual conditions, become less “agreeable” to the user. Such an object may lower its performance level, become less usable, or start producing discouraging stimuli. Yet, it always remains fully functional and usable. We call such objects resistive. We describe three ways, in which making an object resistive may add value to the user. The resistive object may offer feedback on the user's past improper actions. It may encourage the user to improve her behavior at the moment. Or, it may offer the user an instant, discouraging evaluation of a bad operation on the object, which she is about to perform. In each case, resistivity is meant to help the user avoid undesirable behavior and choose a better course of action. In the paper we introduce the concepts of personal and community-wide resistive objects and provide a model of object resistivity. We exemplify the concepts with a number of ideas for resistive objects. 'otably, the examples may serve as an inspiration for quite a few experiments with end users. Finally, we hint at selected research issues.

We present the concept, design, and implementation of the proxy mote, a Linux-based TinyOS platform able to execute a TinyOS application in a fully functional way. The main use case for the proxy mote is to expose a non-TinyOS (âlegacyâ) sensor/actuator device to TinyOS applications. This is achieved by complementing the proxy mote with a device-specific driver. Multiple proxy motes, each representing its own device, run in parallel on a single PC and communicate among themselves. Thus a distributed, multi-node TinyOS application can use a complex, heterogeneous legacy sensor/actuator installation. At runtime, a proxy mote is a Linux process. Multiple proxy motes form an intra-PC ânetworkâ and exchange Active Message packets over a dedicated infrastructural Linux process that acts as a switch. We include performance results for the proxy mote and proxy network, for typical TinyOS applications. The results demonstrate the feasibility of the approach.

The paper targets heterogeneous sensor-actuator networks, in which nodes differ as to resources (sensors and actuators) they are equipped with. Each node contributes its specific sensors and actuators to be used by applications. The key assumption of "opportunistic pervasive computing" is that the actual mix of nodes (and that of available resources) is not known in advance to the programmer. An opportunistic pervasive computing application is supposed to take the best advantage of whatever sensors and actuators happen to be available in the network. The paper presents a technique that can be used in middleware layers supporting such applications. The technique uses virtual machines to orderly expose sensor and actuator resources of a node to the programmer. The virtual machines are domain-oriented, node specific and able to work with the resources at multiple levels of abstraction. They can be implemented on severely constrained nodes (e.g., of the TinyOS class)

The paper presents a programming model for a new pervasive computing middleware. The middleware, called ROVERS, targets an environment composed of tiny, resource-constrained, wirelessly communicating nodes embedded into everyday objects. The environment is heterogeneous in that each node is equipped with a unique set of sensors and actuators. The des establish an ad-hoc network and contribute their specific resources. The ROVERS layer transforms the network into a distributed pervasive computing platform. The ROVERS application is an evolving tree of cooperating, mobile micro-agents. The tree adapts to available resources and the current context. It is largely decoupled from the concept of the physical node. ROVERS provides the programmer with implicit resource discovery, inter-agent communications with logical addressing, minimization of application-generated traffic, ontology-driven representation of sensor and actuator resources, as well as support for component-based programming. The programming model lends itself to an implementation for a miniature operating system, like TinyOS.

Fractal coding and classi#ed transform coding exhibit strong structural similarity, but they use di#erenttypes of redundancy in image data: piecewise self-similarity in one case and local correlation in the other. Comparing performance of the two techniques leads to a quantitative, compression-oriented de#nition of piecewise self-similarity. The amount of piecewise self-similarity is evaluated for sample images. For a moderate number of domain blocks, classi#ed transform coding consistently outperforms fractal coding, and the images are not found to be piecewise self-similar. As the number of domain blocks increases, the performance gap becomes negligible. 1. INTRODUCTION Fractal coding #1# is a recent approach to image compression. Its basic premise is that images exhibit a type of redundancy called piecewise self-similarity. In a piecewise self-similar image, a blockofwaveform data can be related to another one so that the two resemble each other. Compression is achieved if one of th...

A part of a fractal code is an assignment of a domain block to every range block. The assignment is used to construct the dependence graph of a fractal code. The vertices of the graph represent the range blocks. Two vertices z and y are connected by a directed edge from y to x if the range block y is overlapped, fully or partially, by the domain block assigned to the range block x. An algorithm to analyze the structure of the dependence graph is presented. The exposed structure of the graph can be used for three different purposes. The first one is convergence analysis: the affine transformations linking domain and range blocks can be classified into those that affect convergence and those that do not. The second one is decoding time reduction: certain range blocks can be reconstructed in a non-iterative way. The third one is improving upon collage coding: the affine transformations for some range blocks can be optimized based on the domain blocks extracted from the reconstructed rather than the original image.

A fractal encoder processes the original source vector by selecting a contractive map whose unique fixed point (attractor) approximates the original. A description of the map is transmitted to the decoder. The decoder iterates the map to recover the attractor. The task of picking the map is called the inverse problem. The predominantly used, suboptimum, technique to solve the inverse problem is collage coding. In collage coding, only the first decoding iteration is optimized. We propose two new suboptimum algorithms for the inverse problem. In our approach, the encoder imitates the iterative operation of the decoder. At each step, however, a new map is used so as to keep the sequence of approximations close to the original. If the sequence converges, then the limit is an attractor, and it is a good candidate for the reconstructed vector. The description of the map corresponding to the attractor is sent to the decoder.>

The problem of entropy-constrained multiple-description scalar quantizer design is posed as an optimization problem, necessary conditions for optimality are derived, and an iterative design algorithm is presented. Performance results are presented for a Gaussian source, along with comparisons to the multiple-description rate distortion bound and a reference system.>

Fractal image compression using self-affine transformations has recently drawn considerable attention. Although some elements of the technique have a well-established foundation, many issues remain unclear. We consider the attractors that are obtained by varying the parameters of the contractive transformation. We show that the parameters can be divided into two groups and that if the parameters in the first group are fixed, the set of attractors obtained by varying the parameters in the second group is a vector space. Based on this observation, an improvement to the collage coding technique for encoding data is obtained. We then present a coder, referred to as the classified transform coder, which is structurally limited in the same way as the fractal coder. However, in the classified transform coder, the design of the pool of subspaces is directly addressed. Finally, some performance results are presented for the classified transform coder.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.