Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.
Security and imperceptibility improving of image steganography using pixel al...IJECEIAES
Information security is one of the main aspects of processes and methodologies in the technical age of information and communication. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography and cryptography. An effective digital image-steganographic method based on odd/even pixel allocation and random function to increase the security and imperceptibility has been improved. This lately developed outline has been verified for increasing the security and imperceptibility to determine the existent problems. Huffman coding has been used to modify secret data prior embedding stage; this modified equivalent secret data that prevent the secret data from attackers to increase the secret data capacities. The main objective of our scheme is to boost the peak-signal-to-noise-ratio (PSNR) of the stego cover and stop against any attack. The size of the secret data also increases. The results confirm good PSNR values in addition of these findings confirmed the proposed method eligibility.
Technical analysis of content placement algorithms for content delivery netwo...IJECEIAES
Content placement algorithm is an integral part of the cloud-based content de-livery network. They are responsible for selecting a precise content to be re-posited over the surrogate servers distributed over a geographical region. Although various works are being already carried out in this sector, there are loopholes connected to most of the work, which doesn't have much disclosure. It is already known that quality of service, quality of experience, and the cost is always an essential objective targeting to be improved in existing work. Still, there are various other aspects and underlying reasons which are equally important from the design aspect. Therefore, this paper contributes towards reviewing the existing approaches of content placement algorithm over cloud-based content delivery network targeting to explore open-end re-search issues.
Comparative study between metaheuristic algorithms for internet of things wir...IJECEIAES
Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Asymmetric image encryption scheme based on Massey Omura scheme IJECEIAES
Asymmetric image encryption schemes have shown high resistance against modern cryptanalysis. Massey Omura scheme is one of the popular asymmetric key cryptosystems based on the hard mathematical problem which is discrete logarithm problem. This system is more secure and efficient since there is no exchange of keys during the protocols of encryption and decryption. Thus, this work tried to use this fact to propose a secure asymmetric image encryption scheme. In this scheme the sender and receiver agree on public parameters, then the scheme begin deal with image using Massey Omura scheme to encrypt it by the sender and then decrypted it by the receiver. The proposed scheme tested using peak signal to noise ratio, and unified average changing intensity to prove that it is fast and has high security.
Security and imperceptibility improving of image steganography using pixel al...IJECEIAES
Information security is one of the main aspects of processes and methodologies in the technical age of information and communication. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography and cryptography. An effective digital image-steganographic method based on odd/even pixel allocation and random function to increase the security and imperceptibility has been improved. This lately developed outline has been verified for increasing the security and imperceptibility to determine the existent problems. Huffman coding has been used to modify secret data prior embedding stage; this modified equivalent secret data that prevent the secret data from attackers to increase the secret data capacities. The main objective of our scheme is to boost the peak-signal-to-noise-ratio (PSNR) of the stego cover and stop against any attack. The size of the secret data also increases. The results confirm good PSNR values in addition of these findings confirmed the proposed method eligibility.
Technical analysis of content placement algorithms for content delivery netwo...IJECEIAES
Content placement algorithm is an integral part of the cloud-based content de-livery network. They are responsible for selecting a precise content to be re-posited over the surrogate servers distributed over a geographical region. Although various works are being already carried out in this sector, there are loopholes connected to most of the work, which doesn't have much disclosure. It is already known that quality of service, quality of experience, and the cost is always an essential objective targeting to be improved in existing work. Still, there are various other aspects and underlying reasons which are equally important from the design aspect. Therefore, this paper contributes towards reviewing the existing approaches of content placement algorithm over cloud-based content delivery network targeting to explore open-end re-search issues.
Comparative study between metaheuristic algorithms for internet of things wir...IJECEIAES
Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Asymmetric image encryption scheme based on Massey Omura scheme IJECEIAES
Asymmetric image encryption schemes have shown high resistance against modern cryptanalysis. Massey Omura scheme is one of the popular asymmetric key cryptosystems based on the hard mathematical problem which is discrete logarithm problem. This system is more secure and efficient since there is no exchange of keys during the protocols of encryption and decryption. Thus, this work tried to use this fact to propose a secure asymmetric image encryption scheme. In this scheme the sender and receiver agree on public parameters, then the scheme begin deal with image using Massey Omura scheme to encrypt it by the sender and then decrypted it by the receiver. The proposed scheme tested using peak signal to noise ratio, and unified average changing intensity to prove that it is fast and has high security.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
A COMBINATION OF THE INTRUSION DETECTION SYSTEM AND THE OPEN-SOURCE FIREWALL ...IJCNCJournal
There are many security models for computer networks using a combination of Intrusion Detection System and Firewall proposed and deployed in practice. In this paper, we propose and implement a new model of the association between Intrusion Detection System and Firewall operations, which allows Intrusion Detection System to automatically update the firewall filtering rule table whenever it detects a weirdo intrusion. This helps protect the network from attacks from the Internet.
