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The purpose of this paper is to study thickness dependent variation in microwave properties of the Mg_xMn_(0.9−x)Al_0.1Zn_0.8Fe_1.2O₄ (x=0.8, 0.9) thick films and enhancement of power efficiency of Ag thick film EMC patch antenna.

X‐band microwave properties of the Mg_xMn_(0.9−x)Al_0.1Zn_0.8Fe_1.2O₄ (x=0.8, 0.9) thick films were measured by superstrate technique using Ag thick film EMC patch antenna as the resonant element. The complex permittivity and permeability of these thick films were also measured by this technique. The microwave response of the EMC patch, complex permeability and permittivity of Mg_0.8Mn_0.1Al_0.1Zn_0.8Fe_1.2O₄ and Mg_0.9Al_0.1Zn_0.8Fe_1.2O₄ thick films and their thickness dependency were investigated.

The XRD patterns reveal the cubic spinel crystal system was obtained for both compositions. The crystallite size obtained was 134.68 nm for Mg_0.8Mn_0.1Al_0.1Zn_0.8Fe_1.2O₄ and 155.99 nm for Mg_0.9Al_0.1Zn_0.8Fe_1.2O₄ The superstrate technique has been used successfully to evaluate the complex permittivity and permeability of the ferrite thick films in the X band. The EMC patch also show thickness and composition dependent frequency agility and enhancement of power efficiency.

The complex permeability of Mg_xMn_(0.9−x)Al_0.1Zn_0.8Fe_1.2O₄ (x=0.8, 0.9) thick films measured by superstrate technique is reported in this paper. The superstrate of Mg_xMn_(0.9−x)Al_0.1Zn_0.8Fe_1.2O₄ (x=0.8, 0.9) thick films makes the Ag thick film EMC patch antenna frequency agile and power amplification is obtained.

The proposed method is to generate the 3 D model of frame assemblies based on their topological model automatedly. It was a very demanding task and there was no appropriate automated method to facilitate this work.

The proposed method includes two stages. The first stage is decisive. In this stage, a deep learning network and the Chu–Liu–Edmonds algorithm are used to recognize contact relations among parts. Based on this recognition, the authors perform a geometrical computation in the second stage to finalize the 3 D model.

The authors verify the feasibility of the proposed method using a case study and find that the classification rate of the deep learning network for part contact relations is higher than 75 per cent. Furthermore, more accurate results could be achieved with modification by the Chu–Liu–Edmonds algorithm. The proposed method has lower computational complexity compared with traditional heuristic methods, and its results are more consistent with existing designs.

The paper introduces machine learning method into assembly modelling issue. The proposed method divides the assembly modelling into two steps and solves the assemble relation creatively.

Frame assemblies are fundamental to many areas. The proposed method could automate frame assembly modelling in a viable way. It could benefit design and manufacture process significantly.

The proposed method expands the application of machine learning into a new field. It would be more useful than simple machine learning in industry. The proposed method is better than general heuristic algorithms. It outputs identical results when the inputs are the same. Meanwhile, the algorithmic complexity in worst situation is better than general heuristic algorithms.

The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.

IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).

The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.

The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.

The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

Internet of Things (IoT) is a challenging and promising system concept and requires new types of architectures and protocols compared to traditional networks. Security is an extremely critical issue for IoT that needs to be addressed efficiently. Heterogeneity being an inherent characteristic of IoT gives rise to many security issues that need to be addressed from the perspective of new architectures such as software defined networking, cryptographic algorithms, federated cloud and edge computing.

The paper analyzes the IoT security from three perspectives: three-layer security architecture, security issues at each layer and security countermeasures. The paper reviews the current state of the art, protocols and technologies used at each layer of security architecture. The paper focuses on various types of attacks that occur at each layer and provides the various approaches used to countermeasure such type of attacks.

The data exchanged between the different devices or applications in the IoT environment are quite sensitive; thus, the security aspect plays a key role and needs to be addressed efficiently. This indicates the urgent needs of developing general security policy and standards for IoT products. The efficient security architecture needs to be imposed but not at the cost of efficiency and scalability. The paper provides empirical insights about how the different security threats at each layer can be mitigated.

The paper fulfills the need of having an extensive and elaborated survey in the field of IoT security, along with suggesting the countermeasures to mitigate the threats occurring at each level of IoT protocol stack.