Yasser M. Seddiq

A Proposed Scalable Design and Simulation of Wireless Sensor Network-Based Long-Distance Water Pipeline Leakage Monitoring System

Anomalies such as leakage and bursts in water pipelines have severe consequences for the environment and the economy. To ensure the reliability of water pipelines, they must be monitored effectively. Wireless Sensor Networks (WSNs) have emerged as an effective technology for monitoring critical infrastructure such as water, oil and gas pipelines. In this paper, we present a scalable design and simulation of a water pipeline leakage monitoring system using Radio Frequency IDentification (RFID) and WSN technology. The proposed design targets long-distance aboveground water pipelines that have special considerations for maintenance, energy consumption and cost. The design is based on deploying a group of mobile wireless sensor nodes inside the pipeline and allowing them to work cooperatively according to a prescheduled order. Under this mechanism, only one node is active at a time, while the other nodes are sleeping. The node whose turn is next wakes up according to one of three wakeup techniques: location-based, time-based and interrupt-driven. In this paper, mathematical models are derived for each technique to estimate the corresponding energy consumption and memory size requirements. The proposed equations are analyzed and the results are validated using simulation.

FPGA-based implementation of a CFAR processor using Batcher's sort and LUT arithmetic

This paper presents the realization of the forward automatic censored cell averaging detector (F-ACCAD), a novel CFAR algorithm for detecting targets in log-normal distribution clutter recently published in [1]. The algorithm is realized through an FPGA-based parallel architecture. The timing constraints of high resolution radar applications are considered and satisfied in the system. The sequential nature of the algorithm has been parallelized to achieve the desired processing delay. The intensive statistical calculations and the complexity of the algorithm have been significantly reduced by using lookup tables (LUTs). Batchers sort, a parallel sorting algorithm, is adopted in this work. The hardware synthesis results and timing analysis are reported at the end.

Evaluation of Energy-Efficient Cooperative Scheme for Wireless Sensor Nodes Used in Long Distance Water Pipeline Monitoring Systems

Due to the significant demand of energy efficient wireless sensor network (WSN) for different industrial applications, it is important to research and develop robust energy efficient schemes for WSN nodes. One such application is monitoring leakage, bursts and other anomalies in long distance water pipeline systems. In this paper, we propose an energy-efficient cooperative scheme for a group of mobile wireless sensor nodes deployed inside the pipeline. The nodes are supposed to run cooperatively in order to save their resources. It is assumed that only one node shall remain active for a specific period of time while all other nodes are in sleep mode. As soon as the active node completes its cycle, it goes to sleep while another node is triggered by its timer to wake up and continue the process. The proposed scheme is evaluated for energy consumption by respective nodes with the help of a mathematical model.

An implementation of a 2D FIR filter using the signed-digit number system

An FIR filter is implemented in this work. Enhancing the arithmetic operations of the filter is considered. For the addition operation, the signed-digit number system is utilized. For the multiplication operation, Booth-3 algorithm is used to reduce the number of partial products. Then a 1D filter is used to construct a 2D filter that is deployed on real hardware in an image processing application.

Revisiting distinctive phonetic features from applied computing perspective: unifying views and analyzing modern Arabic speech varieties

Distinctive phonetic features (DPFs) provide the description of phonemes’ places and manners of articulation. Several, sometimes contradictory, views and definitions of the DPF elements of Modern Standard Arabic have been proposed in the phonology literature. This contrast in views is a significant barrier against utilizing the advantages of DPFs in digital speech processing applications because computer systems do not deliver correct results under vague rules and models. This is a review paper that presents background on Arabic DPFs and in addition to highlighting the historical and geographical verities. It also addresses the problem of ambiguous definitions between classical and modern phonology that may introduce significant challenge to computer scientists and engineers when developing computer systems. Another contribution of this work is to investigate the deviations in phonemes and DPF elements across dialects of Arabic. This is important to provide engineers with better understanding when designing computer software targeting a wide spectrum of Arabic speaking users.

