Electro/Information Technology 

Abstract: Classification of brain tumor is the heart of the computer-aided diagnosis (CAD) system designed to aid the radiologist in the diagnosis of such tumors using Magnetic Resonance Image (MRI). In this paper, we present a framework for classification of brain tumors in MRI images that combines statistical features and neural network algorithms. This algorithm uses region of interest (ROI), i.e. the tumor segment that is identified either manually by the technician/radiologist or by using any of the ROI segmentation techniques. We focus on feature selection by using a combination of the 2D Discrete Wavelet Transform (DWT) and 2D Gabor filter techniques. We create the features set using a complete set of the transform domain statistical features. For classification, back propagation neural network classifier has been selected to test the features selection impact. To do so, we used a large dataset consisting of 3,064 slices of T1-weighted MRI images with three types of brain tumors, Meningioma, Glioma, and Pituitary tumor. We obtained a total accuracy of 91.9%, and specificity of 96%, 96.29%, and 95.66% for Meningioma, Glioma, and Pituitary tumor respectively. Experimental results validate the effectiveness of the features selection method and indicate that it can compose an effective feature set to be used as a framework that can be combined with other classifications technique to enhance the performance.

Abstract: Over the years, Automated Image Analysis Systems (AIAS) have been developed for pavement surface analysis and management. The cameras used by most of the AIAS are based on Charge-Coupled Device (CCD) image sensors where a visible ray is projected. However, the quality of the images captured by the CCD cameras was limited by the inconsistent illumination and shadows caused by sunlight. To enhance the CCD image quality, a high-power artificial lighting system has been used, which requires a complicated lighting system and a significant power source. In this paper, we will introduce an efficient and more economical approach for pavement distress inspection by using laser imaging. After the pavement images are captured, regions corresponding to potholes are represented by a matrix of square tiles and the estimated shape of the pothole is determined. The vertical, horizontal distress measures, the total number of distress tiles and the depth index information are calculated providing input to a three-layer feed-forward neural network for pothole severity and crack type classification. The proposed analysis algorithm is capable of enhancing the pavement image, extracting the pothole from background and analyzing its severity. To validate the system, actual pavement pictures were taken from pavements both in highway and local roads. The experimental results demonstrated that the proposed model works well for pothole and crack detection.

Abstract: Mobile devices such as cellular phones are now capable of storing a significant amount of multimedia files and personal data. However these devices still use traditional directory browsing which offers little in terms of usability for searching and retrieving specific files. In this paper we design and implement a prototype media search engine for a mobile phone. We use modified versions of open source search and metadata extraction libraries along with scrollable instant search results and an auto-fill feature. The prototype has acceptable performance in terms of resource requirement and execution speed. Potential applications may include multimedia file searching in distributed mobile computing and peer-to-peer file sharing environments.

Abstract: The utility of unmanned aerial vehicles (UAVs) has significantly disrupted aviation-related industries. As technology and policy continue to develop, this disruption is likely to continue and become even larger in magnitude. A specific technology poised to disrupt industry is UAV swarm. UAV swarm has the potential to distribute tasks and coordinate operation of many drones with little to no operator intervention. This paper surveys literature regarding UAV swarm and proposes a swarm architecture that will allow for higher levels of swarm autonomy and reliability by utilizing cellular mobile network infrastructure. Additionally, this paper chronicles initial testbed development to meet this proposed architecture. Specific development of higher levels of autonomous swarms with UAV-to-UAV communication and coordination ability is central to advancing the utility of UAV swarms. The use of cellular mobile framework alleviates many limiting factors for UAVs including range of communication, networking challenges, size-weight-and-power (SWaP) considerations, while leveraging a robust and reliable infrastructure for machine to machine (M2M) communication proposed by 5G systems.