Intelligent GIS-Based Road Accident Analysis and Real-Time Monitoring Automat...CSCJournals
It has been a big concern for many people and government to reduce the amount of road accident specially in Malaysia since it could be a big threat to this country. Malaysian government has spent millions of money in order to reduce the number of accident occurrence through several modes of campaign. Unfortunately, from years to years the number keeps increasing. The lack of a comprehensive accident recording and analysis system in Malaysia can be effective in these kinds of problems. By making use of IRAS (Intelligent Road Accident System), the police would be control and manage whole accident events as a real-time monitoring system. This system exploits WiMAX and GPRS communications to connect to the server for transfer the specific data to the data center. This system can be used for a comprehensive intelligent GIS-based solution for accident analysis and management. The system is developed based on object and aspect oriented software design such as .NET technology.
Sierpinski carpet fractal monopole antenna for ultra-wideband applications IJECEIAES
Microstrip antenna is broadly used in the modern communication system due to its significant features such as light weight, inexpensive, low profile, and ease of integration with radio frequency devices. The fractal shape is applied in antenna geometry to obtain the ultra-wideband antennas. In this paper, the sierpinski carpet fractal monopole antenna (SCFMA) is developed for base case, first iteration and second iteration to obtain the wideband based on its space filling and self-similar characteristics. The dimension of the monopole patch size is optimized to minimize the overall dimension of the fractal antenna. Moreover, the optimized planar structure is proposed using the microstrip line feed. The monopole antenna is mounted on the FR4 substrate with the thickness of 1.6 mm with loss tangent of 0.02 and relative permittivity of 4.4. The performance of this SCFMA is analyzed in terms of area, bandwidth, return loss, voltage standing wave ratio, radiation pattern and gain. The proposed fractal antenna achieves three different bandwidth ranges such as 2.6-4.0 GHz, 2.5-4.3 GHz and 2.4-4.4 GHz for base case, first and second iteration respectively. The proposed SCFMA is compared with existing fractal antennas to prove the efficiency of the SCFMA design. The area of the SCFMA is 25×20 푚푚 2 , which is less when compared to the existing fractal antennas.
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...IJECEIAES
In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Channel encoding system for transmitting image over wireless network IJECEIAES
Various encoding schemes have been introduced till date focusing on an effective image transmission scheme in presence of error-prone artifacts in wireless communication channel. Review of existing schemes of channel encoding systems infer that they are mostly inclined on compression scheme and less over problems of superior retention of signal retention as they lacks an essential consideration of network states. Therefore, the proposed manuscript introduces a cost effective lossless encoding scheme which ensures resilient transmission of different forms of images. Adopting an analytical research methodology, the modeling has been carried out to ensure that a novel series of encoding operation be performed over an image followed by an effective indexing mechanism. The study outcome confirms that proposed system outshines existing encoding schemes in every respect.
CL-SA-OFDM: cross-layer and smart antenna based OFDM system performance enha...IJECEIAES
The growing usage of wireless services is lacking in providing high-speed data communication in recent times. Hence, many of the modulation techniques are evolved to attain these communication needs. The recent researches have widely considered OFDM technology as the prominent modulation mechanism to fulfill the futuristic needs of wireless communication. The OFDM can bring effective usage of resources, bandwidth, and system performance enhancement in collaboration with the smart antenna and resource allocation mechanism (adaptive). However, the usage of adaptive beamforming with the OFDM leads to complication in the design of medium access layer and which causes a problem in adaptive resource allocation mechanism (ARAM). Hence, the proposed manuscript intends to design an OFDM system by considering different switched beam smart antenna (SBSA) along with the cross-layer adaptive resource allocation (CLARA) and hybrid adaptive array (HAA). In this, various smart antenna mechanism are considered to analyze the quality of service (QoS) and complexity reduction in the OFDM system. In this paper, various SA schemes are used as per the quality of service (QoS) requirement of the different users. The performance analysis is conducted by considering data traffic reduction, bit-rate reduction, and average delay.
