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Gabor filter is a time-frequency combined analysis method, which is suitable for detecting local anomalies in periodic textures. Gabor-based methods mainly include the optimal channel method and multi-channel fusion method. Compared with the optimal channel method, the multi-channel fusion method can obtain more complete image features and has advantages in detecting mixed and isotropic defects. However, the multi-channel fusion method has the problem of feature redundancy and poor anti-noise ability, which reduces the effect of the algorithm. Therefore, a novel fabric defect detection algorithm based on residual energy distribution and Gabor feature fusion is proposed. First, we use a relatively complete bank of Gabor filters to extract the testing and template image features under different channels and calculate the residual energy between them. Then we use the max-to-mean ratio (MMR) metric to measure the saliency of each channel’s defect features and use nonlinear normalization to calculate the weight of each channel. Finally, fuse the multi-channel Gabor features according to the weights. In addition, we optimize the parameters using the signal-to-noise ratio (SNR) indicator and genetic algorithm. Experiments show that the proposed algorithm has advantages over the current state-of-the-art defect detection algorithms.

Wireless sensor networks is one of the important parts in modern-day communication that employing low-cost sensor devices with different environmental and physical parameters. The communication path between the base station and sensor nodes are built with the help of an efficient routing protocol. In the past years, the existing protocols met few difficulties in terms of higher computational complexity, poor cluster head selection performance, higher energy consumption, expensive in cluster head selection, scalability management, and uneven load distribution, and so on. In this paper, we proposed BM-BWO with fuzzy logic based HEED protocol (BMBWFL-HEED). In BMBWFL-HEED, we use the combination of the boosted mutation based black widow optimization (BM-BWO) algorithm with HEED protocol to select the higher residual energy. Particularly, the mutation phase of the Black Widow Optimization (BWO) algorithm is improved with the help of direction average strategy (BM-BWO). The fuzzy logic system selects the most relevant and optima cluster heads. Different kinds of experimental analysis, benchmark functions are applied to evaluate the performance of proposed BMBWFL-HEED protocol and it is compared with some existing algorithms like ICFL -HEED, HEED, and ICHB-HEED. In the case of residual energy, a variation of energy consumption and the number of cluster head formation for both homogeneous and heterogeneous environments. The proposed BMBWFL-HEED method demonstrates optimal performance output among all other methods.

The openness nature of the Ad-hoc sensor networks emerged as a security threat in this network environment that leads to packets drop, network overhead, high range energy consumption, and transmission delay. Previous methods such as Trust-Based Malicious Nodes Detection in AD-hoc (TBMND) and Security based Data-Aware Routing Protocol (SDARP) found that this happens due to malicious nodes in the network. A malicious node in a network degrades its efficiency that also affects the routing decision making and also makes error decisions in route selection. Trust vector model and Trust-based Secure Routing (TBSR) have made attempts to evaluate trust nodes via various techniques, but unfortunately, the accuracy level has not been reached to the required range. In this paper, a trust-based energy-aware routing using GEOSR protocol for Ad-hoc sensor networks is presented for providing an energy-efficient and secured routing. In this trust-based energy-aware routing, initially the sensor nodes are deployed in the ad-hoc network. The clustering is performed based on the estimation of distance among the each nodes and the cluster head (CH) is selected based on the threshold value. After CH selection, the trust evaluation is performed for identify the trust and untrusted nodes present in the network. The untrusted node is considered as malicious node which is detected and blocked. The trusted nodes are forwarded to select the optimal routing path for secured transmission. Golden Eagle Optimized Secure Routing (GEOSR) is introduced for selecting the optimal routing path based on the parameters such as distance delay and energy objective function. Thus, energy-efficient and secured routing were done using the GEOSR protocol. GEOSR protocol was then implemented in the NS-2 simulation tool and then compared with existing techniques. GEOSR protocol shows 95% of residual energy for the prediction of two malicious nodes. Thus GEOSR was suitable for real-time applications.

Conserving energy and finding the stable path are the two vital issues in Mobile Ad Hoc Networks (MANETs) as the prior increases the network lifetime and the later increases the network throughput. The nodes which are not legitimate in terms of residual energy and packet forwarding history might be a threat to the path thereby making the path unstable. Thus, it results in frequent link failure, reduced throughput, reduced network and path life time. In order to reduce these hazards, authors have proposed an energy efficient, reliable path selecting protocol referred to as Trust Based Energy Aware Multipath Disjoint Routing Protocol (TEA-MDRP) for MANETS in this article. TEA-MDRP finds the optimum route between the source and the destination nodes using two parameters namely, the Accumulated Trust Value (ATV) and the node’s residual energy (N_res). ATV is calculated based on the packet forwarding status of the node which shows how good the node is in terms of packet forwarding point of view. TEA-MDRP allows only the nodes which have a good ATV and sufficient residual energy. A good ATV shows loyalty in forwarding the packets while a sufficiently large residual energy node avoids frequent path breakups and packet drops. Thus, the TEA-MDRP not only increases the network and path lifetime but also increases the throughput of the communication. Further, with the legitimate nodes in the paths, the TEA-MDRP considerably reduces the control packet overhead which might occur because of the frequent route re-discovery process. An extensive simulation is carried out using Network Simulator-2.35 for the quantitative and qualitative analysis of TEA-MDRP. The results obtained are compared with classical AOMDV and the results are satisfactory.

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA.

