Simplified method for the measurement of segmental colonic transit time

Segmental colonic transit has been measured in 101 patients. Two MBq of 111Indium absorbed on resin pellets and encapsulated in an enteric coated capsule was given at 7 00 am. Hourly images during the first day, and three images during each subsequent day were acquired for up to three days. Using all scan and patient data the scans were categorised in one of the five patterns of colonic transit: normal, rapid, right delay, left delay, or generalised delay. The geometric centres and per cent activity at each time point was compared between the five groups of colonic transit patients to find the best time for imaging and so to distinguish the five groups. During the first day, early images did not help in diagnosis of patterns of transit, however, in the later images (six hours onwards after the ingestion of the activity) the rapid transit groups could be identified. Images at 27 and 51 hours were both required to distinguish all five groups of patients from each other. Only in the 'normal' transit patients was there some excretion of the activity during the course of the second day, otherwise there was no difference in the images taken in the course of a day (second or third day). A simplified protocol requires a minimum of three images to distinguish all five patterns of colonic transit. The activity should be ingested in the morning (7 00 am) and the first image taken at the end of the working day (8-10 hours after ingestion), the second image on the morning of the second day, and the third image during the course of the third day. This simple protocol would provide all the clinically relevant information necessary for correct classification of the colonic transit.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

An integrated modeling approach to predict flooding on urban basin.

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.