Lifetime centric load balancing mechanism in wireless sensor network based Io...IJECEIAES
Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.
Scene Text Detection of Curved Text Using Gradiant Vector Flow MethodIJTET Journal
Abstract--Text detection and recognition is a hot topic for researchers in the field of image processing and multimedia. Content based Image Retrieval (CBIR) community fills the semantic gap between low-level and high-level features. For text detection and extraction that achieve reasonable accuracy for multi-oriented text and natural scene text (camera images), several methods have been developed. In general most of the methods use classifier and large number of training samples to improve the accuracy in text detection. In general, connected components are used to tackle the multi-orientation problem. The connected component analysis based features with classifier training, work well for achieving better accuracy when the images are highly contrast. However, when the same methods used directly for text detection in video it results in disconnections, loss of shapes etc, because of low contrast and complex background. For such cases, deciding geometrical features of the components and classifier is not that easy. To overcome from this problem the proposed research uses Gradiant Vector Flow and Grouping based Method for Arbitrarily Oriented Scene text Detection method. The GVF of edge pixels in the Sobel edge map of the input frame is explored to identify the dominant edge pixels which represent text components. The method extracts dominant pixel’s edge components corresponding to the Sobel edge map, which is called Text Candidates (TC) of the text lines. Experimental results on different datasets including text data that is oriented arbitrary, non-horizontal text data also horizontal text data, Hua’s data and ICDAR-03 data (Camera images) show that the proposed method outperforms existing methods.
Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels.
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
A COMBINATION OF THE INTRUSION DETECTION SYSTEM AND THE OPEN-SOURCE FIREWALL ...IJCNCJournal
There are many security models for computer networks using a combination of Intrusion Detection System and Firewall proposed and deployed in practice. In this paper, we propose and implement a new model of the association between Intrusion Detection System and Firewall operations, which allows Intrusion Detection System to automatically update the firewall filtering rule table whenever it detects a weirdo intrusion. This helps protect the network from attacks from the Internet.
Intelligent GIS-Based Road Accident Analysis and Real-Time Monitoring Automat...CSCJournals
It has been a big concern for many people and government to reduce the amount of road accident specially in Malaysia since it could be a big threat to this country. Malaysian government has spent millions of money in order to reduce the number of accident occurrence through several modes of campaign. Unfortunately, from years to years the number keeps increasing. The lack of a comprehensive accident recording and analysis system in Malaysia can be effective in these kinds of problems. By making use of IRAS (Intelligent Road Accident System), the police would be control and manage whole accident events as a real-time monitoring system. This system exploits WiMAX and GPRS communications to connect to the server for transfer the specific data to the data center. This system can be used for a comprehensive intelligent GIS-based solution for accident analysis and management. The system is developed based on object and aspect oriented software design such as .NET technology.
Sierpinski carpet fractal monopole antenna for ultra-wideband applications IJECEIAES
Microstrip antenna is broadly used in the modern communication system due to its significant features such as light weight, inexpensive, low profile, and ease of integration with radio frequency devices. The fractal shape is applied in antenna geometry to obtain the ultra-wideband antennas. In this paper, the sierpinski carpet fractal monopole antenna (SCFMA) is developed for base case, first iteration and second iteration to obtain the wideband based on its space filling and self-similar characteristics. The dimension of the monopole patch size is optimized to minimize the overall dimension of the fractal antenna. Moreover, the optimized planar structure is proposed using the microstrip line feed. The monopole antenna is mounted on the FR4 substrate with the thickness of 1.6 mm with loss tangent of 0.02 and relative permittivity of 4.4. The performance of this SCFMA is analyzed in terms of area, bandwidth, return loss, voltage standing wave ratio, radiation pattern and gain. The proposed fractal antenna achieves three different bandwidth ranges such as 2.6-4.0 GHz, 2.5-4.3 GHz and 2.4-4.4 GHz for base case, first and second iteration respectively. The proposed SCFMA is compared with existing fractal antennas to prove the efficiency of the SCFMA design. The area of the SCFMA is 25×20 푚푚 2 , which is less when compared to the existing fractal antennas.
