Line matching of wide baseline images in an affine projection space

ABSTRACT

Line matching plays an important role in vision localization and three-dimensional reconstruction of building structures. The conventional method of line matching is not effective for processing stereo images with wide baselines and large viewing angles. This paper proposes a line matching method in an affine projection space, aiming to solve the problem of change of viewing angles in aerial oblique images. Firstly, monocular image orientation can be performed through geometric structures of buildings. Secondly, according to the pose information of the camera, the affine projection matrix is obtained. The original image can be rectified as a conformal image based on this projection matrix, thereby reducing the difference in the viewing angle between images. Then, line matching is performed on the rectified images to get the matched line pairs. Finally, the inverse affine projection matrix is used to back-project the matched line pairs to the original images. The experimental results of five groups of aerial oblique images show that the matched line segments obtained by the proposed method are basically superior to those of the methods which are directly processed on the original image in terms of quantity, correctness, and efficiency.     

Interactive multilevel focus+context visualization framework

In this article, we present the construction of an interactive multilevel focus+context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution technique supported by a balanced wavelet transform (BWT). It extends the mode of focus+context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. We describe the underlying data structure efficiently support such operations. Changes in the spatial locations of query windows defining the ROIs trigger on-demand reconstruction queries. We explain in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization of complex large-scale imagery. We also discuss the performance characteristics of our proposed framework from various aspects, such as time and space complexities and achieved frame rates.     

A portable wireless body sensor data logger and its application in video capsule endoscopy

Due to the advancement of low power miniature electronic devices, there is growing interest of physiological data sensing and recording using wireless body sensor networks. The paper presents the design of a portable physiological data logger that includes rechargeable battery, wireless radio frequency and Bluetooth connectivity, and graphical display with touch screen capability. The target application is video capsule endoscopy. Image data are logged in micro SD cards which can be easily transferred to PC or Smartphone using SD card reader, USB interface or Bluetooth wireless link. The hardware design is general and can be used in various medical or industrial applications by changing only the firmware of the microcontroller. The design is prototyped in 109 × 107 × 20 mm printed circuit board (PCB). Tests with animal tissues have been conducted to demonstrate the performance advantages of the data logger. A demonstration of wireless heart pulse monitoring and data logging is also presented.     

Process simulation for HgCdTe infrared focal plane array flexible manufacturing

The strategy and status of a process simulator for the flexible manufacture of HgCdTe infrared focal plane arrays is described. It has capabilities to simulate Hg vacancy and interstitial effects and cation impurity diffusion, for various boundary conditions in one dimension. Numerical complexity of these problems stems from the necessity of solving diffusion equations for each defect that are coupled to each other via nonlinear interaction terms. The simulator has already led to the prediction of heretofore unexplained experimental data. Current extensions of the one-dimensional simulator planned over the next few years include the addition of Te antisites, antisite-Hg vacancy pairs, and In-Hg vacancy pairs, ion implantation, and various energetic processes (such as ion milling). The sequential effect of various processes will be possible with the input to the simulator looking much like a process run sheet.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

On-chip high voltage charge pump in standard low voltage CMOS process

An on-chip high voltage tolerant 4VDD charge pump with symmetrical architecture in a standard low voltage 1.8 V 0.18 /spl mu/m CMOS process is presented. For a 250 k/spl Omega/ load, circuit efficiency of the charge pump is approximately 71%. All the MOS transistors satisfy typical voltage stress related reliability requirements for standard low voltage CMOS devices.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Modified distributed snapshots algorithm for protocol stabilization

This paper assesses the use of Chandy and Lamport's distributed snapshots algorithm (DSA) for stabilizing a communication protocol, a special type of distributed system. We show that when a loss of coordination occurs during the distributed execution of the protocol, DSA is not guaranteed to terminate, and therefore it sometimes fails to obtain a global state or snapshot. We propose some modifications to DSA to solve this problem. Finally, we discuss how, in the case of a loss of coordination, the modified algorithm can be used to stabilize a communication protocol, and we assess the suitability of the global state obtained by DSA as a recovery point to be used later in a backward recovery procedure.     

Scheme for reducing size of coefficient memory in FFT processor

Long fast Fourier transforms (FFTs) are required in applications such as orthogonal frequency division multiplexing, radars and sonars. It is highly desirable to reduce the size and power requirements of the FFT so as to realise single chip long FFT-based systems targeting portable applications. Presented here is a novel technique to reduce the coefficient memory almost by a factor of four by exploiting the relationships among the coefficient values thereby significantly reducing the area and power requirements of the hardware     

A new honey bee mating optimization algorithm for non-smooth economic dispatch

The non-storage characteristics of electricity and the increasing fuel costs worldwide call for the need to operate the systems more economically. Economic dispatch (ED) is one of the most important optimization problems in power systems. ED has the objective of dividing the power demand among the online generators economically while satisfying various constraints. The importance of economic dispatch is to get maximum usable power using minimum resources. To solve the static ED problem, honey bee mating algorithm (HBMO) can be used. The basic disadvantage of the original HBMO algorithm is the fact that it may miss the optimum and provide a near optimum solution in a limited runtime period. In order to avoid this shortcoming, we propose a new method that improves the mating process of HBMO and also, combines the improved HBMO with a Chaotic Local Search (CLS) called Chaotic Improved Honey Bee Mating Optimization (CIHBMO). The proposed algorithm is used to solve ED problems taking into account the nonlinear generator characteristics such as prohibited operation zones, multi-fuel and valve-point loading effects. The CIHBMO algorithm is tested on three test systems and compared with other methods in the literature. Results have shown that the proposed method is efficient and fast for ED problems with non-smooth and non-continuous fuel cost functions. Moreover, the optimal power dispatch obtained by the algorithm is superior to previous reported results.