Size Control and Magnetic Property Trends in Cobalt Ferrite Nanoparticles Synthesized Using an Aqueous Chemical Route

Cobalt ferrite (CoFe2O4) is an engineering material which is used for applications such as magnetic cores, magnetic switches, hyperthermia based tumor treatment, and as contrast agents for magnetic resonance imaging. Utility of ferrites nanoparticles hinges on its size, dispersibility in solutions, and synthetic control over its coercivity. In this work, we establish correlations between room temperature co-precipitation conditions, and these crucial materials parameters. Furthermore, post-synthesis annealing conditions are correlated with morphology, changes in crystal structure and magnetic properties. We disclose the synthesis and process conditions helpful in obtaining easily sinterable CoFe2O4 nanoparticles with coercive magnetic flux density (H-c) in the range 5.5-31.9 kA/m and M-s in the range 47.9-84.9 A.m(2)Kg(-1). At a grain size of similar to 54 +/- 2 nm (corresponding to 1073 K sintering temperature), multi-domain behavior sets in, which is indicated by a decrease in H-c. In addition, we observe an increase in lattice constant with respect to grain size, which is the inverse of what is expected of in ferrites. Our results suggest that oxygen deficiency plays a crucial role in explaining this inverse trend. We expect the method disclosed here to be a viable and scalable alternative to thermal decomposition based CoFe2O4 synthesis. The magnetic trends reported will aid in the optimization of functional CoFe2O4 nanoparticle

A Review on Deep Learning Techniques for IoT Data

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments

A Review on Deep Learning Techniques for IoT Data

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments