Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.

Integrated Multi-Trophic Aquaculture (IMTA) is an advance method of aquaculture which combines species with different nutritional needs to live together. The combination between aquatic live and crops is called aquaponics. Aquatic waste that normally removed by biofilters in normal aquaculture practice will be absorbed by crops in this practice. Aquaponics have few common components and growbed provide the best filtration function. In growbed a siphon act as mechanical structure to control water fill and flush process. Water to the growbed comes from fish tank with multiple flow speeds based on the pump specification and height. Too low speed and too fast flow rate can result in siphon malfunctionality. Pumps with variable speed do exist but it is costly. Majority of the aquaponic practitioner use single speed pump and try to match the pump speed with siphon operational requirement. In order to remove the matching requirement some control need to be introduced. Preliminarily this research will show the concept of fill-and-flush for multiple pumping speeds. The final aim of this paper is to show how water level management can be done to remove the speed dependency. The siphon tried to be controlled remotely since wireless data transmission quite practical in vast operational area. Fog architecture will be used in order to transmit sensor data and control command. This paper able to show the water able to be retented in the growbed within suggested duration by stopping the flow in once predefined level.

Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.

Microwave technology is very promising tool for breast cancer detection. Microwave transmits and receives UWB signals. UWB signals carries information of the breast cancer. UWB signals need to be pre-processed in order to remove irrelevant and redundant features. Feature extraction and feature selection methods are mostly used to remove the unwanted features. In this paper, a statistically modelled feature selection (SMFS) method is proposed for microwave breast cancer detection. Initially, performance of different feature extraction and feature selection method are analysed using Anova test (p-value) and machine learning (SVM, DT, PNN, NB) accuracy. The best feature extraction and feature selection methods are combined and tested. Based on the performance of feature extraction and feature selection method, Combined Neighbour Component Analysis (feature selection) and Statistical features (feature extraction) are combined and tested. This method is able to achieve up to 85%. The result proves two stage methods are able to improve the accuracy compared to single stage method. Therefore, SMFS is able to detect breast cancer efficiently.

Cache replacement policies in chip multiprocessors (CMP) have been investigated extensively and proven able to enhance shared cache management. However, competition among multiple processors executing different threads that require simultaneous access to a shared memory may cause cache contention and memory coherence problems on the chip. These issues also exist due to some drawbacks of the commonly used Least Recently Used (LRU) policy employed in multiprocessor systems, which are because of the cache lines residing in the cache longer than required. In image processing analysis of for example extra pulmonary tuberculosis (TB), an accurate diagnosis for tissue specimen is required. Therefore, a fast and reliable shared memory management system to execute algorithms for processing vast amount of specimen image is needed. In this paper, the effects of the cache replacement policy in a partitioned shared cache are investigated. The goal is to quantify whether better performance can be achieved by using less complex replacement strategies. This paper proposes a Middle Insertion 2 Positions Promotion (MI2PP) policy to eliminate cache misses that could adversely affect the access patterns and the throughput of the processors in the system. The policy employs a static predefined insertion point, near distance promotion, and the concept of ownership in the eviction policy to effectively improve cache thrashing and to avoid resource stealing among the processors.