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Showing papers in "TELKOMNIKA Telecommunication Computing Electronics and Control in 2023"


Journal ArticleDOI
TL;DR: In this article , the hypertext transfer protocol (HTTP) intrusion detection system (IDS) that runs on the server where our website is running is proposed, which works by detecting rampant intrusions that attack servers or websites and will notify the system admin using the Keris Telegram chatbot or an alternative Keris mobile application with firebase cloud messaging (FCM) technology.
Abstract: Behind the rapid development of the internet in today’s era, various types of crime are also targeting vital players in the internet industry. With many online crime types rampant, an antidote is also needed to suppress internet crime. Therefore, the researcher proposes a solution in the form of a Keris, namely the hypertext transfer protocol (HTTP) intrusion detection system (IDS) that runs on the server where our website is running. Keris works by detecting rampant intrusions that attack servers or websites. When an intrusion is detected, Keris will notify the system admin using the Keris Telegram chatbot or an alternative Keris mobile application with firebase cloud messaging (FCM) technology. The research was conducted by comparing the results of one-way delay (OWD) between Telegram Webhook and FCM with the help of the open web application security project (OWASP) zed attack proxy (ZAP) test tool. From the results of the tests, OWD against directory brute force attacks on Telegram Webhook for 0.72 seconds and on FCM for 0.44 seconds. In this case, FCM is more suitable for real-time notifications if we need a very responsive notification.

3 citations


Journal ArticleDOI
TL;DR: In this paper , a trapezoidal microstrip patch antenna built on the Rogers RT5880 laminate with a permittivity of 2.2 and tangent loss of 0.0009 was used to support the structure.
Abstract: Nowadays, millimeter-wave frequencies present a catchy solution to securing the colossal data rate needed for 5G communications. Accordingly, this research deals with the conception of a novel orthogonal 2×2 multiple input, multiple output (MIMO) antenna design operating in the millimeter wave spectrum with quite small dimensions of 11×6×0.8 mm 3 . The single antenna element consists of a trapezoidal microstrip patch antenna built on the Rogers RT5880 laminate with a permittivity of 2.2 and tangent loss of 0.0009. A trapezoidal-slot ground plane is used to support the structure. The antenna resonates at 28 GHz with a large bandwidth of 4 GHz from 26 to 30 GHz, a good gain of up to 5 dB, and a high radiation efficiency of 99%. A strong isolation is achieved that surpasses 26 dB. Besides, a high diversity performance is achieved where the envelope correlation coefficient (ECC) is lower than 0.001, the diversity gain (DG) is greater than 10 dB, and the channel capacity loss (CCL) is no longer than 0.4 bit/s/Hz. The achieved outcomes prove the robustness of the suggested MIMO antenna and qualify it to be a strong candidate for 5G wireless devices.

2 citations


Journal ArticleDOI
TL;DR: In this paper , the authors propose a new website,telkomnika.uad.ac.id, which can be found here: http://www.telkomsnika-uad-ac.ac.,
Abstract: http://telkomnika.uad.ac.id

2 citations


Journal ArticleDOI
TL;DR: In this paper , the authors describe the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also include the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean.
Abstract: The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean’s environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean.

1 citations


Journal ArticleDOI
TL;DR: In this article , the authors propose a solution to solve the problem of the problem: this article ] of "uniformity" and "uncertainty" of the solution.
Abstract: ,

