JournalTOCs

International Journal of Intelligent Unmanned Systems
Number of Followers: 3

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2049-6427 - ISSN (Online) 2049-6435
Published by Emerald

[362 journals]

Retraction notice: Semantic tracking and recommendation using fourfold
similarity measure from large scale data using hadoop distributed
framework in cloud
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  Retraction notice: Semantic tracking and recommendation using fourfold similarity measure from large scale data using hadoop distributed framework in cloud
  International Journal of Intelligent Unmanned Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-International Journal of Intelligent Unmanned Systems2024-08-09
  DOI: 10.1108/IJIUS-07-2024-0180
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Detection, identification and alert of wild animals in surveillance videos
using deep learning
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  Authors: Harish A. Jartarghar, M.N. Kruthi, B. Karuntharaka, Azra Nasreen, T. Shankar, Ramakanth Kumar, K. Sreelakshmi
  Abstract: With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances. The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome. Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally. The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-07-26
  DOI: 10.1108/IJIUS-09-2022-0125
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Retraction notice: An RSSI-based Sybil attack detection system with
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  Retraction notice: An RSSI-based Sybil attack detection system with continuous authentication using a novel lightweight multimodal biometrics
  International Journal of Intelligent Unmanned Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-International Journal of Intelligent Unmanned Systems2024-07-15
  DOI: 10.1108/IJIUS-05-2024-0154
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
A systematic study of traffic sign recognition and obstacle detection in
autonomous vehicles
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  Authors: Reshma Dnyandev Vartak Koli, Avinash Sharma
  Abstract: This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets. In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS. The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey. Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-07-02
  DOI: 10.1108/IJIUS-03-2024-0065
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Secure data communication in WSN using Prairie Indica optimization
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  Authors: Amruta Chandrakant Amune, Himangi Pande
  Abstract: Security is the major issue that motivates multiple scholars to discover security solutions apart from the advantages of wireless sensor networks (WSN) such as strong compatibility, flexible communication and low cost. However, there exist a few challenges, such as the complexity of choosing the expected cluster, communication overhead, routing selection and the energy level that affects the entire communication. The ultimate aim of the research is to secure data communication in WSN using prairie indica optimization. Initially, the network simulator sets up clusters of sensor nodes. The simulator then selects the Cluster Head and optimizes routing using an advanced Prairie Indica Optimization algorithm to find the most efficient communication paths. Sensor nodes collect data, which is securely transmitted to the base station. By applying prairie indica optimization to WSNs, optimize key aspects of data communication, including secure routing and encryption, to protect sensitive information from potential threats. The Prairie Indica Optimization, as proposed, achieves impressive results for networks comprising 50 nodes, with delay, energy and throughput values of 77.39 ms, 21.68 J and 22.59 bps. In the case of 100-node networks, the achieved values are 80.95 ms, 27.74 J and 22.03 bps, significantly surpassing the performance of current techniques. These outcomes underscore the substantial improvements brought about by the Prairie Indica Optimization in enhancing WSN data communication. In this research, the Prairie Indica Optimization is designed to enhance the security of data communication within WSN.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-06-25
  DOI: 10.1108/IJIUS-12-2023-0187
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Analyzing the opening and closing of windows in residential for predicting
the energy consumption using optimized multi-scale convolution networks
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  Authors: C. Sivapriya, G. Subbaiyan
  Abstract: This proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques. An EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO2 in indoor spaces is highly reduced. The MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN. The findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-05-31
  DOI: 10.1108/IJIUS-06-2023-0059
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
A novel quadrotor carrying payload concept via PID with Feedforward terms
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  Authors: Saman Yazdannik, Shamim Sanisales, Morteza Tayefi