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...IJECEIAES
In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Channel encoding system for transmitting image over wireless network IJECEIAES
Various encoding schemes have been introduced till date focusing on an effective image transmission scheme in presence of error-prone artifacts in wireless communication channel. Review of existing schemes of channel encoding systems infer that they are mostly inclined on compression scheme and less over problems of superior retention of signal retention as they lacks an essential consideration of network states. Therefore, the proposed manuscript introduces a cost effective lossless encoding scheme which ensures resilient transmission of different forms of images. Adopting an analytical research methodology, the modeling has been carried out to ensure that a novel series of encoding operation be performed over an image followed by an effective indexing mechanism. The study outcome confirms that proposed system outshines existing encoding schemes in every respect.
CL-SA-OFDM: cross-layer and smart antenna based OFDM system performance enha...IJECEIAES
The growing usage of wireless services is lacking in providing high-speed data communication in recent times. Hence, many of the modulation techniques are evolved to attain these communication needs. The recent researches have widely considered OFDM technology as the prominent modulation mechanism to fulfill the futuristic needs of wireless communication. The OFDM can bring effective usage of resources, bandwidth, and system performance enhancement in collaboration with the smart antenna and resource allocation mechanism (adaptive). However, the usage of adaptive beamforming with the OFDM leads to complication in the design of medium access layer and which causes a problem in adaptive resource allocation mechanism (ARAM). Hence, the proposed manuscript intends to design an OFDM system by considering different switched beam smart antenna (SBSA) along with the cross-layer adaptive resource allocation (CLARA) and hybrid adaptive array (HAA). In this, various smart antenna mechanism are considered to analyze the quality of service (QoS) and complexity reduction in the OFDM system. In this paper, various SA schemes are used as per the quality of service (QoS) requirement of the different users. The performance analysis is conducted by considering data traffic reduction, bit-rate reduction, and average delay.
Lifetime centric load balancing mechanism in wireless sensor network based Io...IJECEIAES
Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.
Scene Text Detection of Curved Text Using Gradiant Vector Flow MethodIJTET Journal
Abstract--Text detection and recognition is a hot topic for researchers in the field of image processing and multimedia. Content based Image Retrieval (CBIR) community fills the semantic gap between low-level and high-level features. For text detection and extraction that achieve reasonable accuracy for multi-oriented text and natural scene text (camera images), several methods have been developed. In general most of the methods use classifier and large number of training samples to improve the accuracy in text detection. In general, connected components are used to tackle the multi-orientation problem. The connected component analysis based features with classifier training, work well for achieving better accuracy when the images are highly contrast. However, when the same methods used directly for text detection in video it results in disconnections, loss of shapes etc, because of low contrast and complex background. For such cases, deciding geometrical features of the components and classifier is not that easy. To overcome from this problem the proposed research uses Gradiant Vector Flow and Grouping based Method for Arbitrarily Oriented Scene text Detection method. The GVF of edge pixels in the Sobel edge map of the input frame is explored to identify the dominant edge pixels which represent text components. The method extracts dominant pixel’s edge components corresponding to the Sobel edge map, which is called Text Candidates (TC) of the text lines. Experimental results on different datasets including text data that is oriented arbitrary, non-horizontal text data also horizontal text data, Hua’s data and ICDAR-03 data (Camera images) show that the proposed method outperforms existing methods.
Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels.
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
Accident vehicle types classification: a comparative study between different...nooriasukmaningtyas
Classifying and finding type of individual vehicles within an accident image are considered difficult problems. This research concentrates on accurately classifying and recognizing vehicle accidents in question. The aim to provide a comparative analysis of vehicle accidents. A number of network topologies are tested to arrive at convincing results and a variety of matrices are used in the evaluation process to identify the best networks. The best two networks are used with faster recurrent convolution neural network (Faster RCNN) and you only look once (YOLO) to determine which network will identifiably detect the location and type of the vehicle. In addition, two datasets are used in this research. In consequence, experiment results show that MobileNetV2 and ResNet50 have accomplished higher accuracy compared to the rest of the models, with 89.11% and 88.45% for the GAI dataset as well as 88.72% and 89.69% for KAI dataset, respectively. The findings reveal that the ResNet50 base network for YOLO achieved higher accuracy than MobileNetV2 for YOLO, ResNet50 for Faster RCNN with 83%, 81%, and 79% for GAI dataset and 79%, 78% and 74% for KAI dataset.
Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition.
Application of improved you only look once model in road traffic monitoring ...IJECEIAES
The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.