1 citations


Journal ArticleDOI
TL;DR: In this article , the authors proposed a fault-tolerant computing model for the Internet of Things (IoT) based networks, which considers both linear and probabilistic methods of computing the fault tolerance considering many complex networking topologies used in each layer of IoT networks.
Abstract: Many Internet of Things (IoT) - based networks are being built to develop applications spanning multiple domains. Many small to large devices connected in various ways increases the risk of IoT networks failing. Small devices in the devices layer frequently fail due to their small size and high usage. Intermittent failures of the IoT networks lead to catastrophes at times. The IoT systems must be designed to be fault-tolerant. Fault tolerance of IoT networks must be computable so that the same can be considered while designing IoT networks. However, the computation of fault tolerance of IoT networks is complex, especially when heterogeneous structures are used for building a specific IoT network. Fault tree-based models are not suitable for computing fault-tolerance of complex models, which requires probability assessment. Hybrid fault tolerance computing models have been presented in this paper that consider both linear and probabilistic methods of computing the fault tolerance considering many complex networking topologies used in each layer of IoT networks. The fault-tolerance computing models are formal methods that can be used to compute the fault tolerance of any IoT network built with any internal processing. The accuracy of fault tolerance computing is 12.9% higher than other methods.

1 citations


Journal ArticleDOI
TL;DR: For Bangla news articles classification, term frequencyinverse document frequency (TF-IDF) weighting and count vectorizer have been used as a feature extraction process, and two common classifiers named support vector machine (SVM) and logistic regression (LR) employed for classifying the documents as mentioned in this paper .
Abstract: In the information age, Bangla news articles on the internet are fast-growing. For organizing, every news site has a particular structure and categorization. News article classification is a method to determine a document’s classification based on various predefined categories. This research discusses the classification of Bangla news articles on the online platform and tries to make constructive comparison using several classification algorithms. For Bangla news articles classification, term frequencyinverse document frequency (TF-IDF) weighting and count vectorizer have been used as a feature extraction process, and two common classifiers named support vector machine (SVM) and logistic regression (LR) employed for classifying the documents. It is clear that the accuracy of the experimental results by applying SVM is 84.0% and LR is 81.0% for twelve categories of news articles. In this research work, when we have made comparison two renowned classification algorithms applied on the Bangla news articles, LR was outperformed by SVM.

1 citations


Journal ArticleDOI
TL;DR: In this paper , a survey for researchers who are interested in exploiting the dynamic tunneling technique to optimize software-defined wide area network (SD-WAN) is presented, which can be summarized into four categories as following: exploring VLAN in SDN, management of multi VLANs in SDNs, recovery link failure of SDNs; and development of SDN by using VLAN.
Abstract: Software-defined network (SDN) is one of the most predominant technologies for networking in the existing and next-generation networks. Therefore, this paper is conducted to introduce a survey for researchers who are interested in exploiting the dynamic tunneling technique to optimize software-defined wide area network (SD-WAN). The main purpose of this survey is not only to investigate the related works of dynamic tunneling with SD-WAN but also to classify this related work according to the aim of each research into the practicable categories and present the most dominated employments for tunneling with SD-WAN, specifically virtual local area network (VLAN). The performed classification accompany dynamic tunneling in SDN can be summarized into four categories as following: exploring VLAN in SDN; management of multi VLAN in SDN; recover link failure of SDN; and development of SDN by using VLAN. Finally, the intensive study of the literature in this paper discovers that the dominant path of research falls in the class that covers SDN’s link failure. This class takes full advantage of SD-WANs due to offering more robust networking and restoring most communication failures. In the event of a fault, the controller could respond and recover quickly by switching to a pre-computed backup route.

1 citations


Journal ArticleDOI
TL;DR: In this article , two bandpass filters in waveguide technology having rectangular symmetrical discontinuities with a half-radius r , designed and operating respectively in the X-Band (9 - 11.5) GHz and C-band (3.5 - 5.5 GHz) GHz were presented.
Abstract: We propose in this paper, two bandpass filters in waveguide technology having rectangular symmetrical discontinuities with a half-radius r , designed and operating respectively in the X-Band (9 - 11.5) GHz and C-Band (3.5 - 5.5) GHz. These filters consists of eight irises placed symmetrically respectively on standard rectangular waveguides WR90 and WR229 in which resonant irises are inserted. These irises are used to couple the sections very strongly in this filter, which allows the bandwidth to be increased and the matching to be controlled. The comparison between the numerical and electromagnetic results, which we obtained for the filters, constitutes a means of validation of computer simulation technology (CST) environment and Mician for the design of the other circuit elements in the various frequency bands. We observed excellent consistency between the simulation curves and those of the measurements. The results obtained are promising and pave the way for the use of these structures in the fields of telecommunications.

