·         Table of Contents (Volume #3, Issue #3)

·         Cover Page (Volume #3, Issue #3)

·         Editorial (Volume #3, Issue #3)

Paper #1

Towards a Quantum Field Theory for Optical Artificial Intelligence

Antonio Manzalini

Abstract: Today, several socio-techno-economic drivers are steering the evolution of Telecommunications and Internet towards a growing exploitation of ultra-broadband infrastructures (e.g., 5G) and Artificial Intelligence (AI) systems. Focusing on the most promising AI technological approaches, Deep Neural Networks (DNNs) are outperforming in several applications domains. One of the possible explanations, elaborated in literature, is that DNN functioning is deeply rooted in the principles of theoretical Physics, specifically Quantum Field Theory (QFT) and Gauge theory. This is encouraging even more researches and experiments in the direction of a full exploitation of quantum computing and networking for the development of innovative Information Communication Technologies (ICT) and AI systems. In this innovation avenue, given that QFT and Gauge theory have been already proposed for modeling the brain and biological nervous systems, this paper explores the intriguing possibility of exploiting QFT principles also for future DNN, for instance by using electromagnetic waves effects in metamaterials. This appears to be a promising direction of future studies and experiments: therefore, the paper also describes the architecture of a simple optical DNN prototype, based on metamaterials, which is intended as a live test-bed, for simulations and experiments.

Keywords: Artificial Intelligence; Deep Learning; Deep Neural Networks; Gauge Theory; Quantum Field Theory.

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Paper #2

A Wearable Wireless Sensor Network Node for Prevention of Physical Injuries

Tobias Heiduk, Aye Min Thike, Peter S. Excell and Ardeshir Osanlou

Abstract: The economic burden of healthcare provision is continually rising. To combat this, targeted preventative measures have been proposed, together with encouragement of healthier lifestyle, enhanced health data collection and empowerment of patients in disease prevention and monitoring. A major cause of impairment is muscular strain and/or bone damage caused by sports injuries in the young, or falling in the elderly. To address such issues, a wearable wireless inertial sensor network is proposed which monitors body dynamics for probable disruptive incidents. The sensor node consists of a combined accelerometer, gyroscope and magnetometer, linked to a micro controller to gather information from the sensor and a wireless transceiver for communication with the network. A miniaturised wearable prototype was designed and realised in hardware.

Keywords: Body Area Network (BAN); IEEE 802.15.4; motion monitoring; prevention of physical injuries; MEMS; microcontroller; wearable devices; wireless sensor network.

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Paper #3

The Cascade Carry Array Multiplier – A Novel Structure of Digital Unsigned Multipliers for Low-Power Consumption and Ultra-Fast Applications

Mohsen Sadeghi, Mahya Zahedi and Maaruf Ali

Abstract: This article presents a low power consumption, high speed multiplier, based on a lowest transistor count novel structure when compared with other traditional multipliers. The proposed structure utilizes 4×4-bit adder units, since it is the base structure of digital multipliers. The main merits of this multiplier design are that: it has the least adder unit count; ultra-low power consumption and the fastest propagation delay in comparison with other gate implementations. The figures demonstrate that the proposed structure consumes 32% less power than using the bypassing Ripple Carry Array (RCA) implementation. Moreover, its propagation delay and adder units count are respectively about 31% and 8.5% lower than the implementation using the bypassing RCA multiplier. All of these simulations were carried out using the HSPICE circuit simulation software in 0.18 µm technology at 1.8 V supply voltage. The proposed design is thus highly suitable in low power drain and high-speed arithmetic electronic circuit applications.

Keywords: Cascade Carry Array Multiplier; Propagation Delay; Ripple Carry Array Multiplier (RCA); Braun Multiplier; Bypassing RCA; Bypassing CSA; Carry Save Array; Ripple Carry Array.

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Paper #4

Survey on Internet of Things (IoT) for Different Industry Environments

Usman Ahmad, Junaid Chaudhary, Mudassar Ahmad and Amjad Ali Naz

Abstract: Internet of Things (IoT) provides an opportunity to build powerful applications and computing systems by using wireless communication and radio frequency identification (RFID), mobile, wired and wireless sensor device. In recent years several IoT applications have been developed for industrial use. To comprehend the IoT development, this survey paper provides a precise review of current research on IoT technologies. This study provides IoT applications regarding industries and categorizes the research challenges, issues, and developments. This survey contributes in providing the current state-of-the-art information regarding industrial IoT.

Keywords: Internet of Things; IoT; Radio-frequency identification (RFID); WSN; Industry.

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Paper #5

A Diabetic Disease Prediction Model Based on Classification Algorithms

Ravinder Ahuja, Subhash C. Sharma and Maaruf Ali

Abstract: Diabetes is one of the chronic diseases in the world, 246 million people are inflicted by this disease and according to a World Health Organisation (WHO) report, this figure will increase to 380 million sufferers by 2025. Many other debilitating and critical health issues may further develop if this disease is not diagnosed or remain unidentified. Machine Learning (ML) techniques are now being used in various fields like education, healthcare, business, recommendation system, etc. Healthcare data is complex and high in dimensionality and contains irrelevant information - due to this, the prediction accuracy is low. The Pima Indians Diabetes Dataset was used in this research, it consisted of 768 records. Firstly, the missing values are replaced by the median followed by Linear Discriminant Analysis. Using the Python programming language, feature selection techniques is applied in combination with five classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Logistic Regression, Random Forest and Decision Tree. The aim of this paper is to compare the different classification algorithms in order to predict diabetes in patients more accurately. K-fold cross-validation is applied, considering k to be 2, 4, 5 and 10. The performance parameters taken are the: accuracy, precision, recall, F Score and area under the curve. Our study found that the MLP classifier gave the highest accuracy of 78.7% with a recall of 61.26%, precision of 72.45% and F1 Score of 65.97% for k = 4.

Keywords: Classification Algorithms; Diabetes Prediction; Prediction; Feature Selection; Machine Learning; Neural Networks; Multi-layer Perceptron; MLP.

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