iM-SIMPLE: iMproved stable increased-throughput multi-hop link efficient routing protocol for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are envisaged to play crucial role in psychological, medical and non-medical applications. This paper presents iM-SIMPLE; a reliable, and power efficient routing protocol with high throughput for WBAN. We deploy sensor nodes on human body to measure the physiological parameters such as blood pressure, temperature, glucose, lactic acid, EMG, acceleration, pressure, and position. Data from sensors is forwarded to intermediate node, from where it is transmitted to sink. An end user can access the required information available at sink via internet. To minimize energy consumption of the network, we utilize multi-hop mode of communication. A cost function is introduced to select the forwarder; node with high residual energy and least distance to sink has minimum cost function value and is selected. Residual energy parameter balances the energy consumption among the sensor nodes, and least distance improves packet delivery to sink because of reduced less path loss. We formulate the minimum energy consumption and high throughput problems as an Integer Linear Program. In order to support mobility, we also consider two body postures. Simulation results confirm the performance advantage of iM-SIMPLE compared to contemporary schemes in terms of maximizing stability period and throughput of the network.     

An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Digitally compensated beam current transformer

The Spallation Neutron Source (SNS) is being built by a collaboration of six laboratories. Beam current monitors (BCMs) will be used to record the current of H-minus and H-plus beams ranging from 15 mA (tune-up in the Front End and Linac) to over 60A fully accumulated in the Ring and dumped to the load as a single pulse in the Ring to Beam Target (RTBT). The time structure of these beams ranges from 645 ns “mini” bunches at the 1.05 MHz ring revolution rate, to an overall 1 ms long macro-pulse. The requirements for the BCMs will depend upon their location within the system. The need to measure individual mini-pulses, examine the characteristics of the chopper edge, as well as the longer average current pulse of the macropulse, or long duration pulses during Linac tuning place wide requirements upon the response of current transformers. To obtain the desired accuracy and resolution, current transformers must have <1 ns rise time and droops of 0.1%/ms. This places a significant design burden on the current transformer; such a design is almost impossible to achieve. Extremely large expensive cores are needed to meet the low droop, while leakage inductance increases with size, thereby reducing the achievable rise time. In this paper, I discuss a digital compensation approach [M. Kesselman, Spallation neutron source beam current monitor electronics, PAC2001 June 18–22, 2001, Chicago, IL.] that extends the lower cut-off frequency of the current transformer, optimized for high frequency response, so that it can be used in this application with improvements in droop of the order of 1000:1. Transformer saturation (current-time product) is a separate issue and the transformer must be designed to handle the current-time product of the signal to assure it does not saturate.     

A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation

Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.     

Small-world brain functional network altered by watching 2D/3DTV

With the development of display technology, the healthy problems caused by watching 2D/3DTV have received more and more attention. This paper utilized resting-state functional magnetic resonance imaging to study the changes of small-world brain network before and after one-hour 2D/3DTV watching, and explored the brain fatigue mechanism caused by watching 2D/3DTV. We conclude that one-hour watching of 2DTV will not increase the burden of brain. On the contrary, one-hour watching of 3DTV requires the brain to regulate the efficiency of brain areas, such as temporal lobe and occipital lobe, which may explain the fact that watching 3DTV can easier cause brain fatigue than watching 2DTV.     

The influence of compression molding techniques on thermal conductivity of UHMWPE/BN and UHMWPE/(BN + MWCNT) hybrid composites with segregated structure

Various ultra-high-molecular-weight polyethylene (UHMWPE)/boron nitride (BN) and UHMWPE/(BN + multi-wall carbon nanotube (MWCNT)) composites with segregated structure were prepared by using the compression molding process. The dispersion of fillers under different compression molding were observed by optical microscopy and scanning electron microscopy. The results showed that integrated thermal conductive networks were formed after cold-pressing sintering. However, these networks would be destroyed by middle-high pressure/high temperature treatment. Although the treatment of high pressure/high temperature can effectively improve the crystallinity and crystal size of UHMWPE, the thermal conductivity of composite dramatically decreased due to the replacement of filler-filler by filler-polymer-filler interface. The 1D-MWCNT is liable to entangle with 2D-BNs and formed MWCNT-BN networks even at high pressure/high temperature, leading to a nearly constant thermal conductivity (reached 1.794 W/m·K with the addition of 50% (BNs + MWCNT) hybrid fillers). Besides, the dispersion of the fillers have a great influence on thermal stability of the composites.     

Feature extraction and classification for detecting the thermal faults in electrical installations

This paper proposed an effort to investigate the suitability of input features and classifier for identifying thermal faults within electrical installations. The features are extracted from the thermal images of electrical equipment and classified using a multilayered perceptron (MLP) artificial neural network and support vector machine (SVM). In the experiments, the classification performances from various input features are evaluated. The commonly used classification performance indices, including sensitivity, specificity, accuracy, area under curve (AUC), receiver operating characteristic (ROC) and F-score are employed to identify the most suitable input feature as well as the best configuration of classifiers. The experimental results demonstrate that the combination of features set T_max, T_delta and DT_bg produce the best input feature for thermal fault detection. In addition, the implementation of SVM using radial basis kernel function (RBF) produces slightly better performance than the MLP artificial neural network.     

Fast total variation deconvolution for blurred image contaminated by Poisson noise

In this paper, we present a fast non-blind deconvolution method for restoring blurred images contaminated by Poisson noise. The problem is formulated by finding the minimizer of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). To attack the challenging task, we adopt the well-known variable splitting and penalty technique to convert the problem into two easier sub-problems: one is a modified TV regularized deconvolution and the other is a simple convex optimization problem. Experimental results show that the proposed method runs very fast and the quality of the restored image is comparable with that of some state of the art methods.