1 citations


Journal ArticleDOI
TL;DR: In this paper , a line-to-ground (SLG) fault location algorithm based on fundamental frequency measured using the differential equation method is proposed to estimate the fault distance successfully with an acceptable fault location error.
Abstract: About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.

1 citations


Journal ArticleDOI
TL;DR: In this paper , the authors investigated the impact of feature selection meta-heuristic approaches on emotion recognition from speech and obtained maximum recognition accuracy of 89.64% using EO algorithm and 92.71% using CS algorithm.
Abstract: Human-computer interactions benefit greatly from emotion recognition from speech. To promote a contact-free environment in this coronavirus disease 2019 (COVID'19) pandemic situation, most digitally based systems used speech-based devices. Consequently, this emotion detection from speech has many beneficial applications for pathology. The vast majority of speech emotion recognition (SER) systems are designed based on machine learning or deep learning models. Therefore, need greater computing power and requirements. This issue was addressed by developing traditional algorithms for feature selection. Recent research has shown that nature-inspired or evolutionary algorithms such as equilibrium optimization (EO) and cuckoo search (CS) based meta-heuristic approaches are superior to the traditional feature selection (FS) models in terms of recognition performance. The purpose of this study is to investigate the impact of feature selection meta-heuristic approaches on emotion recognition from speech. To achieve this, we selected the rayerson audio-visual database of emotional speech and song (RAVDESS) database and obtained maximum recognition accuracy of 89.64% using the EO algorithm and 92.71% using the CS algorithm. For this final step, we plotted the associated precision and F1 score for each of the emotional classes. [ FROM AUTHOR]

Journal ArticleDOI
TL;DR: Siamese neural network (SINN) is an image processing model that compares the scores of two patterns as mentioned in this paper , which can build an image model without requiring thousands of images for training.
Abstract: Siamese neural network (SINN) is an image processing model that compares the scores of two patterns. The SINN algorithm is a combination of the use of the double convolutional neural network (CNN) algorithm. By combined SINN with a one-shot learning algorithm, we can build an image model without requiring thousands of images for training. The test results from the SINN algorithm and one-shot learning show that this process was successful in matching the two data but was unable to produce labels from the data being tested. Because of this, the researcher decided to continue the implementation process using the CNN algorithm combined with single shot detection (SSD). By using a dataset of 5000, the recognition and translation of the Toba Batak script was successful. The percentage of average accuracy results from CNN and SSD in recognizing Toba Batak characters is 84.08% for single characters and 74.13% for mixed characters. While the percentage of average accuracy results for testing the breadth first search algorithm is 75.725%.

Journal ArticleDOI
TL;DR: In this article , an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models for the multi-class classification and classification between MCIc and MCInc.
Abstract: Alzheimer’s disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer’s disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.

Journal ArticleDOI
TL;DR: In this paper , the authors evaluated the safety measures adopted by Ho Technical University (HTU) students in the use of power extension cords in their halls and hostels, along with their safety considerations for properly selecting these cords to avert fire outbreaks or prevent them from becoming a potential fire hazard.
Abstract: This paper presents the evaluation of the safety measures adopted by Ho Technical University (HTU) students in the use of power extension cords in their halls and hostels, along with their safety considerations for properly selecting these cords to avert fire outbreaks or prevent them from becoming a potential fire hazard. Whenever extension cords are utilized inappropriately it can lead to fire or electric shock perils. The assessment of the awareness level of safety practices is yet to be rigorously pursued as an agenda towards extension cord usage in institutions and agencies perceived to be high energy consumers where fire outbreaks occur frequently. A quantitative research approach was adopted, using a questionnaire for data collection. The findings revealed that about 52% of the respondents did not know the current and power ratings and the effects of overloading the extension cord. It was recommended that consumers purchase extension cords that have been endorsed by an autonomous testing laboratory, whereas the university should immediately organize a seminar to educate the staff and students about the use of the extension cord.

