Andrew G. Dempster

Use of minimum-adder multiplier blocks in FIR digital filters

The computational complexity of VLSI digital filters using fixed point binary multiplier coefficients is normally dominated by the number of adders used in the implementation of the multipliers. It has been shown that using multiplier blocks to exploit redundancy across the coefficients results in significant reductions in complexity over methods using canonic signed-digit (CSD) representation, which in turn are less complex than standard binary representation. Three new algorithms for the design of multiplier blocks are described: an efficient modification to an existing algorithm, a new algorithm giving better results, and a hybrid of these two which trades off performance against computation time. Significant savings in filter implementation cost over existing techniques result in all three cases. For a given wordlength, it was found that a threshold set size exists above which the multiplier block is extremely likely to be optimal. In this region, design computation time is substantially reduced. >

Analysis of infected blood cell images using morphological operators

Abstract This work describes a system for detecting and classifying malaria parasites in images of Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. The first aim of our system is to detect the parasites by means of an automatic thresholding based on a morphological approach. A major requirement of the whole system is an efficient method to segment cell images. So the paper also introduces a morphological approach to cell image segmentation, that is, more accurate than the classical watershed-based algorithm. We have applied grey scale granulometries based on opening with disk-shaped elements, flat and hemispherical. We have used a hemispherical disk-shaped structuring element to enhance the roundness and the compactness of the red cells improving the accuracy of the classical watershed algorithm, while we have used a disk-shaped flat structuring element to separate overlapping cells. These methods make use of knowledge of the red blood cell structure, that is, not used in existing watershed-based algorithms. The last step of the system is classifying the parasites: we present two different classification methods, one based on morphological operators and another one based on colour histogram similarity. The framework is described with the help of a running example and then validated against ‘expert’ analysis of several more images.

Segmentation of blood images using morphological operators

This work describes a part of a malarial image processing system for detecting and classifying malaria parasites in images of Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. A major requirement of the system is an efficient method to segment cell images. This paper introduces morphological approach to cell image segmentation more accurate than the classical watershed-based algorithm. We applied grey scale granulometries based on opening with disk-shaped elements, flat and non-flat. We used a non-flat disk-shaped structuring element to enhance the roundness and compactness of the red cells improving the accuracy of the classical watershed algorithm, while we have used a flat disk-shaped structuring element to separate overlapping cells. These methods make use of knowledge of the red blood cell structure that is not used in existing watershed-based algorithms.

Designing multiplier blocks with low logic depth

The depth of logic in an integrated circuit, particularly a CMOS circuit, is highly correlated both with power consumption and degraded switching speed. Hence, designs with low logic depth can aid in reducing power consumption and increasing switching speed. In this paper we demonstrate how new and modified algorithms have been used to design multiplier blocks with low logic depth and power consumption.

Extended results for minimum-adder constant integer multipliers

By introducing simplifications to multiplier graphs we extend the previous work on minimum adder multipliers to five adders and show that this is enough to express all coefficients up to 19 bits. The average savings are more than 25% for 19 bits compared with CSD multipliers. The simplifications include addition reordering and vertex reduction to see that different graphs can generate the same coefficient sets. Thus, fewer graphs need to be evaluated. A classification of the graphs reduces the effort to search the coefficient space further.

How feasible is the use of magnetic field alone for indoor positioning

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.

Computer vision for microscopy diagnosis of malaria

This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Differences in RSSI readings made by different Wi-Fi chipsets: A limitation of WLAN localization

Wi-Fi positioning has found favour in environments which are traditionally challenging for GPS. The currently used method of Wi-Fi fingerprinting assumes that the devices used for training and locating perform identically. We have undertaken an experiment to determine how different devices behave in an empirical controlled test to identify the challenges and limitations which Wi-Fi fingerprinting positioning systems will face when deployed across many devices. We found that they performed significantly differently in respect to the mean reported signal strength — even those which came from the same vendor. We also found that multiple samples of the same device do not perform identically. Furthermore, it was found that certain devices were entirely unsuitable for positioning as they reported signal strength values uncorrelated with distance from the transmitter. Some other devices behaved in a way that made them poor candidates for use in fingerprinting. Temporal patterns were found in some wireless cards which suggest that filtering should be used. The tests also found that the use of 5GHz band signals had the potential to improve the accuracy of Wi-Fi location due to its higher stability compared to 2.4GHz. Ultimately however, the accuracy of Wi-Fi fingerprinting is limited due to many factors in the hardware and software design of Wi-Fi devices which affect the reported signal strength.

Parasite detection and identification for automated thin blood film malaria diagnosis

This paper investigates automated detection and identification of malaria parasites in images of Giemsa-stained thin blood film specimens. The Giemsa stain highlights not only the malaria parasites but also the white blood cells, platelets, and artefacts. We propose a complete framework to extract these stained structures, determine whether they are parasites, and identify the infecting species and life-cycle stages. We investigate species and life-cycle-stage identification as multi-class classification problems in which we compare three different classification schemes and empirically show that the detection, species, and life-cycle-stage tasks can be performed in a joint classification as well as an extension to binary detection. The proposed binary parasite detector can operate at 0.1% parasitemia without any false detections and with less than 10 false detections at levels as low as 0.01%.

Malaria Parasite Detection in Peripheral Blood Images

This paper investigates the possibility of computerised diagnosis of malaria and describes a method to detect malaria parasites (Plasmodium spp) in images acquired from Giemsa-stained peripheral blood samples using conventional light microscopes. Prior to processing, the images are transformed to match a reference image colour characteristics. The parasite detector utilises a Bayesian pixel classifier to mark stained pixels. The class conditional probability density functions of the stained and the non-stained classes are estimated using the non-parametric histogram method. The stained pixels are further processed to extract features (histogram, Hu moments, relative shape measurements, colour auto-correlogram) for a parasite/non-parasite classifier. A distance weighted K-nearest neighbour classifier is trained with the extracted features and a detailed performance comparison is presented. Our method achieves 74% sensitivity, 98% specificity, 88% positive prediction, and 95% negative prediction values for the parasite detection.