  Abstract: This paper introduces control strategy to enhance the performance of a novel quadrotor unmanned aerial vehicle designed for medical payload delivery. The aim is to achieve precise control and stability when carrying and releasing payloads, which alter the quadrotor’s mass and inertia characteristics. The equations of motion specific to the payload-carrying quadrotor are derived. A feedforward-proportional-integral-derivative (FF-PID) control strategy is then proposed to address the dynamic changes during payload release. The PID components use propeller speed/orientation information for stability. FF terms based on derivatives of desired position/orientation variables enable adaptation to real-time mass fluctuations. Extensive simulations, encompassing various fault scenarios, substantiate the effectiveness of the FF-PID approach. Notably, our findings demonstrate superior performance in maintaining altitude precision and stability during critical phases such as takeoff, payload release and landing. Graphical representations of thrust and mass dynamics distinctly illustrate the payload release event. In contrast to the linear quadratic regulator (LQR) and conventional PID control, which encountered difficulties during the payload release process, our approach proves its robustness and reliability. This study, primarily based on simulations, demands validation through real-world testing in diverse conditions. Uncertainties in dynamic parameters, external factors and the applicability of the proposed approach to other quadrotor configurations require further investigation. Additionally, this research focuses on controlled payload release, leaving unexplored the challenges posed by unforeseen scenarios or disturbances. Hence, adaptability and fault tolerance necessitate further exploration. While our work presents a promising approach, practical implementation, adaptability and resilience to unexpected events are vital considerations for future research in the field of autonomous aerial medical deliveries. The proposed control strategy promises enhanced efficiency, reliability and adaptability for autonomous aerial medical deliveries in critical scenarios. The innovative control strategy introduced in this study holds the potential to significantly impact society by enhancing the reliability and adaptability of autonomous aerial medical deliveries. This could lead to faster and more efficient delivery of life-saving supplies to remote or disaster-affected areas, ultimately saving lives and reducing suffering. Moreover, the technology’s adaptability may have broader applications in fields like disaster relief, search and rescue missions, and industrial cargo transport. However, its successful integration into society will require careful regulation, privacy safeguards and ethical considerations to ensure responsible and safe deployment while addressing potential concerns related to noise pollution and privacy intrusion. While PID control of quadrotors is extensively studied, payload release dynamics have been overlooked. This research studies integration of FF control to enable PID adaptation for a novel payload delivery application.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-05-14
  DOI: 10.1108/IJIUS-10-2023-0141
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Procedure for describing traffic situation scene development
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  Authors: Anna Korotysheva, Sergey Zhukov
  Abstract: This study aims to comprehensively address the challenge of delineating traffic scenarios in video footage captured by an embedded camera within an autonomous vehicle. This methodology involves systematically elucidating the traffic context by leveraging data from the object recognition subsystem embedded in vehicular road infrastructure. A knowledge base containing production rules and logical inference mechanism was developed. These components enable real-time procedures for describing traffic situations. The production rule system focuses on semantically modeling entities that are categorized as traffic lights and road signs. The effectiveness of the methodology was tested experimentally using diverse image datasets representing various meteorological conditions. A thorough analysis of the results was conducted, which opens avenues for future research. Originality lies in the potential integration of the developed methodology into an autonomous vehicle’s control system, working alongside other procedures that analyze the current situation. These applications extend to driver assistance systems, harmonized with augmented reality technology, and enhance human decision-making processes.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-05-09
  DOI: 10.1108/IJIUS-09-2023-0113
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Detection of black hole attacks in vehicle-to-vehicle communications using
ad hoc networks and on demand protocols
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  Authors: C. Bharanidharan, S. Malathi, Hariprasath Manoharan
  Abstract: The potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety. The black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system. Through the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs. All things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-04-30
  DOI: 10.1108/IJIUS-02-2023-0016
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Multibody dynamics modeling and simulation of a three-wheeled mobile robot
using a robotic approach
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  Authors: Aymen Khadr