Journal ArticleDOI
TL;DR: In this paper , a poly methyl methacrylate (PMMA)-coated fiber bragg grating (FBG) was used as a temperature sensor with a sensitivity of 13.73 pm/℃.
Abstract: Fiber Bragg grating (FBG) with silica material has limitations in measuring mechanical quantities such as strain and temperature, this happens because silica fibers are easy to break at higher transverse or axial strains. This deficiency can be overcome in several ways, one of which is by coating the silica FBG with a coating material made of metal or polymer. In this research, the FBG sensor has been designed by poly methyl methacrylate (PMMA)-coated FBG and silica. The finite element method (FEM) is used to analyze the electric field distribution on the surface of PMMA coated FBG with a coating thickness of 20 µm. Furthermore, the sensitivity of each coated FBG as a temperature sensor was measured in the range of 25 ℃ to 85 ℃ using coupled mode theory (CMT). From the design and analysis of coated FBG, it was found that FBG coated with PMMA material had the highest sensitivity of 395.73pm/℃. However, the FBG sensor coated with silica material has a sensitivity of 13.73 pm/℃. the shift obtained is also linear along with the temperature of 25 ℃ to 85 ℃.

Journal ArticleDOI
TL;DR: In this article , the authors investigated the ideal configuration among two distant phosphor configurations in terms of color rendering index (CRI), color quality scale (CQS), lumen output (LO), as well as color homogeneity for multi-chip white LEDs (WLEDs).
Abstract: The distant phosphor configuration produces more illuminating beams than the two settings containing conformal or in-cup phosphor. When this configuration is used, though, it is difficult to manage the color standard of light-emitting diodes (LEDs). As a result, in past few years, numerous studies have focused on controlling the color standard of distant phosphor configurations. Until present, two distant phosphor configurations of single- and triple-film phosphor configurations, have been used to improve color standards. This research will investigate the ideal configuration among these configurations in terms of color rendering index (CRI), color quality scale (CQS), lumen output (LO), as well as color homogeneity for multi-chip white LEDs (WLEDs). WLEDs, operating at five different temperatures of color between 5600 K and 8500 K, are used to perform the studies. The results reveal that the three-sheet phosphor setting would be more desirable, with greater CRI, CQS, and lumen efficiency (LE) indexes. Furthermore, when using a triple-layer phosphor arrangement, color variation is reduced, resulting in a rise in color consistency. This conclusion is possibly verified by using Mie theory to analyze scattering properties in distant phosphor setup, making the study findings legitimate and important data to produce more advanced WLEDs.

Journal ArticleDOI
TL;DR: In this paper , the authors analyse the various approaches utilised for driving cycle construction in various locations of Malaysia under varied operational situations, and the results show that these cycles' applications have failed to provide high-quality results.
Abstract: Over the years, many models have been created to estimate pollution inventories and fuel usage. These models can be divided into two types: travel-based and fuel-based. One of the most used travel-based models for estimating emission inventories is driving cycles. It can be used for a variety of different things, such as establishing pollution regulations, traffic control, and calculating journey time. For these goals, researchers have previously attempted to use easily available, well-established driving cycles. In many ways, however, the local environment differs greatly from that of the driving cycle’s genesis. As a result, these cycles’ applications have failed to provide high-quality results. This research aims to analyse the various approaches utilised for driving cycle construction in various locations of Malaysia under varied operational situations.

