Parallel pipelined array architectures for real-time histogram computation in consumer devices

The real-time parallel computation of histograms using an array of pipelined cells is proposed and prototyped in this paper with application to consumer imaging products. The array operates in two modes: histogram computation and histogram reading. The proposed parallel computation method does not use any memory blocks. The resulting histogram bins can be stored into an external memory block in a pipelined fashion for subsequent reading or streaming of the results. The array of cells can be tuned to accommodate the required data path width in a VLSI image processing engine as present in many imaging consumer devices. Synthesis of the architectures presented in this paper in FPGA are shown to compute the real-time histogram of images streamed at over 36 megapixels at 30 frames/s by processing in parallel 1, 2 or 4 pixels per clock cycle.

Implementation of Parallel (p,q) Counters for High Speed Digital Multipliers

Both the (5,3) counter and (2,2,3) counter multiplication techniques are investigated for the efficiency of their operation speed and the viability of the architectures when implemented in a fast bipolar ECL technology. The implementation of the counters in series-gated ECL and threshold logic are contrasted for speed, noise immunity and complexity, and are critically compared with the fastest practical design of a full-adder. A novel circuit technique to overcome the problems of needing high fan-in input weights in threshold circuits through the use of negative weighted inputs is presented. The authors conclude that a (2,2,3) counter based array multiplier implemented in series-gated ECL should enable a significant increase in speed over conventional full adder based array multipliers.

Parallel Architectures for High Speed Multipliers

The authors compare various array multiplier architectures based on (p,q) counter circuits. The tradeoff in multiplier design is always between adding complexity and increasing speed. It is shown that by using a (2,2,3) counter cell it is possible to gain a significant increase in speed over a conventional full-adder, carry-save array based approach. The increase in complexity should be easily accommodated using modern emitter-coupled-logic processes.

Mites Parasitic on Australasian and African Spiders Found in the Pet Trade; a Redescription of Ljunghia pulleinei Womersley

Parasitic mites associated with spiders are spreading world-wide through the trade in tarantulas and other pet species. Ljunghia pulleinei Womersley, a mesostigmatic laelapid mite originally found in association with the mygalomorph spider Selenocosmia stirlingi Hogg (Theraphosidae) in Australia, is redescribed and illustrated on the basis of specimens from the African theraphosid spider Pterinochilus chordatus (Gersta¨cker) kept in captivity in the British Isles (Wales). The mite is known from older original descriptions of Womersley in 1956; the subsequent redescription of Domrow in 1975 seems to be questionable in conspecificity of treated specimens with the type material. Some inconsistencies in both descriptions are recognised here as intraspecific variability of the studied specimens. The genus Arachnyssus Ma, with species A. guangxiensis (type) and A. huwenae, is not considered to be a valid genus, and is included in synonymy with Ljunghia Oudemans. A new key to world species of the genus Ljunghia is provided.

Good practice transfer within small construction specialist trade contractors

Government and institutionally-driven ‘good practice transfer’ initiatives are consistently presented as a means to enhance construction firm and industry performance. Two implicit tenets of these initiatives appear to be: knowledge embedded in good practice will transfer automatically; and, the potential of implementing good practice will be capitalised regardless of the context where it is to be used. The validity of these tenets is increasingly being questioned and, concurrently, more nuanced knowledge production understandings are being developed which recognise and incorporate context-specificity. This research contributes to this growing, more critical agenda by examining the actual benefits accrued from good practice transfer from the perspective of a small specialist trade contracting firm. A concept model for successful good practice transfer is developed from a single longitudinal case study within a small heating and plumbing firm. The concept model consists of five key variables: environment, strategy, people, technology, and organisation of work. The key findings challenge the implicit assumptions prevailing in the existing literature and support a contingency approach that argues successful good practice transfer is not just adopting and mechanistically inserting into the firm, but requires addressing ‘behavioural’ aspects. For successful good practice transfer, small specialist trade contracting firms need to develop and operationalise organisation slack, mechanisms for scanning external stimuli and absorbing knowledge. They also need to formulate and communicate client-driven external strategies; to motive and educate people at all levels; to possess internal or accessible complementary skills and knowledge; to have ‘soft focus’ immediate/mid-term benefits at a project level; and, to embed good practice in current work practices.

Registering scents as community trade marks

While the registrability of scents as Community trade marks has become source of much controversy, the possibility of trademarking scents represents a great potential for the industry. In the aftermath of the Sieckmann case, which has raised the threshold of registrability for scent marks, companies have refrained from submitting new smell-mark applications. Despite the difficulties in registering scents as trademarks, however, it is not impossible to meet the Sieckmann criteria and file successful scent mark applications. This article explains how this could be possible; it reviews all objections in registering scents as trademarks and brings new light into this topic by way of a comprehensive analysis of the conditions under which scents can be registered as Community trade marks.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.