Mobile Ad-hoc Networks have distinct characteristics namely lack of centralized control and management, severe resource constraints in terms of energy level of nodes, computing power, frequent mobility and frequent change of topology. This dynamic nature of mobile adhoc networks lead to additional overhead in the provision of secured and stable routing. In order to address these issues, we propose a new integrated approach for secure routing approach in this paper which is made of two new algorithms called as the Trust based Next Forwarding Node Selection algorithm and Fuzzy Based Stable and Secure Routing algorithm that makes use of the trust based node selection procedure for providing efficient routing performance. The main contribution of this newly proposed node selection procedure is that this technique uses trust values to isolate the malicious nodes from the routing process in order to enhance the security. Hence, this proposed stable and secured routing technique performs reliable routing by selecting only trusted nodes with high residual energy and link stability. Another contribution of this work is the development of a Fuzzy Inference System which is used to handle uncertainty in the selection of trusted nodes and to identify the stable routes by performing qualitative analysis on trust values and link properties. From the implementation and testing carried out in this research work, it has been observed and proved that this proposed secured routing algorithm is capable of increasing the network performance in terms of improved packet delivery ratio, reduction in delay as well as false positive rate when compared with related secure routing algorithms.

Rockburst is a kind of rock failure phenomenon during which the internal elastic strain energy of surrounding rock mass is released dynamically under external load, and the loading rate is an essential influencing factor of potential for bursting. To investigate the effects of loading rate on rockburst proneness from energy storage and surplus perspectives, conventional uniaxial compression tests are conducted on granite under four orders of magnitude loading rate. The failure process and mode of granite specimens were recorded in real time with a high-speed camera with microsecond shooting speed. The variation trend of the internal elastic strain energy of granite specimens under four loading rates was obtained by performing the single-cycle loading–unloading uniaxial compression test. The experimental results show that the elastic strain energy linearly increases as the input strain energy increases under each loading rate, which meet the linear energy storage law. Based on the linear energy storage law, the peak elastic strain energy of each granite specimen can be accurately obtained. According to the mass and range of ejected rock debris after specimen failure, the bursting liability of each specimen was evaluated by the far-field ejection mass ratio (MF) from a qualitative point of view. Meanwhile, the residual elastic energy index (AEF) and the other three criteria were used to evaluate the potential for bursting of granite specimens under different loading rates. The comparison results show the rockburst proneness of granite specimens increases with the loading rate and that the evaluation results of MF and AEF are unified from qualitative and quantitative aspects, respectively. The fundamental reason for the consistent results is that these two indexes have a common essence of elastic strain energy release.HighlightsThe mechanical properties of granite are significantly influenced by loading rate.The loading rate does not affect the existence of linear energy storage law and the compression energy storage coefficient is independent of loading rate.The rockburst liability of granite increases with the loading rate.At different loading rates, the far-field ejection mass ratio and residual elastic energy index offer consistent qualitative and quantitative evaluations for the rockburst proneness of granite.

The network life of wireless sensor networks (WSNs) relies on the limited energy of non-rechargeable batteries used at the sensor node. Hence, maximum energy saving is essential in the research area while designing a routing algorithm for the WSNs. An energy-saving opportunistic routing (ENS_OR) uses an opportunistic routing concept to improve network performance while relaying data. In this paper, the ENS_OR is further revised with a sleep scheduling algorithm to reduce energy dissipation in one dimensional topology. The proposed sleep scheduling algorithm is designed to enhance network performance by minimizing energy dissipation due to the idle listening of nodes. Sleep interval is adaptive, and it is made proportional to the residual energy of nodes as well as the flow rate of the network. The results of the proposed algorithm are analyzed and compared with ENS_OR without sleep mode and other routing protocols used in WSNs. The results prove that ENS_OR with sleep mode is beneficial to conserve energy for a prolonged lifetime.

Within the scope of this contribution, a method for the determination of a strain limit for designing components made of elastomeric polyurethane systems is presented. The knowledge of a material-specific strain limit is essential for the structural-mechanical calculation of plastic components in the context of component design. Compared to a commonly used component design, based on a simplified dimensioning approach taking only linear viscoelastic deformations into account, the strain limit determined in this study allows an improved utilisation of lightweight construction potential in the dimensioning of technical components made of polyurethanes through the consideration of permissible nonlinear viscoelastic deformations. The test method comprises a sequence of quasi-static loading and unloading cycles, with a subsequent load-free recovery phase, allowing the relaxation of the viscoelastic forces. Standardised tensile and simple shear test specimens and a dynamic mechanical thermal analyser (DMTA) are used within the tests. The strain limit is determined by means of the so-called residual energy ratio, which is a characteristic quantity for the evaluation of hystereses of load–unload cycles. These hystereses are increasingly formed by deformations outside the range of linear viscoelastic deformations. The residual energy ratio relates the proportion of deformation energy recovered during unloading to the deformation work that is applied. In this contribution, the residual energy ratio is successfully used to detect a significant evolution of loss energy under increasing load and to correlate this transition to a characteristic strain. The latter is used as a dimensioning parameter for the design of components made of elastomeric polyurethane materials for quasi-static load cases. The determination of this strain limit is performed under consideration of the criterion of reversibility of deformation.

In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same size. Moreover, owing to randomly distributed node distribution, a cluster with equal nodes is not an obvious possibility to reduce the energy consumption. To resolve this issue, this paper provides a novel, Balanced-Imbalanced Cluster Algorithm (B-IBCA) with a Stabilized Boltzmann Approach (SBA) that attempts to balance the energy dissipation across uneven clusters in WSNs. BIBCA utilizes stabilizing logic to maintain the consistency of energy consumption among sensor nodes’. So as to handle the changing topological characteristics of sensor nodes, this stability based Boltzmann estimation algorithm allocates proper radius amongst the sensor nodes. The simulation shows that the proposed B-IBCA outperforms effectually over other approaches in terms of energy efficiency, lifetime, network stability, average residual energy and so on.