Journal ArticleDOI
TL;DR: In this article , a defect cavity multi-layer Bragg reflector structure is proposed theoretically to find the presence of thyroid cancer cells in the given sample using characteristic matrix method (CMM).
Abstract: In the proposed work, a defect cavity multi-layer Bragg reflector structure is proposed theoretically to find the presence of thyroid cancer cells in the given sample. The modelling, design and analysis of the sensor is performed using characteristic matrix method (CMM). Proposed structure has central defect cavity with 6 pairs of low and high refractive index layers on each side of the defect. To enhances the sensor sensitivity, the incident light in mid-infrared frequency range is used as input light source. The refractive index of normal and thyroid cancer cells is analysed for the performance of the sensor. The obtained Q factor and sensitivity of the sensor design is 3729 and 2828 nm/RIU respectively. The proposed sensor is a best choice of optical sensor for the detection of thyroid cancer cells in the given test sample for accurate analysis in medical applications.

Journal ArticleDOI
TL;DR: In this paper , a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection, and results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads.
Abstract: Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.

Journal ArticleDOI
TL;DR: In this paper , the authors suggested the using of proportional integral (PI) controller as an active queue management (AQM) and then use an optimized control system such as biogeography-based optimization (BBO) with PI controller as (bBO-PI) is characterized by access to design and fine-tuning of defining the shapes of the optimal parameters of PI controller.
Abstract: The congestion is the most important issue that effects on the performance of data transition over internet networks. One of the important techniques developed is active queue management (AQM) that prepares an efficient congestion control by reducing losing packets, queue size, and energy consumption. Therefore, AQM technique deemed as a base of many congestion control algorithms schemes. This work suggested the using of proportional integral (PI) controller as an AQM and then use an optimized control system such as biogeography-based optimization (BBO) with PI controller as (BBO-PI). The optimal control (BBO-PI) is characterized by access to design and fine-tuning of defining the shapes of the optimal parameters of PI controller. The BBO algorithm was implemented by using the mathematical system model by M-file/Matlab and Simulink. The simulation results showed the best performance for the transmission control protocol (TCP) network when compared the system with using the PI controller and using optimal control (BBO-PI), the ratio of enhancing the system with using of BBO-PI better than using a PI controller only in terms of rising time is 1.11, settling time is 2.85 and overshooting is 85%. Therefore, the proposed method was very fast and required few iterations.

Journal ArticleDOI
TL;DR: In this paper , the grey wolf optimization (GWO) was used to estimate the parametric values of the fractional order PI-PD controller with and without disturbance signal existence.
Abstract: The rotary inverted pendulum (RIP) has been used in various control application areas. This system can be represented as two degree of freedom (2-DOF), consisting of a rotating arm and rotating pendulum rod. RIP is an excellent example of designing a single-input multi-output (SIMO) system. Due to unstable RIP system dynamics, and its nonlinear model, multiple control techniques have been used to control this system. This paper uses integer and fractional order proportional integral-proportional derivative (PI-PD) controllers to stabilize the pendulum in the vertical direction. Constrained optimization approaches, such as the grey wolf optimization (GWO) methodology, are utilized to estimate the parametric values of the controllers. The simulation results showed that the fractional order PI-PD controller outperforms the integer order PI-PD controller with and without disturbance signal existence. A multiple results comparison has illustrated the superiority of fractional order controller over a previous work.

Journal ArticleDOI
TL;DR: In this paper , a modified camel travelling behavior algorithm is used to select the channels that used to extract the power of alpha and beta bands of motor imagery signals, and obtain classification accuracy more than 95% using support vector machine classifier.
Abstract: Brain computer interface (BCI) is a protocol to communicate between the human brain and a device or application using brain signals. These signals translated to useful commands by using features extraction and classification. The most widely used features is the power of alpha and beta rhythms. This type of features gives only 70% of classification accuracy without any extra processing using fixed channels to read the signals. Because the distribution of the power in the brain is not a standard for all people, each one has his own brain power map. A selection algorithm is used to find the best channels that could generate higher power than the fixed ones. Modified camel travelling behavior algorithm is used to select the channels that used to extract the power of alpha and beta bands of motor imagery signals. This algorithm is faster to find the best set of channels, and obtain classification accuracy more than 95% using support vector machine classifier.

