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The digitization of important documents and their segregation can be a beneficial and time-saving activity as individuals will have greater access to important documents and will be able to use them in regular tasks as well as endeavours. In recent years, research into the application of deep networks in robot systems has increased as a direct consequence of the advancements made in classification algorithms over the past few decades. Robotic vision automation for the segregation of sensitive and non-sensitive documents is required for many security concerns. The methodology of this article is initially focused on the identification of a good computer vision-based technique for the classification of sensitive documents from non-sensitive documents. The authors first identified the standard parameters in terms of reliability, loss, precision, and recall by employing deep learning techniques, such as neural networks with convolutions and transfer learning (TL) algorithms. The extraction of features based on pre-trained deep learning models was referenced in numerous publications. Similarly, we applied most of the feature extraction techniques to identify feature extraction from the images. Then, these features were classified by machine and ensemble learning models. However, the pre-trained models-based feature extraction along with machine learning classification resulted better in comparison to the deep learning and TL procedures. Further, the better-identified techniques were applied as the brain behind the vision of a robotic structure to automate the segregation of sensitive documents from non-sensitive documents. This proposed robotic structure could be applied when we have to find some specific and classified document from the haystack.

To study the development trend of embedded platforms in home appliances Internet of Things (IoT), an application of embedded Linux in the design of IoT gateway is designed. ARM microprocessor S3C2410, EM310 radio frequency (RF) module, and Ethernet DM9000A module are used to realize the home network information-processing platform. The platform utilizes existing network facilities, RF modules, and network interface modules to realize the interconnection of the home perception network through wireless or wired links. For experimental analysis, Ping the central server (gateway) from the IoT node B is set to twice: The first time ttl = 255 time = 1.9 ms, the second time: ttl = 255 time = 2.4 ms. After application verification, the system has the characteristics of low cost, small size, low power consumption, and vital ease of use. It can not only integrate the home Internet and the IoT gateway but also directly run application-layer communication protocols such as HTTP/MQTT between IoT nodes and Internet terminals. The design supports multi-hop communication and significantly increases the deployment range of IoT nodes without signal relay equipment, hence reducing the cost of IoT networking and the complexity of the network architecture.

This paper explores the revolutionary approach of enhancing aluminum composite manufacturing through the integration of Cr2O3 reinforcement using the Friction Stir Technique. The pivotal role of the vertical milling machine in executing Friction Stir Processing (FSP) is emphasized, detailing precise parameters crucial for achieving optimal results. The even dispersion of Cr2O3 throughout the matrix is highlighted as essential for ensuring consistent mechanical and chemical properties, enhancing overall strength, durability, and resistance to corrosion. Experimental findings reveal significant improvements across multiple mechanical properties, including a remarkable 21.56% increase in tensile strength, a notable 36.89% enhancement in hardness, a significant 24.33% improvement in fatigue strength, and a substantial 29.04% increase in wear resistance. These results underscore the effectiveness of Cr2O3 reinforcement via FSP in revolutionizing aluminum composite manufacturing, offering a pathway towards the development of high-performance materials with diverse industrial applications.

Buckling analysis of a vertical structures is crucial in structural design for various loads, and simultaneously, reducing the long structures mass is essential for minimizing weight and cost. This study involves the analysis of long structures with rectangular and circular cross-sections under compressive loads, calculating the buckling load multiplier. Additionally, hollow rectangular and hollow circular columns are designed and analyzed under the same load and boundary conditions as the solid counterparts. By varying the hollowness of the rectangular and circular columns, the buckling load and the percentage of mass saved compared to solid columns are determined. At the same volume of material, the rectangular structure exhibits a 3% higher load multiplier than the circular structure. Increasing mass reduction by introducing hollowness also decreases the buckling load multiplier.

The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.

Vibration signal diagnosis and analysis plays an important role in the industrial machinery since it enhances the machinery performance under supervision. The information regarding the future condition is given by vibration diagnosis techniques which is growing interest for the scientific and industrial communities. Information for failure diagnostic and prediction are provided by the motor vibration through signal processing. The development of mechanical systems fault prognosis and in the last decades, research is done at a very rapid rate. The examination of vibration signal monitoring is done in this paper with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT). The machines maintenance strategies are implemented by using the data collected from machines which are based on the fault prognosis. The cloud computing platform is presented in this paper which is having three layers and the unlabelled data is received to generate an interpreted online decision. Feature extraction of the vibration signal is obtained in terms of range, mean value, root mean square value, and standard deviation and crest values. The performance of the model is evaluated by utilizing the classical statistical metrics such as RMSE Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of the vibration signal. It is obtained that the proposed technique is 25% and 90% better than the Adaptive Neurofuzzy Inference System and the Single Modeling System respectively in terms of RMSE. The performance in terms of MAPE, then the proposed technique outperforms the existing Adaptive Neurofuzzy Inference System and the Single Modeling System by 8 % and 60% respectively. The presented technique is better than the existing Adaptive Neurofuzzy Inference System and the Single Modeling techniques by average of 15% and 30 % respectively.

The speed sensor fusion of urban rail transit train speed ranging based on deep learning builds a user-friendly structure but it in-turn increases the risk of traffic that significantly challenges its safety and transportation efficacy. In order to improve the operation safety and transportation efficiency of urban rail transit trains, a train speed ranging system based on embedded multi-sensor information is proposed in this article. The status information of the train is acquired by the axle speed sensor and the Doppler radar speed sensor; however, the query transponder collects the status information of the train, and is used in the embedded system. Various other modules like adaptive correction, idling/sliding detection and compensation of speed transition/sliding are used in the proposed methodology to reduce the vehicle speed positioning errors due to factors such as wheel wear, idling, sliding, and environment. The results show that the running time of the train is 1000s, the output period of the axle speed sensor is 0.005s and the accelerometer output period is 0.01s. The output cycle of doppler radar is observed to be 0.1s, the output cycle of the transponder is 1s and the fusion period of the main filter is observed as 1s. The train speed ranging system of the embedded multi-sensor information fusion system proposed in this article can effectively improve the accuracy of the train speed positioning.