Malaysian car plate localization using region-based convolutional neural networkjournalBEEI
Automatic car plate localization and recognition system is a system that identifies the car plate location and recognizes the characters on the car plate input images. Within the automated system, the car plate localization stage is the first stage and is the most crucial stage as the success rate of the whole system depends heavily on it. In this paper, a Malaysian car plate localization system using Region-based Convolutional Neural Network (R-CNN) is proposed. Using transfer learning on the AlexNet CNN, the localization was greatly improved achieving best precision and recall rate of 95.19% and 97.84% respectively. Besides, the proposed R-CNN was able to localize car plates in complex scenarios such as under occlusion.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
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Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
A hierarchical RCNN for vehicle and vehicle license plate detection and recognition
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 1, February 2022, pp. 731~737
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i1.pp731-737 731
Journal homepage: http://ijece.iaescore.com
A hierarchical RCNN for vehicle and vehicle license plate
detection and recognition
Chunling Tu1
, Shengzhi Du2
1
Department of Computer Systems Engineering, Tshwane University of Technology, Pretoria, South Africa
2
Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa
Article Info ABSTRACT
Article history:
Received Aug 30, 2020
Revised Jun 17, 2021
Accepted Jun 30, 2021
Vehicle and vehicle license detection obtained incredible achievements
during recent years that are also popularly used in real traffic scenarios, such
as intelligent traffic monitoring systems, auto parking systems, and vehicle
services. Computer vision attracted much attention in vehicle and vehicle
license detection, benefit from image processing and machine learning
technologies. However, the existing methods still have some issues with
vehicle and vehicle license plate recognition, especially in a complex
environment. In this paper, we propose a multivehicle detection and license
plate recognition system based on a hierarchical region convolutional neural
network (RCNN). Firstly, a higher level of RCNN is employed to extract
vehicles from the original images or video frames. Secondly, the regions of
the detected vehicles are input to a lower level (smaller) RCNN to detect the
license plate. Thirdly, the detected license plate is split into single numbers.
Finally, the individual numbers are recognized by an even smaller RCNN.
The experiments on the real traffic database validated the proposed method.
Compared with the commonly used all-in-one deep learning structure, the
proposed hierarchical method deals with the license plate recognition task in
multiple levels for sub-tasks, which enables the modification of network size
and structure according to the complexity of sub-tasks. Therefore, the
computation load is reduced.
Keywords:
Deep learning
Region convolution neural
network
Vehicle detection
Vehicle license recognition
This is an open access article under the CC BY-SA license.
Corresponding Author:
Chunling Tu
Department of Computer Systems Engineering, Tshwane University of Technology
2 Aubrey Matlakala St, Soshanguve, Pretoria, South Africa
Email: duc@tut.ac.za
1. INTRODUCTION
Vehicle and vehicle license plate tracking and recognition management systems play an important
role in an intelligent transportation system, it is commonly used in automatic driving, vehicle theft
prevention, access control security, high-efficiency roadway services, and so on. Most of these systems are
based on traffic image or video analysis, where computer vision techniques were commonly employed. A
huge recent development was found with the employment of machine learning and deep learning techniques,
which demonstrate higher classification accuracy and robustness. Both deep learning and computer vision
methods [1]-[15] were developed in various vehicle-relevant applications, such as vehicle detection, vehicle
classification, vehicle plate recognition, and road condition monitoring [16]. Comparing with computer
vision methods, the newly developed deep learning techniques have some advantages on the generalization
capacity and robustness to uncertainties, noise, and occlusion in images, in the cost of higher computation
load and demand on sample set size. Several methods were proposed to detect vehicle and vehicle license
plates based on these newly developed techniques, such as the dirty number plate detection system based on
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you only look once (YOLO) [1], the license number plate recognition system based on convolutional neural
network (CNN), and recurrent neural network (RNN) [2], the technique to improve the car plate recognition
rate based on support vector machines (SVM) [3], the vehicle detection system based on region convolutional
neural network (RCNN) [4], the vehicle detection and counting system using the convolutional regression
neural network (CRNN) [5].
However, there are some challenges to deep learning-based techniques in vehicle and vehicle license
plate detection and recognition. Firstly, fake objects from a complex environment around the vehicle reduce
the accuracy of detection by confusing the deep neural networks, buildings, trees, pedestrians, and other
objects surrounded the vehicles. Secondly, the weather and illumination blur the vehicles and license plate,
strong sunlight, foggy, rainy, and shadow. Thirdly, the human factor, speed of the vehicle, driver behaviors.
All of the above make the difficult to achieve accurate and efficient detection.
