Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

Parkinson's disease is a neural degenerative disease. It slowly progresses from mild to severe stage, resulting in the degeneration of dopamine cells of neurons. Due to the deficiency of dopamine cells in the brain, it leads to a motor (tremor, slowness, impaired posture) and non-motor (speech, olfactory) defects in the body. Early detection of Parkinson's disease is a difficult chore as the symptoms of disease appear overtime. However, different diagnostic systems have contributed towards disease detection by considering gait, tremor and speech characteristics. Recent work has shown that speech impairments can be considered as a possible predictor for Parkinson's disease classification and remains an open research area. The speech signals show major differences and variations for Parkinson patients as compared to normal human beings. Therefore, variation in speech should be modeled using acoustic features to identify these variations. In this research, we propose three methods-the first method employs a transfer learning-based approach using spectrograms of speech recordings, the second method evaluates deep features extracted from speech spectrograms using machine learning classifiers and the third method evaluates simple acoustic feature of recordings using machine learning classifiers. The proposed frameworks are evaluated on a Spanish dataset pc-Gita. The results show that the second framework shows promising results with deep features. The highest 99.7% accuracy on vowel \o\ and read text is observed using a multilayer perceptron. Whereas 99.1% accuracy observed on vowel \i\ deep features using random forest. The deep feature-based method performs better as compared to simple acoustic features and transfer learning approaches. The proposed methodology outperforms the existing techniques on the pc-Gita dataset for Parkinson's disease detection.

In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L.

Abstract__-__With the advancements in telecommunication,it is generally believed that we are getting short of spectrum. If we look at frequency usage chart it is practically viewed that spectrum usage is concentrated at some portion while some of frequency bands are left unutilized, means actually we have spectrum abundance problem lies in spectrum management policies. Cognitive radio technology provides the efficient way to utilize the electromagnetic spectrum. Physical layer of cognitive radio provides the facility to sense spectrum to determine the spectrum holes. Literature has presented various techniques to sense the spectrum but still there is room for researchers in this field. This paper provides the implementation issues for several spectrum sensing techniques and performance analysis for energy detection, matched filter detection, cyclotationary feature based detection and eigenvalue based detection.Index Terms -Cognitive Radios, AWGN, eigenvalues and eigenfunctions, radio spectrum management, wireless networks.