Weighted norm inequalities for a nonlinear transform

We present various weighted norm inequalities for a nonlinear transform in different normed spaces. As particular cases, we obtain some results of Lebedev–Milin, Saitoh, and Burbea.     

A review on amorphous noble-metal-based electrocatalysts for fuel cells: Synthesis,characterization, performance,and future perspective

An urgent need for cleaner energy alternatives has driven the research of fuel cells, and consequently, electrocatalysts. Noble metals such as Pt and Pd are well known as the most suitable catalysts for fuel cell reactions. However, because of their high costs, much effort has been invested to improve their efficiency. Amorphous noble metal nanoparticles are promising electrocatalysts because of their abundant uncoordinated active sites that result from their long-range disordered lattice. Researches on amorphous Pt- and Pd-based catalysts have progressed well with several different synthesis routes. This review aims to systematically study the differences, advantages, and disadvantages of these methods and the resulting characteristics, performance in fuel cell applications, and future prospects of the amorphous Pt- and Pd-based catalysts.     

Visual-GPS combined ‘follow-me’ tracking for selfie drones

ABSTRACT

The ‘follow-me’ mode, where the drone autonomously follows and captures the user or target, is a new and attractive feature for camera drones, especially selfie drones. For this purpose, today’s commercial drones use the difference between two GPS data in the drone and user-side mobile GCS, e.g. a smartphone, but the targeting performance is often not satisfactory due to the inaccuracy of the GPS data, ranging from a few to tens of meters. Visual tracking can be considered for a solution to this problem, but the reliability of visual tracking is still questionable for long-term tracking in un-expected operating environments. The paper proposes a hybrid approach that combines the high accuracy of a visual tracking algorithm in short-term tracking and the reliability of GPS-based one in long-term tracking. The experiment with our prototype drone system demonstrates that the proposed combined approach can accomplish the follow-me operation very successfully, capturing the target in the center of video contents over 50% higher accuracy than the GPS-based ones. Also, the extreme scenario experiments verify the system can recover vision tracking failure and Wi-Fi failure quickly in a short-term, e.g. 3–7?s.     

A genetic algorithm-based multi-objective optimization of an artificial neural network classifier for breast cancer diagnosis

The conventional technique for diagnosing the breast cancer disease relies on human experiences to identify the presence of certain pattern from the database. It is time-consuming and incurs unnecessary burden to radiologists. This work proposes a genetic algorithm-based multi-objective optimization of an Artificial Neural Network classifier, namely GA-MOO-NN, for the automatic breast cancer diagnosis. It performs a simultaneous search for the significant feature subsets and the optimum architecture of the network. The combination of ANN’s parameters with feature selection to be optimized by Genetic Algorithm is novel. The Pareto-optimality with new ranking approach is applied for simultaneous minimizations of two competing objectives: the number of network‘s connections and squared error percentage of the validation data. Result shows that the algorithm with the proposed combination of objectives has achieved the best and average, 98.85 and 98.10 % accuracy of classification, respectively, on breast cancer dataset which outperform most systems of other works found in the literature.     

Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach

Segmentation, where pixels are categorized by tissue types, is essential in medical image processing. This paper proposes a multi-level Fuzzy C-Means method to extract an intracranial from its background and skull. Then, a two-level Otsu multi-thresholding method is applied to segment the intracranial structure into cerebrospinal fluid, brain matters and other homogenous regions. Based on symmetrical properties in the intracranial structures, the left-half and right-half segmented intracranial regions are quantitatively compared with respect to the intracranial midline. The segmented regions are found to be very useful in providing information regarding normal and abnormal structures in the intracranial because any asymmetry that is detected would indicate a high probability of abnormalities. Additionally, pixel intensity information such as standard deviation and the maximum value of the pixels of the segmented regions are used to distinguish abnormalities such as bleeding and calcification from normal cases. This experimental work uses a medical image database consisting of 519 normal and 201 abnormal serial computed tomography (CT) brain images from 31 patients. The proposed multi-level segmentation approach proved to effectively isolate important homogenous regions in CT brain images. The extracted features of the regions would provide a strong basis for the application of content-based medical image retrieval (CMBIR).