A DRL based 4-r Computation Model for Object Detection on RSU using LiDAR in IloT

Internet of vehicle (IoV) network comprises Road Side Unit (RSU), which has become a computation and communication device for effective LiDAR data communication (ex: object detect information) between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle. However, the LiDARs generate a massive volume of 3D data with a notable redundancy rate leads to inadequate object detection accuracy, and the high operational cost of RSU due to inadequate resource and time consumption. Estimating the computation capacity for RSU selection is an NP-hard problem. To address this issue, we propose a Deep Reinforcement Learning (DRL) influenced 4-r computation model to measure RSU cost based on resource feasibility factor and object region detection rate based on novel region-of-interest (RoI) strategy. The resource feasibility factor appraises the residual capacity and cost of RSU based on a criterion of optimality. The RoI strategy eliminates irrelevant points, noise and ground points based on distance and shape measures of an object on RSU with feasible consumption of computation resources. The simulation results show that our mechanism achieves 83% average object detection accuracy rate, 81% average service rate and 17% service offloading rate than state-of-art approaches

Fe-Dy Nanogranular Thin Films: Investigation of Structural, Microstructural and Magnetic Properties

A series of Few(100) Dy-x(x) thin films with the concentration range x = 6 to 35 were fabricated by dc magnetron sputtering process. X-ray diffraction and TEM studies revealed that films have separate Fe and Dy nanograins and that there is no intermixing of Fe and Dy thus forming nanogranular films. This unmixed behaviour in our thin films is very different from the bulk Fe-Dy alloys where several stoichiometric compounds can be formed. Magnetic properties of the films have been systematically studied. The contribution to the total magnetization is due to the Fe grains and the Dy grains are paramagnetic down to 4 K. The saturation magnetization of all the samples is significantly lower than that of bulk Fe due to the existence of superparamagnetic Fe grains. Upon increasing x, the in-plane magnetic anisotropy is found to decrease and the samples become isotropic. The zero field cooled and field cooled magnetization measurements also confirmed the presence of the superparamagnetic Fe grains

Exchange Bias Induced by the Spin Glass-Like Surface Spins in Sputter Deposited Fe3O4 Thin Films

The exchange bias in the reactive sputtered polycrystalline Fe3O4 thin films of thicknesses 50 and 150 nm is studied. X-ray diffraction, laser Raman, and selected area electron diffraction studies confirm the formation of the Fe3O4 single phase. The high-resolution transmission electron microscope images show the presence of well-defined crystallites. The presence of the exchange bias effect is mainly due to the existence of a significant exchange coupling between the core spins and the spin glass-like surface spins of the grains. The temperature dependence of the magnitude of the exchange bias field HEB shows two exponential regimes of which the lower temperature regime corresponds to the spin freezing effect below 50 K. The first magnetization curves measured after zero field cooling show S-shape below the spin freezing temperature. The presence of superparamagnetism and spin freezing has been investigated through the field cooled (FC) and zero FC magnetization measurements. Temperature dependence of coercivity also indicates the spin freezing effect. Hence, the observed large exchange bias of the samples at lower temperatures is due to the freezing of the surface spins

A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate a feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design an Angular-based node position analysis method to localize the node through rotation and t-gate usage to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results show that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks

Machine Learning Inspired Phishing Detection (PD) for Efficient Classification and Secure Storage Distribution (SSD) for Cloud-IoT Application

Cloud-IoT data security and privacy have become a major problem due to its sensitivity, which curbs multiple cloud applications. In addition, if the encrypted data lives in one place, in many fields, such as the financial industry and government agencies, the man-in-the-middle-attack (MMA) and phishing attack (PA) may have chances of realising the extraction. The phishing goal is evaluated and predicted by most previous machine learning models through a discrete or continuous result. The current models lag in accurately determining both attacks because of this approach. We developed a three-step phishing detection (PD) framework inspired by machine learning and a secure storage distribution (SSD) for cloud to improve model accuracy and storage security. The partition-based selection of features is designed for phishing detection (PD) with a hybrid classifier approach and hyper-parameter classifier tuning. Initially, the entire data set is partitioned by entropy and is hybridised for each performing model partition. In order to reduce the complexity, the next entropy is applied to decrease the dimension of each partition. Finally, to improve precision, the performing model is optimised with hyper-parameter tuning. The partition-based feature choice with the hybrid classifier approach outperforms with 97.86% accuracy for both attack detection from the experimental and comparative results of SVM, LM, NN and RF. Atlast, SSD performance is evaluated against other storage models where SSD outperforms other models

Sign Language Translator for Dumb and Deaf

The detection of sign language for the deaf and dumb community is a challenging task due to the complex and variable nature of signing gestures One problem that can arise in this task is overfitting where the model tries to memorize the training data rather than generalize patterns from it This can lead to underwhelming results on new and unseen information One common way to increase model capacity is to add more hidden units to the neural network which can exacerbate overfitting To address this problem We propose combining L2 regularisation with convolutional neural networks and long short term memory LSTM models implementing relu activation functions This approach facilitates the model to acquire knowledge of complex temporal patterns while the final dense layers enable meaningful classification The probabilities of the output layers are determined by the softmax activation function This method penalises greater weights in the model in order to promote the determination of simplified patterns in the data By using this technique we can increase the capacity of the model without overfitting leading to better generalization performance on new data Our proposed method has the potential to increase the precision of sign language detection allowing the deaf and dumb to better interact with the hearing worl