Journal ArticleDOI
TL;DR: In this paper , an energy-aware wireless mesh network deployment optimization mechanism is proposed to determine a suitable location for the mesh routers with the aim of maximizing the network lifetime, and the location solution is obtained from the proposed mechanism, it is then evaluated for system lifetime and network performance by the network simulator (ns-3).
Abstract: Wireless mesh networks are widely used to create network infrastructure in rural areas due to their flexible properties, such as self-healing mechanisms and associated redundant paths. For example, a wireless mesh network can suitably operate with limited battery power in wildlife monitoring applications. The locations of mobile routers to support mobile sensor nodes are essential for extending the system lifetime. This study proposes an energy-aware wireless mesh network deployment optimization mechanism. The goal is to determine a suitable location for the mesh routers with the aim of maximizing network lifetime. After the location solution is obtained from the proposed mechanism, it is then evaluated for system lifetime and network performance by the network simulator (ns-3). The proposed method outperforms the brute-force method in terms of computation time for all amounts of mesh clients. For example, for 30 mesh clients, the proposed method uses only a few minutes, while the brute-force mechanism requires more than 200 minutes to complete the process. Furthermore, compared to the brute-force method, it achieves nearly the same system lifetime and other performance parameters, such as throughput, packet delivery ratio, and packet inter-arrival time. In the real implementation, in which the sensor node placements can be changed during the installation period owing to the environmental status or the recommendation of the installers, the results can be recalculated in a short period.

Journal ArticleDOI
TL;DR: In this paper , the best spectrum characteristics and photometric productivities of white light emitting diode (WLED) device possessing light emitting diodes (LEDs) in red rather than phosphor pc/R-WLEDs with the hue fidelity index (Rf) > 97 for correlated color temperatures (CCTs) between 2700 K and 6500 K were attained using the illumination effectiveness (LE) model.
Abstract: The best spectrum characteristics and photometric productivities of white light emitting diode (WLED) device possessing light emitting diodes (LEDs) in red rather than phosphor pc/R-WLEDs with the hue fidelity index (Rf) > 97 for correlated color temperatures (CCTs) between 2700 K and 6500 K were attained using the illumination effectiveness (LE) model. We demonstrate four practical pc/R-WLEDs that have indices of Rf and LE of 96–97 and 120–124 lm/W, respectively, under CCT values measured at 2969 K, 4468 K, 5682 K, as well as 6558 K. These LED packages use blue and red LEDs, also phosphors in green as well as yellow, with respective wavelengths of 448 nm, 650 nm, 507 nm, and 586 nm. In the comparison between phosphor-transformed WLED devices (with phosphor conversion) and quantum-dot WLED devices (QD-WLEDs), pc/R-WLEDs make an outstanding performance in competitiveness for high hue generation, particularly under small CCT values, and could eventually replace current pc-WLEDs.

Journal ArticleDOI
TL;DR: In this article , a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade lgoma (HGG) group.
Abstract: Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

Journal ArticleDOI
TL;DR: In this article , three kinds of chaotic maps are utilized to build a digital image encryption strategy depending on a chaotic system: the logistic map, Arnold Cat's map, and Baker's map.
Abstract: Over the last twenty years, chaos-based encryption has been an increasingly popular way to encrypt and decrypt data using nonlinear dynamics and deterministic chaos. Discrete chaotic systems based on iterative maps have gotten a lot of interest because of their simplicity and speed. In this paper, three kinds of chaotic maps are utilized to build a digital image encryption strategy depending on a chaotic system. These chaotic maps are the logistic map, Arnold Cat’s map, and Baker’s map. In addition to using the triple data encryption standard (3DES) encryption scheme with the chaotic maps mentioned. The results of the experiments revealed that the suggested digital image encryption technique is both efficient and secure, making it ideal for usage in insecure networks. The transmission control protocol (TCP)/internet protocol (IP) protocol was used for the purpose of transferring data from server to client through the network and vice versa.