In this paper, we proposed a hierarchical RCNN system for vehicle detection and license plate
recognition under complex traffic backgrounds. The system contains four steps: i) a high-level RCNN to
detect vehicles from traffic images/video, ii) a lower level RCNN is used to locate the license plate from the
detected vehicle regions, iii) a smaller RCNN is trained to detect individual letter from the detected license
plate, iv) finally an RCNN classified is employed to recognize the individual letter. To reduce computation,
the training data were cropped into smaller blocks. The main innovation of the proposed method is
manifested in the idea of hierarchical structure with multiple-level networks for sub-tasks. Differentiating
from the common all-in-one deep learning structure, the proposed method considers the license plate
recognition as multiple sub-tasks, which enables the optimization of network depth and structure for each
sub-task. Each RCNN is designed with careful consideration of the complexity of the problem to be solved in
the corresponding level, so an RCNN of appropriate depth is chosen for each step. The experiment results
show the proposed system has higher speed and accuracy.
The rest of the paper is organized as follows. Section 2 introduces the relative work. Section 3
explains the proposed method. Section 4 demonstrates the experiments and analysis. Section 5 concludes the
paper and indicates future work.
2. RESEARCH METHOD
2.1. Vehicle and license plate detection
Vehicle detection has been popularly studied in literature based on computer vision, which is the
fundamental stage for an automatic driver system. There are many algorithms developed. According to the
literature, vehicle detection is categorized into moving vehicle detection and static vehicle detection. In this
paper, we focus on only moving vehicle detection. The features, background subtractions, frame difference,
optical flow, machine learning, and combined methods are used to detect moving detection. A method was
proposed [6] using optical flow to detect a moving vehicle. The color and texture features were used to
reduce the effects from the complex background, and the fuzzy background subtraction was developed [7] for
moving vehicle detection. [8] improved the frame difference method for moving vehicle detection using
image contrast and morphological filter. The optical flow was combined with k-nearest neighbor (KNN) to
classify the different kinds of moving vehicles [12]. An anti-tracking system [17] was proposed to detect the
vehicle based on Haar features and modified the adaptive boosting algorithm.
The vehicle license plate, as an ID, can uniquely identify vehicles. So the automatic license plate
detection and recognition is one of the important tasks in an intelligent driving system. Various methods have
been developed, a method [10] was developed to detect vehicle plates using salient features, with a success
rate of 93.1% reported. The rear vehicle lights were used to detect the range of license plate and then identify
the license plated using the histogram algorithm [11], which was only validated by vehicles from five
countries. The average success rate is 90.4%. An automatic license plate recognition [12] was proposed using
updated YOLO, with a license plate recognition accuracy of 78%. However, the accurate rate still is an issue
for real applications. And most of these methods have high computational requirements due to the system's
need to search the vehicles and vehicle license plate from large and high-resolution images or videos that
were captured from the road.
2.2. Region convolution neural network (RCNN)
Deep learning is one of the algorithms of the machine learning method that was developed based on
the neural network [18]. Various deep learning networks structure have been depleted, such as AlexNet [19],
Over feat [20], GoogLeNet [21], ResNet [22]. RCNN is the most popular model of deep learning because of
the significant success in image processing. The RCNN is made up of neurons that have tunable weights and
bias. The input data can be images, the layers of RCNN have 3 dimensions (width, height, and depth).
RCNNs are widely used in vehicle and vehicle number plate detection. For instance, Brody [13] detected the
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lanes and vehicles using CNN. The front collision was predicated using CNN with adaptive region-of-interest
[14]. Several recent studies [15], [23] developed one system for license plate recognition based on RCNN. In
this paper, a hierarchical RCNN will be employed to obtain high accuracy and fast speed for vehicle license
plate detection.
3. PROPOSED METHOD
Deep learning techniques are commonly a hunger for computation resources. Compared to CNN,
the RCNN gets the advantage of higher computation efficiency. The traditional all-in-one method did not
leave much space for modifications according to the real-world scenario, such as the task complexity,
features to be considered, image qualities, and the relationship among sub-tasks. This paper is intending to
propose a hierarchical structure to separate the all-in-one RCNN into multiple levels, with each layer focuses
on a single sub-task. Therefore, the network in each layer will be developed according to the real demand of
the sub-task only. This will lead to the lower complexity of the total task, which results in a smaller network
size, lower computation load, and higher total accuracy.
Using the vehicle plate recognition applications, this paper employs RCNN for 3 level sub-tasks,
vehicle detection, plate detection, and character level detection and recognition. These 3 tasks have different
complexity on the background and object features. The main idea is to consider RCNNs with different
complexity levels for these tasks. Figure 1 depicts the proposed hierarchical structure of the solution for
vehicle-plate-character detection and identification, where multiple RCNNs with feasible complexity are
considered for different tasks. The hierarchical structure is not only for RCNN, various pre-trained deep
neural networks can be considered to replace the RCNN. In this paper, the RCNN is considered just as an
example to show the idea.
The vehicle level RCNN is the most complex one because the vehicles are commonly found in busy
traffic contexts when smart traffic monitoring systems are concerned. Detecting vehicles with acceptable
accuracy is critical for total system performance. In this level, a 15 layer RCNN is employed, as shown in
Figure 2, where the RCNN has 3 folds of convolutional-pooling layers, which are pre-trained.
After the vehicle is detected by the vehicle level RCNN, we can get the region of the vehicle. The
plate level RCNN only focuses on this region, instead of the whole image. This greatly reduces the
computation load. Meanwhile, this strategy can avoid mis-hit in plate detection, because other potential
candidates in the background are excluded.
Because the problem to detect a license plate from a vehicle is relevantly simpler than detecting
vehicles from the background, this plate level RCNN can be smaller (having fewer layers). Figure 3 shows
the 12-layer structure of this RCNN, where only 2 folds of convolution-ReLU-Pooling layers are contained.
Both the input and the final layers are the same as the vehicle level RCNN.
Figure 1. The hierarchical structure of the proposed RCNN vehicle identification system
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Figure 2. The structure of the vehicle level RCNN
Figure 3. The structure of RCNN in the character and the plate levels
The features used to detect and recognize characters from the plate image are even less. So
theoretically the character level RCNN can be smaller than the plate level. However, the plate level RCNN is
already very shallow (only 2 convolution layers), it is not feasible to reduce the layers further to avoid the
low fitting capacity of the RCNN. The character level RCNN takes the same structure as the plate level one.
It should be noted that if the vehicle level deep network has more convolution layers, one can expect the
character level RCNN can be smaller than the plate level.
4. EXPERIMENTS AND DISCUSSION
To validate the proposed method, experiments are designed based on the public accessible Canadian
Institute for Advanced Research, 10 classes (CIFAR-10) database [24].
4.1. Experiment environment
All the experiments in this paper are completed on a Lenovo Thinkpad X1 laptop, with 16 GB
RAM, an Intel(R) Core(TM) i7-8550 CPU at 1 Ghz. The operating system is windows 10 x64. The software
is Matlab R2019a with the following toolboxes: computer vision, image processing, deep learning, and
statistics and machine learning. The RCNNs are modified and trained based on the CIFAR-10 network [25].
4.2. Reduce the layers of RCNN
Associating the complexity of RCNN to the task is the main advantage of the proposed strategy.
This experiment aims to demonstrate the feasibility of the deduction of the CIFAR-10 Network. The original
CIFAR-10 Network (15-layer RCNN) has 1 image input layer, 3 folds of Convolution-ReLU-Pooling layers
as the middle layer, and the final layers, as shown in Figure 2. The first experiment is to remove 1 fold of the
ConvolutionReLU-Pooling layers from the middle layers. The modified network has the structure as shown
in Figure 3.
After training using the CIFAR-10 dataset, the weight of the first convolution layer for the original
network (3 convolution layers) and the modified network 12-layer RCNN (2 convolution layers) are shown
in Figure 4 and Figure 5 respectively. From the weights, one finds that both of the networks have captured
the basic features of the images in the dataset. Therefore, the modification of the structure does not
significantly damage the feature extraction capacity.
The next experiment depicts the further reduction of the middle layers to a single convolution layer
(9-layer RCNN). The weights of the single convolution network shown in Figure 6 shows the strong random
distribution, which means the network failed to capture the image features. This is because the removal of
two convolution layers has damaged the learning capacity.
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Figure 4. The weight of the first convolution layer in
the 15-layer RCNN
Figure 5. The weight of the first convolution layer in
the 12-layer RCNN
Figure 6. The weight of the first convolution layer in the 9-layer RCNN
The accuracy during the training iterations in Figure 7 confirmed this point, where the 12-layer and
15-layer RCNNs have similar accuracy. This means the reduction of the layers does not significantly affect
the learning capacity. However, the accuracy of the 9-layer RCNN was significantly lowered. Considering
that the dataset of CIFAR-10 has 10 classes, the accuracy of 10% obtained by the 9-layer RCNN is just a
random guess.
