Muhammad Ali Akbar

A multi-sensing reconfigurable platform for gas applications

The production of hazardous gases due to different physicochemical reactions in gas industries have raised the need of autonomous sensor tag. However the adaptation of remotely controlled sensor tag is still suffering with many challenges, among which power consumption, selectivity and durability are the most prominent problems in gas application. Therefore in this paper, a reconfigurable multi-sensing platform is presented for gas applications. A low power ring-oscillator based temperature and tin-oxide based gas sensors are utilized to indicate the temperature and type of the gas with a total power of few μWs. The processing unit is designed on a heterogeneous ZynQ platform and a passive radio frequency identification (RFID) tag is designed to provide communication between the sensors and the processing unit.

Design and Performance Evaluation of a Committee Machine for Gas Identification

Selecting the best classifier plays a significant role in the current electronic nose systems that can be deployed for gas applications. For this purpose, this paper presents an empirical study on the performance of three different classifiers, namely, binary decision tree (BDT), K-nearest neighbours (KNN) and extended nearest neighbours (ENN) on gas identification. It has been observed that with BDT and ENN a maximum classification accuracy of up to 96.4\(\%\) and 96.7\(\%\) can be obtained, respectively, whereas in the case of KNN up to 97.0\(\%\) accuracy can be achieved. In addition to the individual classifiers, a committee machine (CM) based on the three classifiers has been designed, with and without feedback mechanism to determine the improvement gained by combining these classifiers. The performance attained by the CM with feedback is 97.44\(\%\) and it is slightly better than the one without feedback, that is 97.2\(\%\).

Image stitching system with scanning microscopy for histopathological applications

Histopathological analysis of biopsy or surgical specimen is a common clinical practice for diagnostic purposes. Essentially, the process involves slicing the biopsy or surgical sample into very thin slices, placing them on glass slides and viewing them under microscopes. Predominantly, the placement, positioning, and view control is done manually by the pathologists in most of the clinics and hospitals because of which the diagnosis remains heavily dependents upon the experience and performance of the pathologist. Moreover, the slide scanning relies predominantly on the slide placement accuracy. A misaligned slide will create misaligned images which can either miss out information or have blank artifacts due to image frame placement methodology. In this paper, a simple ‘add-on’ system has been presented that can be used to scan single slide with moderate speed and produces the image on a Virtual reality headset to provide the submerged feeling. Most importantly, it utilizes advanced image stitching algorithms to align the frames from the captured video stream of the slide to produce a very accurate image with a very large size. The stitching is done using the standard feature-based algorithms which have been modified in this work by incorporating affine blending maps to combine the features into final image. It has been found that the image stitching algorithm provides the stitched image with less than 2% error for the given test images.