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.

Emotional speech recognition for the Arabic language is insufficiently tackled in the literature compared to other languages. In this paper, we present the work of creating and verifying the King Saud University Emotions (KSUEmotions) corpus, which was released by the Linguistic Data Consortium (LDC) in 2017 as the first public Arabic emotional speech corpus. KSUEmotions contains an emotional speech of twenty-three speakers from Saudi Arabia, Syria, and Yemen, and includes the emotions: neutral, happiness, sadness, surprise, and anger. The corpus content is verified in two different ways: a human perceptual test by nine listeners who rate emotional performance in audio files, and automatic emotion recognition. Two automatic emotion recognition systems are experimented with: Residual Neural Network and Convolutional Neural Network. This work also experiments with emotion recognition for the English language using the Emotional Prosody Speech and Transcripts Corpus (EPST). The current experimental work is conducted in three tracks: (i) monolingual, where independent experiments for Arabic and English are carried out, (ii) multilingual, where the Arabic and English corpora are merged in as mixed corpus, and (iii) cross-lingual, where models are trained using one language and tested using the other. A challenge encountered in this work is that the two corpora do not contain the same emotions. That problem is tackled by mapping the emotions to the arousal-valance space.

Distinctive phonetic features have an important role in Arabic speech phoneme recognition. In a given language, distinctive phonetic features are extrapolated from acoustic features using different methods. However, exploiting lengthy acoustic features vector in the sake of phoneme recognition has a huge cost in terms of computational complexity, which in turn, affects real time applications. The aim of this work is to consider methods to reduce the size of features vector employed for distinctive phonetic feature and phoneme recognition. The objective is to select the relevant input features that contribute to the speech recognition process. This, in turn, will lead to a reduced computational complexity of recognition algorithm, and an improved recognition accuracy. In the proposed approach, genetic algorithm is used to perform optimal features selection. Therefore, a baseline model based on feedforward neural networks is first built. This model is used to benchmark the results of proposed features selection method with a method that employs all elements of a features vector. Experimental results, utilizing the King Abdulaziz City for Science and Technology Arabic Phonetic Database, show that the average genetic algorithm based phoneme overall recognition accuracy is maintained slightly higher than that of recognition method employing the full-fledge features vector. The genetic algorithm based distinctive phonetic features recognition method has achieved a 50% reduction in the dimension of the input vector while obtaining a recognition accuracy of 90%. Moreover, the results of the proposed method is validated using Wilcoxon signed rank test.

Distinctive phonetic features (DPFs) abstractedly describe the place, manner of articulation, and voicing of the language phonemes. While DPFs are powerful features of speech signals that capture the unique articulatory characteristics of each phoneme, the task of DPF extraction is challenged by the need for efficient computational model. Unlike the ordinary acoustic features that can be directly determined form speech waveform using closed-form expressions, DPF elements are extracted from acoustic features using machine learning (ML) techniques. Therefore, for the objective of developing an acoustic-to-phonetic converter of high accuracy and low complexity, it is important to select the input acoustic features that are simple, yet carry adequate information. This paper examines the effectiveness of using spectrogram as the acoustic feature with DPFs modeled using two deep learning techniques: the deep belief network (DBN) and the convolutional recurrent neural network (CRNN). The proposed method is applied on Modern Standard Arabic (MSA). Multi-label modeling is considered in the proposed acoustic-to-phonetic converter. The learning techniques were evaluated by proper evaluation measures that accommodate the imbalanced nature of DPF elements. The results showed that the CRNN is more accurate in extracting the DPFs than the DBN.

Feature extraction is a critical stage of digital speech processing systems. Quality of features are of great importance to provide solid foundation upon which the subsequent stages stand. Distinctive Phonetic Features (DPFs) are one of the most representative features of speech signals. The significance of DPFs is in their ability to provide abstract description of the places and manners of articulation of the language phonemes. A phoneme’s DPF element reflects unique articulatory information about that phoneme. Therefore, there is a need to discover and investigate each DPF element individually in order to achieve deeper understanding and to come up with a descriptive model for each one. Such fine-grained modeling will satisfy the uniqueness of each DPF element. In this paper, the problem of DPF modeling and extraction of Modern Standard Arabic is tackled. Due to the remarkable success of Deep Neural Networks (DNNs) that are initialized using Deep Belief Networks (DBNs) in serving DSP applications and its capability of extracting highly representative features from raw data, we exploit its modeling power to investigate and model DPF elements. DNN models are compared to the classical Multilayer Perceptron (MLP) models. The representativeness of several acoustic cues for different DPF elements was also measured. This work is based on formalizing DPF modeling problem as a binary classification problem. Because DPF elements are highly imbalanced data, evaluating the quality of models is a very tricky process. The paper addresses the proper evaluation measures satisfying the imbalanced nature of DPF elements. After modeling each element individually, two top-level DPF extractors are designed: MLP-based and DNN-based extractors. Results shows the quality of DNN models and their superiority over MLPs with accuracies of 89.0% and 86.7%, respectively.

In this paper, we presented a novel electrostatic Roll/Pitch MEMS gyroscope with in-plane drive mode and out-of-plane sense mode. The proposed structure is developed based on a tuning fork gyroscope with decoupled sense mass on each tine that control the sense out-of-plane frequency. A multi-height deep reactive ion etching (DRIE) fabrication process was utilized to achieve and enhance decoupling between the drive and sense modes. We presented our design methodology followed by an analytical and finite element (FEM) model. Our experimental results showed a good match between the analytical model and those obtained experimentally, from the drive and sense oscillation frequencies. Our characterization setup used a custom made application specific integrated circuit (ASIC) for characterization and was able to achieve ARW of 0.2 deg/rt-h, a bias instability 5.5 deg/h, and scale factor non-linearity (SFNL) 156 ppm FS.