Figure 7. The mini-batch accuracy during the training of RCNN
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It should be noted that, due to the advantage of the transfer learning strategy, the RCNNs do not
need to reach 100% accuracy when training the middle layers, as long as the middle layers can capture the
image features. Table. 1 shows the accuracy of the 3 RCNNs after the training of middle layers using the 10
classes. When the computation load is concerned, the modified RCNNs (12-layer and 9-layer networks) have
higher efficiency. Considering both accuracy and time efficiency, one finds the 12-layer RCNN can be
considered as the lower-level classifiers in the hierarchical structure in Figure 1.
Table 1. Comparison of accuracy and computation of the RCNNs
RCNN 15-layer 12-layer 9-layer
Training Time (min) 49 44 33
Test Set Accuracy (%) 56.7 52.5 10
4.3. Vehicle license plate detection and recognition
This experiment focuses on the specific application, the vehicle license plate detection and
recognition. Table 2 shows the accuracy of each RCNN in the proposed structure. From Table 2, one finds
that the RCNNs in vehicle and plate levels have similar accuracy, although the classification capacity was
lowered when a convolution layer was removed. This is because the problem complexity for plate detection
is lower than the vehicle detection, therefore, a smaller RCNN also can get similar accuracy. This also means
there are some spaces to modify the all-in-one RCNN without significant damage to the accuracy. The
character level accuracy is lower than the other two levels although the characters have fewer features than
the plate and vehicle. This is because the classification problem in the character level becomes a multiple-
class (36 classes) problem. The final layers of the character level should be improved, and more training sets
should be considered. The experiments demonstrated the reduction of RCNNs and the performance of the
total system, which validated the proposed strategy.
Table 2. Accuracy of the proposed method for detection and recognition
RCNNs Vehicle Level Plate Level Character Level
Accuracy(%) 98.5 97 85
5. CONCLUSION
This paper proposed a hierarchical strategy for vehicle and vehicle license plate detection and
identification based on RCNN. The complexity of the RCNN was associated with the tasks. In this way,
multiple complex level RCNNs can be employed in the same system. As an example, a sample vehicle
detection system was developed for smart traffic monitoring, thereafter the license plate recognition RCNN
was considered for vehicle identification.
The vehicle level RCNN with the highest complexity was employed to detect the vehicle from the
background. In this RCNN, the task can be considered as a two-class (vehicle and background) classification
problem. The detected vehicle region (a portion of the original image) was then inputted to the license plate
level RCNN, where the license plate was detected. This is also a two-class classification problem (license
plate and vehicle body as background). In this way, the computation load is greatly reduced. Furthermore, the
disturbances from outside of the vehicle were excluded, which improved the success rate of the plate level
RCNN. Finally, the detected license plate became the input of the character level RCNN, where the
individual characters were detected and classified. This RCNN solved a multiple class classification problem,
where the numbers (‘0’ to ‘9’) and letters(‘a’ to ‘z’) are the classification targets. The future work includes:
(1) separate the total task into more levels and design the network in each level according to the specific
features in the corresponding sub-task; (2) improve the final layers for the character level RCNN to improve
the accuracy of plate recognition.
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BIOGRAPHIES OF AUTHORS
Chunling Tu received a bachelor degree in computer science from Tianjin
University of Technology and Education, China in 2002; MTech and MSc degrees in
Electrical Engineering from Tshwane University of Technology (South Africa) and ESIEE
Paris University (France) in 2010; DTech and Ph.D. degrees of Electrical Engineering from
the Tshwane University of Technology and University Paris East, France in 2015. Her
research interests include image processing, AI, industrial control, machine learning, deep
learning, and pattern recognition. She can be contacted at email: duc@tut.ac.za.
Shengzhi Du received an M.S. degree in control theory and control engineering
from Tianjin Poly Technology University, Tianjin, China, in 2001 and the Ph.D. degree in
control theory and control engineering from Nankai University, Tianjin, China, in 2005. He
is currently a professor in the French South Africa Institute of Technology (F’SATI),
Tshwane University of Technology, South Africa. His research interests include computer
vision, AI, pattern recognition, and human in loop systems. He can be contacted at email:
dus@tut.ac.za.