Lamiaa A. Elrefaei

Joint physical and link layer error control analysis for nanonetworks in the Terahertz band

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. The limited capabilities of individual nanomachines and the Terahertz (THz) band channel behavior lead to error-prone wireless links. In this paper, a cross-layer analysis of error-control strategies for nanonetworks in the THz band is presented. A mathematical framework is developed and used to analyze the tradeoffs between Bit Error Rate, Packet Error Rate, energy consumption and latency, for five different error-control strategies, namely, Automatic Repeat reQuest (ARQ), Forward Error Correction (FEC), two types of Error Prevention Codes (EPC) and a hybrid EPC. The cross-layer effects between the physical and the link layers as well as the impact of the nanomachine capabilities in both layers are taken into account. At the physical layer, nanomachines are considered to communicate by following a time-spread on-off keying modulation based on the transmission of femtosecond-long pulses. At the link layer, nanomachines are considered to access the channel in an uncoordinated fashion, by leveraging the possibility to interleave pulse-based transmissions from different nodes. Throughout the analysis, accurate path loss, noise and multi-user interference models, validated by means of electromagnetic simulation, are utilized. In addition, the energy consumption and latency introduced by a hardware implementation of each error control technique, as well as, the additional constraints imposed by the use of energy-harvesting mechanisms to power the nanomachines, are taken into account. The results show that, despite their simplicity, EPCs outperform traditional ARQ and FEC schemes, in terms of error correcting capabilities, which results in further energy savings and reduced latency.

Dynamic User Verification Using Touch Keystroke Based on Medians Vector Proximity

In this paper a user verification system on mobile phones is proposed. This system is based on behavioral biometric traits which is a keystroke dynamics derived from a touchable keyboard. A mobile application is developed for collecting those touch keystroke dynamics. In contrast to other systems, no specific text or numbers are used to build our dataset. The Median Vector Proximity classifier is applied on the touch keystroke data (touchable keyboard) and the performance of the system is investigated using different number of features and we found that the system with 31 features gained an average EER=12.9%. While with an extra two features (average of finger size and pressure) the average EER=12.2%. This shows that the more features used results in more accurate systems. The proposed system is compared against other systems and shows promising results in dynamic authentication area.

Saudi riyal coin detection and recognition

Coin detection and recognition applications play an important role in computer vision and in industry. Many applications have been developed to detect coins and estimate its corresponding values either by camera picture or mobile devices. This paper proposes a system that uses number of computer vision techniques to detect and recognize coins applied to Saudi riyal currency and returns its estimated values. The main goal goes around differentiating between different division of the same currency.

Distributed Timely Throughput Optimal Scheduling for the Internet of Nano-Things

Nanotechnology is enabling the development of miniature devices able to perform simple tasks at the nanoscale. The interconnection of such nano-devices with traditional wireless networks and ultimately the Internet enables a new networking paradigm known as the Internet of Nano-Things (IoNT). Despite their promising applications, nano-devices have constrained power, energy, and computation capabilities along with very limited memory on board, which may only be able to hold one packet at once and, thus, requires packets to be delivered before certain hard deadlines. Toward this goal, a fully-distributed computation-light provably-correct scheduling/MAC protocol is introduced for bufferless nano-devices, which can maximize the network throughput, while achieving perpetual operation. More specifically, the proposed scheduling algorithm allows every nano-device to make optimal transmission decisions locally based on its incoming traffic rate, virtual debts, and channel sensing results. It is proven that the proposed algorithm is timely throughput optimal in the sense that it can guarantee reliable data delivery before deadlines as long as the incoming traffic rates are within the derived maximum network capacity region. This feature not only can lead to high network throughput for the IoNT, but also guarantees that the memory of each device is empty before the next packet arrives, thus addressing the fundamental challenge imposed by the extremely limited memory of nano-devices. In addition, the optimal deadline is derived, which guarantees that all the nano-devices can achieve perpetual communications by jointly considering the energy consumption of communications over the terahertz channel and energy harvesting based on piezoelectric nano-generators.

Automatic electricity meter reading based on image processing

This paper introduces a system based on image processing to obtain efficiently and accurately reading of the electricity digital meter. In this system the back camera of the mobile phones is used to acquire the image of the electricity meter. The system then applies a sequence of image processing functions to automatically extract and recognize the digits of the meter reading image. This image goes through three main stages: preprocessing which ends up with cropping the numeric reading area, segmentation of individual digits using horizontal and vertical scanning of the cropped numeric area, and recognition of the reading by comparing each segmented digit with the digits templates. The proposed system is implemented using Android Studio software with openCV library and has been tested on 21 images of electric meters captured by Smartphone camera in Saudi Arabia, and results shows a recognition with the accuracy rate of 96,49% (per number digit) and 85.71% accuracy rate for the electricity meter readings. The proposed system will be used in the future to develop a mobile application that could be used by the electricity company employees to facilitate the reading process.

Automated Fingerprint Identification System based on weighted feature points matching algorithm

Most of fingerprint identification systems perform matching algorithms based on different minutiae details present in the fingerprint. Usually, minutiae are extracted from the thinned fingerprint image. Due to the image noise and different preprocessing methods, the thinned image could results in a large number of false minutiae which may decrease the performance of the system. In this paper, some existing algorithms from different studies were integrated to build Automated Fingerprint Identification System. A new matching algorithm was proposed based on feature with two points; minutiae and ridge point. In addition, each feature extracted assigned to an appropriate weight according to proposed weights table. The modified system was tested on FVC2002 DB1 set-A and all four FVC2004 set-A databases and showed that it is effective and gives excellent results that exceed the performance of classic minutiae-based matching algorithm.

Developing Iris Recognition System for Smartphone Security

ABSTRACTSmartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.

Automatic Segmentation of Chromosome Cells

Chromosome’s segmentation is an essential step in the automated chromosome classification system. It is important for chromosomes to be separated from noise or background before the identification and classification. Chromosomes image (Metaphase) is generated in the third phase of mitosis. During metaphase, the cell’s chromosomes arrange themselves in the middle of the cell through a cellular. The analysis of metaphase chromosomes is one of the essential tools of cancer studies and cytogenetics. The Chromosomes are thickened and highly twisted in metaphase which make them very appropriate for visual analysis to determine the kind of each chromosome within the 24 classes (Chromosome karyotyping). This paper represents a chromosome segmentation method of high-resolution digitized metaphase images. Segmentation is done using Difference of Gaussian (DoG) as a sharpening filter before the classic technique (Otsu’s thresholding followed by morphological operations). The proposed method is tested using 130 metaphase images (6011 chromosomes) provided by The Diagnostic Genomic Medicine Unit (DGMU) laboratory at King Abdulaziz University. The experimental results show that the proposed method can successfully segment the metaphase chromosome images with 99.8% segmentation accuracy.