  Abstract: This paper focuses on the application of a robotic technique for modeling a three-wheeled mobile robot (WMR), considering it as a multibody polyarticulated system. Then the dynamic behavior of the developed model is verified using a physical model obtained by Simscape Multibody. Firstly, a geometric model is developed using the modified Denavit–Hartenberg method. Then the dynamic model is derived using the algorithm of Newton–Euler. The developed model is performed for a three-wheeled differentially driven robot, which incorporates the slippage of wheels by including the Kiencke tire model to take into account the interaction of wheels with the ground. For the physical model, the mobile robot is designed using Solidworks. Then it is exported to Matlab using Simscape Multibody. The control of the WMR for both models is realized using Matlab/Simulink and aims to ensure efficient tracking of the desired trajectory. Simulation results show a good similarity between the two models and verify both longitudinal and lateral behaviors of the WMR. This demonstrates the effectiveness of the developed model using the robotic approach and proves that it is sufficiently precise for the design of control schemes. The motivation to adopt this robotic approach compared to conventional methods is the fact that it makes it possible to obtain models with a reduced number of operations. Furthermore, it allows the facility of implementation by numerical or symbolical programming. This work serves as a reference link for extending this methodology to other types of mobile robots.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2024-04-24
  DOI: 10.1108/IJIUS-12-2023-0192
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Towards a novel cyber physical control system framework: a deep learning
driven use case
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  Authors: Mariam Moufaddal, Asmaa Benghabrit, Imane Bouhaddou
  Abstract: The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations. The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly. The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods. This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2023-09-28
  DOI: 10.1108/IJIUS-03-2022-0031
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2023)
Intelligent personal assistant for personal computers using long
short-term memory-based verbalizer
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  Authors: Iwin Thanakumar Joseph Swamidason, Sravanthy Tatiparthi, Karunakaran Velswamy, S. Velliangiri
  Abstract: An intelligent personal assistant for personal computers (PCs) is a vital application for the current generation. The current computer personal assistant services checking frameworks are not proficient at removing significant data from PCs and long-range informal communication information. The proposed verbalizers use long short-term memory to classify the user task and give proper guidelines to the users. The outcomes show that the proposed method determinedly handles heterogeneous information and improves precision. The main advantage of long short-term memory is that handle the long-term dependencies in the input data. The proposed model gives the 22% mean absolute error. The proposed method reduces mean square error than support vector machine (SVM), convolutional neural network (CNN), multilayer perceptron (MLP) and K-nearest neighbors (KNN). This paper fulfills the necessity of intelligent personal assistant for PCs using verbalizer.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2022-07-05
  DOI: 10.1108/IJIUS-02-2022-0012
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2022)
Analytical review on deep learning and IoT for smart healthcare monitoring
system
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  Authors: Sangeetha Yempally, Sanjay Kumar Singh, S. Velliangiri
  Abstract: Selecting and using the same health monitoring devices for a particular problem is a tedious task. This paper aims to provide a comprehensive review of 40 research papers giving the Smart health monitoring system using Internet of things (IoT) and Deep learning. Health Monitoring Systems play a significant role in the healthcare sector. The development and testing of health monitoring devices using IoT and deep learning dominate the healthcare sector. In addition, the detailed conversation and investigation are finished by techniques and development framework.
  Authors have identified the research gap and presented future research directions in IoT, edge computing and deep learning. The gathered research articles are examined, and the gaps and issues that the current research papers confront are discussed. In addition, based on various research gaps, this assessment proposes the primary future scope for deep learning and IoT health monitoring model.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2022-06-07
  DOI: 10.1108/IJIUS-02-2022-0019
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2022)
An intelligent optimization technique for performance improvement in
radial distribution network
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  Authors: Rafi Vempalle, Dhal Pradyumna Kumar
  Abstract: The demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization. In this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques. The proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms. The main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2022-06-06
  DOI: 10.1108/IJIUS-04-2022-0052
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2022)
Predicting arrhythmia, atrial fibrillation from electrocardiogram signals
using Pivot Range Fitness Scale-Based Machine Learning Model
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  Authors: Sreedhar Jyothi, Geetanjali Nelloru
  Abstract: Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually. There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted. This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity. The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2022-04-22
  DOI: 10.1108/IJIUS-11-2021-0140
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2022)
Microstructure investigations and optimisation of maraging steel parts for
UAV applications
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  Authors: Rama Pavan Kumar Varma Indukuri, Rama Murty Raju Penmetsa, Srinivasa Rao Chalamalasetti, Rajesh Siriyala
