In this paper we improve previous bounds on expected measures of AVL trees by using fringe analysis. A new way of handling larger tree collections that are not closed is presented. An inherent difficulty posed by the transformations necessary to keep the AVL tree balanced makes its analysis

As wireless sensor networks continue to grow in size, we are faced with the prospect of emerging wireless networks with hundreds or thousands of nodes. Geographic routing algorithms are a promising alternative to tradition ad hoc routing algorithms in this new domain for point-to-point routing, but deployments of such algorithms are currently uncommon because of some practical difficulties.

Service almost £1 billion (1). There is evidence that the incidence of falls can be reduced by strength and balance training (SBT) (2), an example of which is Tai Chi, a relaxing activity that consists of slow, gentle, flowing movements. However, most older people are not sufficiently aware

Abstract not found

Building a more sustainable sensor network via protocol innovation

devices in the Internet of Things (IoT) and other data sources are increasingly bridging the gap between the physical and digital world by providing fine-grained data about real-world events. Enterprise software systems are adopting the paradigm of event-based systems (EBS) to enable them to react to meaningful events in a timely manner. Smart supply chains fusing dynamic sensor data with information provided by backend-systems are one such example of event-based enterprise systems. Monitoring their global state in an effective way for runtime governance remains an open research challenge: providing the required type of information while trading off precision for costs. We previously introduced application-specific integrated aggregation (ASIA) as a means for collecting metadata in distributed event-based systems. In this paper, we show how ASIA can support IT Service Management in monitoring and governing decentralized event-based enterprise systems at runtime. We present a dashboard based on industry-strength technology as proof of concept and discuss how to integrate usage statistics provided on-the-fly by ASIA into metrics for runtime governance. We evaluate our monitoring approach in terms of performance, scalability and precision. I.

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sam-pling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bit-depths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement

Experiences acquired in the design of RoboCup teams:

In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information,