Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.

Fiber orientation measurement from mesoscale CT scans of prepreg platelet molded composites Part A Applied science and manufacturing

X-ray computed tomography (CT) analysis is used to measure the heterogeneous fiber orientation fields in a 20 cm 3 composite bracket made from prepreg platelet-based molding compound (PPMC). The as-molded mesostructure of the complete geometry is captured using material density gradients in 50 µm resolution CT scans without the need to resolve individual fibers. Fiber collimation and physical density gradients within intact platelets of this material system facilitates nondestructive assessment of average local fiber orientation at the component scale. Microscopy-based validation of the local orientation measurements indicate the accuracy that can be attained utilizing the density-based structure tensor CT analysis method. Local orientation field measurements for the complete geometry can be mapped into a “digital twin” model, for purposes such as experimental performance simulation and validation of molding process orientation state predictions. Composite designers, analysts, and material suppliers can employ this methodology to more confidently utilize PPMCs and morphologically similar composite material systems.

Fiber orientation measurement from mesoscale CT scans of prepreg platelet molded composites

Nowadays the number of vehicle users increasing day by day, so the vehicle manufacture trying to develop higher end vehicle that reduce the complexity during driving. Advance Driver Assists Sytsem is one of such type that provide alert, warning and information during driving. In our proposed method Gaussian filtering, median filtering and connected component analysis are used to detect speed bump. This system go well with the roads that are constructed with proper painting. Several existing method need special hardware, sensors, accelerometer and GPS for detecting speed bump.

https://file.scirp.org/pdf/CS_2016071214552295.pdf

Real time speed bump detection using Gaussian filtering and connected component approach

Developing a workflow to select representative reservoir models combining distance-based clustering and data assimilation for decision making process

Noised image is a major problem of digital image systems when images transferred between most electronic communications devices. Noise occurs in digital images due to transmit the image through the internet or maybe due to error generated by noisy sensors. Other types of errors are related to the communication system itself, since image needed to be transferred from analogue to digital and vice versa, also to be transmitted in most of the communication systems. Impulse noise added to image signal due to process of converting image signal or error from communication channel. The noise added to the original image by changes the intensity of some pixels while other remain unchanged. Salt-and-pepper noise is one of the impulse noises, to remove it a simplest way used by windowing the noisy image with a conventional median filter. Median filters are the most popular nonlinear filters extensively applied to eliminate saltand-pepper noise. This paper evaluates the performance of median filter based on the effective median per window by using different window sizes and cascaded median filters. The performance of the proposed idea has been evaluated in MATLAB simulations on a gray and RGB images. The experimental results show that median filter has a good performance in gray and RGB images in low noise densities and also in high noise densities when using cascaded median filter and high level of window sizes, but with higher window size a degree of blurring effect will be added to filtered noise.

http://www.sersc.org/journals/IJSIP/vol8_no10/34.pdf

Median Filter Performance Based on Different Window Sizes for Salt and Pepper Noise Removal in Gray and RGB Images

Cloud computing is one of the most important communication model nowadays since it’s provides a sets of resources and multiple types of services offered through the internet. The services and resources provided by cloud computing are cheaper because of no maintenance cost required in the core of clouds, since all services were offered to the clients based on services availability by providers only and clients are free to manage and maintains the resources machines. People use the cloud computing only when they need it, for this reasons cloud computing may be called a services over internet on demand. Companies also use the clouds to reduce their operation costs by resting virtual machines for digital services from cloud providers. With the growth of data every day which require a more services and resources in cloud computing, a security issues are creates a new demands and opportunities for security models that because cloud computing facing many types of attack threats with increasing of clouds . Network Denial of services is one of the most famous attack threats that make sense in a cloud computing context and may be divided into network distributed denial of services and DNS denial of services knows as availability threats. This paper reviews the types of network denial of services attacks also classify the methods of security defences and then compare between all of them.

Rasha Eltayeb Abd Elatif

Papers

Median Filter Performance Based on Different Window Sizes for Salt and Pepper Noise Removal in Gray and RGB Images

Network Denial of Service Threat Security on Cloud Computing A Survey