This paper presents a method for the study of the influence of stability of a power transformer on the power system based on the vibration principle. Traditionally, the EMD and EEMD algorithms are employed to test the box vibration signal data of the power transformer under three working conditions. The proposed method utilizes a partial EMD screening along with MPEEMD method for the online monitoring of power transformer. A complete online monitoring system is designed by using the STM32 processor and LabVIEW system. The proposed system is compared with EMD and EEMD algorithms in terms of the number of IMFs obtained by decomposition, maximum correlation coefficient, and mean square error. The inherent mode correlation, when compared with the mean square error of the reconstructed signal, shows that the reconstruction error of MPEEMD algorithm is 4.762×10−15 which is better than the traditional EMD algorithm. It is observed from the results that the proposed method outperforms both EMD and EEMD algorithms.

As noise brings great error in the analysis of metallographic images, an adaptive weighted mean filtering method proposed to overcome the shortcomings of the standard mean filtering method.

In recent years, Chemical Vapor Deposition (CVD) has emerged as a pivotal technique for the synthesis of high-quality nanomaterials, owing to its ability to produce uniform and scalable thin films with controlled properties. This study presents a comprehensive characterization and modelling of nanomaterials synthesized via CVD, elucidating the intricate relationship between process parameters and the resultant material properties. Utilizing advanced characterization techniques, including Transmission Electron Microscopy (TEM), XPS, and Raman Spectroscopy, we have discerned the morphological, compositional, and structural attributes of the synthesized nanomaterials. The experimental data were subsequently employed to develop a predictive model, leveraging machine learning algorithms, to forecast the properties of nanomaterials based on CVD parameters. The model exhibited high accuracy and can serve as a robust tool for optimizing CVD processes in real-time. Our findings underscore the potential of CVD in tailoring nanomaterial properties for specific applications and provide a foundational framework for researchers and industries aiming to harness the full potential of nanomaterials synthesized via CVD. This work not only advances our understanding of CVD-synthesized nanomaterials but also paves the way for their application in next-generation electronic, photonic, and energy devices.

Computer vision systems are increasingly focusing on age recognition from facial images. To solve this problem, In this paper, proposed a method that computes the Fibonacci Weighted Neighborhood Pattern on an image to obtain local neighborhood information, then evaluates Co-occurrence features for work sustainability age classification with SVM classifier. These characteristics show how people’s ages differ. The proposed method has been tested on the FG-Net facial images dataset as well as other scanned images. Experiments showed that the proposed approach outperformed other currently existing methods.

In recent years, intermetallic reinforced composites (IRCs) have garnered significant attention due to their exceptional mechanical properties, corrosion resistance, and high-temperature stability, making them ideal candidates for both structural and functional applications. This research paper presents an advanced modelling and simulation approach to understand the microstructural evolution, mechanical behaviour, and functional properties of IRCs. Utilizing a combination of finite element analysis (FEA), molecular dynamics (MD), and phase-field modelling, the study offers a comprehensive insight into the intricate interplay between the matrix, reinforcement, and the resultant composite behaviour. The developed models accurately predict the stress-strain response, thermal conductivity, and fatigue life of the IRCs under various loading and environmental conditions. Furthermore, the simulations provide a detailed understanding of the mechanisms governing crack initiation and propagation in these composites. The outcomes of this research not only pave the way for optimizing the design and processing parameters of IRCs but also underscore the potential of these materials in aerospace, automotive, and energy sectors. The findings presented herein serve as a foundational reference for researchers and engineers aiming to harness the full potential of intermetallic reinforced composites in advanced engineering applications.

The effect of magnetic field dependent (MFD) viscosity on thermal convection in a horizontal ferromagnetic fluid layer has been investigated numerically. A correction is applied to Sunil et al. [24] which is very important in order to predict the correct behavior of MFD viscosity. Linear stability analysis has been carried out for stationary convection. The MFD viscosity parameter δ as well as the measure of nonlinearity of magnetization M3, both have a stabilizing effect on the system. Numerical results are also obtained for large values of magnetic parameter M1 and predicted graphically.

One step green synthesis of silver nanoparticles (AgNPs) from silver nitrate (AgNO3) using Leucaena leucocephala seeds extract as the reducing agent at room temperature was performed. The bioreduced NPs were characterized by UV-vis spectroscopy, dynamic light scattering (DLS), transmission electron microscopy (TEM), scanning electron microscopy (SEM) coupled with energy dispersive spectrophotometry, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA) techniques. Qualitative information of major components in the seed extract was obtained through its phytochemical screening. The phytochemical data of L. leucocephala revealed the presence of terpenes, flavonoids, coumarins and sterols. The reaction was optimized for AgNO3, extract concentration and time duration for the reaction. The obtained NPs showed a characteristic UV peak of AgNPs at 420 nm. TEM and SEM images showed the spherical shaped NPs over which the extract coating was very prominent. The binding of L. leucocephala seeds extract onto NPs was tested using FTIR and TGA. The antifungal activity of the as-synthesized NPs against two fungal species, namely Phlebiopsis gigantea and Echinodontium taxodii, was studied. The antimicrobial effect of the as-synthesized NPs was ascertained against Escherichia coli and Staphylococcus aureus. The antioxidant potential of the AgNPs was tested with 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging. Also, the sensitivity of the NPs towards Fe3+ ions was tested in aqueous media.