  Abstract: Military and unmanned aerial vehicles (UAV) applications like rocket motor casings, missile covers and ship hulls use components that are made of maraging steel. Maraging steel has properties that are superior to other metals, making it more suitable for the fabrication of such components. A grey relational analysis (GRA) that is based on the Taguchi method has been utilised in the current study to optimise a laser beam welding (LBW) process. Further aspects such as GRA's optimum ranges and percentage contributions were also estimated. A Taguchi L16 orthogonal array is utilised to design and conduct the experiments. Laser power (LP), welding speed (WS) and focal position (FP) are the three parameters are chosen for the process of welding. The output responses are the upper width of the heat-affected zone (HAZup), the upper width of the fusion zone (FZup) and the depth of penetration (DOP). The effect of the above key parameters on the responses was examined using an analysis of variance (ANOVA). The results of ANOVA reveal that the parameter that has the most influence on the overall grey relational grade (GRG) is the FP. Finally, metallographic characterisation and a microstructural analysis are conducted on the weld bead geometry to demarcate the zone of HAZ and fusion zone (FZ). As the most important criteria for LBW of maraging steels is the provision of higher DOP, higher FZ width and lower heat-affected zone, the study intended to prove the applicability of GRA technique in solving multi-objective optimisation problems in applications like defence and unmanned systems.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2022-02-24
  DOI: 10.1108/IJIUS-11-2021-0136
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2022)
Framelet transform and fuzzy clustering-based intelligent technique for
speckle noise removal in ultrasound images
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  Authors: Praveen Kumar Lendale, N.M. Nandhitha
  Abstract: Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches. The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information. The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators. Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2021-12-31
  DOI: 10.1108/IJIUS-07-2021-0086
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2021)
Development of green synthesized nanomaterials for hybrid vehicle
applications
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  Authors: Dayanand Bhaurao Jadhav, Rajendra D. Kokate
  Abstract: Renewable energy alternatives and nanoscale materials have gained huge attention in recent years due to the problems associated with fossil fuels. The recyclable battery is one of the recent developments to address the energy requirement issues. In this work, the development of nanoscale materials is focused on using green synthesis methods to address the energy requirements of hybrid electric vehicles. The current research focuses on developing metal oxide nanoscale materials (NANO-SMs). The Zno-Aloe vera NANO-SM is prepared using the green synthesis method. The developed nanoscale materials are characterized using analysis methods like FESEM, TEM, XRD and FTIR. The average size of ZnO-Aloe vera mono-crystalline was recorded as 60–70 nm/Hexagonal shape. The nanoscale materials are used for the detection of LPG gases. The sensitivity observed was 48%. The response time and recovery time were recorded as 8–10 s and 230–250 s, respectively. The average size of SnO2-green papaya leaves poly-crystalline was recorded as 10–20 nm/powder form. Nanoscale materials are developed using green synthesis methods for hybrid vehicle applications. The nanoscale materials are used for the detection of harmful gases in hybrid vehicles.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2021-11-19
  DOI: 10.1108/IJIUS-07-2021-0085
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2021)
High throughput SRAM design for improved computing in autonomous systems
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  Authors: Kumar Neeraj, Jitendra Kumar Das
  Abstract: High throughput and power efficient computing devices are highly essential in many autonomous system-based applications. Since the computational power keeps on increasing in recent years, it is necessary to develop energy efficient static RAM (SRAM) memories with high speed. Nowadays, Static Random-Access Memory cells are predominantly liable to soft errors due to the serious charge which is crucial to trouble a cell because of fewer noise margins, short supply voltages and lesser node capacitances. Power efficient SRAM design is a major task for improving computing abilities of autonomous systems. In this research, instability is considered as a major issue present in the design of SRAM. Therefore, to eliminate soft errors and balance leakage instability problems, a signal noise margin (SNM) through the level shifter circuit is proposed. Bias Temperature Instabilities (BTI) are considered as the primary technology for recently combined devices to reduce degradation. The proposed level shifter-based 6T SRAM achieves better results in terms of delay, power and SNM when compared with existing 6T devices and this 6T SRAM-BTI with 7 nm technology is also applicable for low power portable healthcare applications. In biomedical applications, Body Area Networks (BANs) require the power-efficient SRAM design to extend the battery life of BAN sensor nodes. The proposed method focuses on high speed and power efficient SRAM design for smart ubiquitous sensors. The effect of BTI is almost eliminated in the proposed design.
  Citation: International Journal of Intelligent Unmanned Systems
  PubDate: 2021-08-24
  DOI: 10.1108/IJIUS-05-2021-0031
  Issue No: Vol. 12, No. 3 (2021)

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