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors developed an efficient iris segmentation algorithm using image processing techniques, which solved the outer boundary segmentation of the iris problem and detected the pupil boundary.
Abstract: The limitation of traditional iris recognition systems to process iris images captured in unconstraint environments is a breakthrough. Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, the most challenging problems are related to the severe noise effects that are inherent to these unconstrained iris recognition systems, varying illumination, obstruction of the upper or lower eyelids, the eyelash overlap with the iris region, specular highlights on pupils which come from a spot of light during captured the image, and decentralization of iris image which caused by the person’s gaze. Iris segmentation is one of the most important processes in iris recognition. Due to the different types of noise in the eye image, the segmentation result may be erroneous. To solve this problem, this paper develops an efficient iris segmentation algorithm using image processing techniques. Firstly, the outer boundary segmentation of the iris problem is solved. Then the pupil boundary is detected. Testes are done on the Chinese Academy of Sciences’ Institute of Automation (CASIA) database. Experimental results indicate that the proposed algorithm is efficient and effective in terms of iris segmentation and reduction of time processing. The accuracy results for both datasets (CASIA-V1 and V4) are 100% and 99.16 respectively.

Journal ArticleDOI
TL;DR: In this paper , the authors used several classifiers and vectorizers to see their effect on processing social media data and found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering.
Abstract: In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided learning. Our tests use social media data. Based on the tests that have been carried out on classifier variations and vectorizer variations, it was found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering. Meanwhile, from the test results on the count vectorizer, term frequency-inverse document frequency (TFIDF), and hashing, the best accuracy is achieved on the TFIDF vectorizer. In this case, it means that the TFIDF vectorizer has a better value in presenting word feature dimensions.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a robust and lightweight security and key agreement-based identity protocol LSKA-ID for vehicular communication, which utilizes the elliptic curve cryptography, Chinese reminder theorem, and identity-based cryptosystem to resolve the issues found in the previously proposed schemes, in which their protocol can resolve the key escrow issues accompanied in most ID-based schemes.
Abstract: Recently, a huge effort has been pushed to the wireless broadcasting nature in the open area. However, the vehicular ad hoc network (VANET) is disposed to various kinds of attacks. Hence, keeping the security in VANET is the most critical issue because of the VANET network related to human life. Thus, we propose a robust and lightweight security and key agreement-based identity protocol LSKA-ID for vehicular communication. Our protocol utilizes the elliptic curve cryptography, Chinese reminder theorem, and identity (ID)-based cryptosystem to resolve the issues found in the previously proposed schemes, in which our protocol can resolve the key escrow issues accompanied in most ID-based schemes. Also, it does not need batch verification operations, which cause some problems to the verifier in case the batch beacons have one or more illegal beacons. Moreover, the LSKA-ID protocol addresses the dependency on the trusted authority (TA) during the high frequent handover between the groups that may cause a bottleneck problem on the TA. The security analysis proves the correctness of the LSKA-ID protocol by using the random oracle model and has shown to be effective in a performance evaluation.

Journal ArticleDOI
TL;DR: In this article , a controller for a doubly fed induction generator (DFIG) connected to a wind system is proposed, which assigns its own structures as an optimal control method, the electric model in the DFIG state space is also shown, for which it is expected to estimate a linear model through subspace technique and thus to design the controller.
Abstract: In this paper, a controller for a doubly fed induction generator (DFIG) connected to a wind system is proposed. This control assigning its own structures as an optimal control method, the electric model in the DFIG state space is also shown, for which it is expected to estimate a linear model through the subspace technique and thus to design the controller. It will be possible to show that a structure assignment controller is undoubtedly a good option for the control of multivariable systems. The results of the output signals will be analyzed when applying the controller, assigning their own structures, which will allow us to observe that the response and disturbance times are below two tenths of a second.