SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Magnetic field into multifunctional materials: magnetorheological, magnetostrictive and magnetocaloric

Society is facing serious challenges towards achieving highly efficient utilization of materials and devices. Magnetoactive lightweight materials, such as magnetorheological, magnetostrictive and magnetocaloric materials are attracting increasing interest once they allow a high number of applications such as energy generation, conversion, storage, sensing and actuation, as well as in the biomedical field. In this chapter, the latest research and development in multifunctional lightweight magnetorheological, magnetostrictive and magnetocaloric materials is summarized and discussed in the scope of different application areas. Furthermore, it will be also illustrated the unique functions of inorganic nanomaterials to improve performance of organic materials, as well as combination of the functions of nanomaterials into a device. Final remarks and future perspectives allow to look into a “magnetoactive crystal ball” aiming to foresee what will/should happen in this fascinating research field.The authors thank the FCT- Fundação para a Ciência e Tecnologia- for financial support in the framework of the Strategic Funding UID/FIS/04650/2019 and under project PTDC/BTM-MAT/28237/2017 and PTDC/EMD-EMD/28159/2017. P. Martins thanks FCT for the contract under the Stimulus of Scientific Employment, Individual Support – 2017 Call (CEECIND/03975/2017). The authors acknowledge funding by the Spanish Ministry of Economy and Competitiveness (MINECO) through the project MAT2016-76039-C4-3-R (AEI/FEDER, UE) and from the Basque Government Industry and Education Department under the ELKARTEK, HAZITEK and PIBA (PIBA-2018- 06) programs, respectively. Funding from the European Union’s Horizon 2020 Programme for Research, ICT-02-2018 - Grant agreement no. 824339 – WEARPLEX is also acknowledged

Porous pyroelectric ceramic with carbon nanotubes for high-performance thermal to electrical energy conversion

The recycling of low-grade thermal energy from our surroundings is an environmental-friendly approach to contribute to sustainability, which remains a grand challenge. Herein, a high-performance porous pyroelectric ceramic formed using carbon nanotubes (CNT) is designed and fabricated using a modified solid-state reaction technique. Localized characterization of PMN-PMS-PZT and PMN-PMS-PZT with 0.3 wt% CNT additions by piezoelectric force microscopy suggests that the presence of porosity and defects in grains can restrict the reversal of domains and weaken the local piezoresponse; that is due to the influence of porosity on the electric field, domain morphology, or screening effects induced by defects at the pore surface. More importantly, the porous ceramics showed enhanced figure of merits, including voltage responsibility and energy harvesting figure of merit, compared to the dense ceramic. The harvested energy increased by 208% when the 0.3 wt% of CNT was added to produce porosity, which has a potential application in thermal energy harvesting and sensing system.</p

National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

Application acceleration for wireless and mobile data networks

This work studies application acceleration for wireless and mobile data networks. The problem of accelerating application can be addressed along multiple dimensions. The first dimension is advanced network protocol design, i.e., optimizing underlying network protocols, particulary transport layer protocol and link layer protocol. Despite advanced network protocol design, in this work we observe that certain application behaviors can fundamentally limit the performance achievable when operating over wireless and mobile data networks. The performance difference is caused by the complex application behaviors of these non-FTP applications. Explicitly dealing with application behaviors can improve application performance for new environments. Along this overcoming application behavior dimension, we accelerate applications by studying specific types of applications including Client-server, Peer-to-peer and Location-based applications. In exploring along this dimension, we identify a set of application behaviors that significantly affect application performance. To accommodate these application behaviors, we firstly extract general design principles that can apply to any applications whenever possible. These design principles can also be integrated into new application designs. We also consider specific applications by applying these design principles and build prototypes to demonstrate the effectiveness of the solutions. In the context of application acceleration, even though all the challenges belong to the two aforementioned dimensions of advanced network protocol design and overcoming application behavior are addressed, application performance can still be limited by the underlying network capability, particularly physical bandwidth. In this work, we study the possibility of speeding up data delivery by eliminating traffic redundancy present in application traffics. Specifically, we first study the traffic redundancy along multiple dimensions using traces obtained from multiple real wireless network deployments. Based on the insights obtained from the analysis, we propose Wireless Memory (WM), a two-ended AP-client solution to effectively exploit traffic redundancy in wireless and mobile environments. Application acceleration can be achieved along two other dimensions: network provision ing and quality of service (QoS). Network provisioning allocates network resources such as physical bandwidth or wireless spectrum, while QoS provides different priority to different applications, users, or data flows. These two dimensions have their respective limitations in the context of application acceleration. In this work, we focus on the two dimensions of overcoming application behavior and Eliminating traffic redundancy to improve application performance. The contribution of this work is as follows. First, we study the problem of application acceleration for wireless and mobile data networks, and we characterize the dimensions along which to address the problem. Second, we identify that application behaviors can significantly affect application performance, and we propose a set of design principles to deal with the behaviors. We also build prototypes to conduct system research. Third, we consider traffic redundancy elimination and propose a wireless memory approach.Ph.D.Committee Chair: Sivakumar, Raghupathy; Committee Member: Ammar, Mostafa; Committee Member: Fekri, Faramarz; Committee Member: Ji, Chuanyi; Committee Member: Ramachandran, Umakishor

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users