A new look at the automatic mapping between Arabic distinctive phonetic features and acoustic cues

In this paper, the multidimensional phonological feature structure of Arabic is investigated. Our goal is to assess the performance of statistical and connectionist approaches in performing the complex mappings between distinctive phonetic features (DPF) and associated acoustic cues. The present study explores the mapping between 29 phonological voicing, place, and manner features and Mel-frequency acoustic cues. For this purpose, three machine-learning techniques are deployed: Deep Belief Networks (DBN), Multilayer Perceptron (MLP), and Hidden Markov Models (HMM). The three techniques show satisfactory acoustic-phonetic mapping performance and indicate that couple of Arabic DPF elements such as affricatives, alveopalatals, labiodentals, lateral, palatal, pharyngeal, rounded, and uvular have a strong correlation with the acoustic information. The implications of these results on Arabic phonological contrasts are discussed.

Building a Rich Arabic Speech and Language Corpus Based on the Holy Quran

This paper pursues the goal of creating a reliable speech corpus based on The Holy Quran (THQ) audio recordings. Achieving that goal involves major steps to be done and essential requirements to be considered. With the availability of tremendous amount of recordings nowadays, it is of a fundamental importance to select the ones that feature both high audio quality and perfect reciter performance. Also, since the targeted beneficiaries from the corpus are the digital speech processing research community, it is also very essential to maintain an efficient, a familiar and a convenient way of presenting the audio corpus and other language material, such as the language model. Audio recordings of THQ are selected from four sources having a high standard regarding the reciters’ performance. A significant effort is made in phonetical transcription of the audio content such that the written transcript maps perfectly to the uttered phonemes. Furthermore, the corpus dictionary, which is usually required in many fields such as machine learning and datamining, is also created. The first release of the corpus consists of recorded recitations and the necessary metadata of three chapters of THQ of different lengths recited by four reference reciters. Those chapters are selected for this phase based on statistical analysis of the lengths of all chapters and the frequency of occurrence of the Arabic phonemes across all chapters of THQ.

Reintroducing KAPD as a Dataset for Machine Learning and Data Mining Applications

KACST Arabic Phonetic Database (KAPD) has been in use by researchers for around fifteen years since its initial release. Researches in acoustics and phonetics have benefited from its phonetically rich content. In fact, KAPD has the potential to go further steps with the research community. In this work, KAPD is subject to enhancements and improvements in order to serve as dataset for machine learning and data mining application. This work involves refining and reviewing the already existing metadata of KAPD and adding new material that are necessary for machine learning and data mining applications. The updated phoneme statistics after the corpus upgrade are presented from different perspectives. Data format and time units are made compatible with those of HTK. The paper discusses the potential of KAPD to serve as either a balanced or an imbalanced dataset.

PHEWnA: An expert system framework for children health care and awareness

This paper presents the work of developing a framework for expert systems of health self-assessment and education targeting children. The framework is called the Personal Health Early Warning and Awareness (PHEWnA). The concept of operation of PHEWnA is that it acts as a common ground for interaction between three parties: software developers, physicians and users. A set of health-related expert systems can be developed and run under that framework. An expert system under that framework, which is a diabetes self-assessment system, is reported. The set of rules of this system is presented. The diabetes self-assessment system is introduced in the forms of web and mobile applications.

Frame distance array algorithm parameter tune-up for TIMIT corpus automatic speech segmentation

This work is related to unsupervised automatic speech segmentation. An experiment was carried out on the Frame Distance Array (FDA) algorithm with a main goal of the algorithm parameter tune-up. The experiment was carried out by applying the algorithm on TIMIT corpus and by using MFCC as the speech signal features. The parameters tuned up in this work are the frame length, the frame increment, the number of test frames and the test frame step size. The best combination of values was chosen based on the observations on the detection rate, the miss rate and the false boundary rate. The best parameter tune-up found at 23 ms, 1.5 ms, 9 frames and 2 frames for the frame length, the frame increment, the number of test frames and the test frame step size respectively.