Energy Efficient Design of Massive MIMO Based on Closely Spaced Antennas: Mutual Coupling Effect

Uthansakul, Peerapong; Khan, Arfat Ahmad; Uthansakul, Monthippa; Duangmanee, Pumin

doi:10.3390/en11082029

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…The membership functions, as well as ranges of linguistic variables, can also be enhanced by doing a more comprehensive research. Moreover, with the advancements in wireless signal processing [56][57][58][59][60][61][62][63][64][65], and electronics [66][67][68], we believe that the methodology will be more simplified in the near future.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

Intelligent Medical Diagnostic System for Hepatitis B

Singh¹,

Prashar²,

Singla³

et al. 2022

Computers, Materials &Amp; Continua

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The hepatitis B virus is the most deadly virus, which significantly affects the human liver. The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage; otherwise, it will become a severe problem and make a human liver suffer from the most dangerous diseases, such as liver cancer. In this paper, two medical diagnostic systems are developed for the diagnosis of this life-threatening virus. The methodologies used to develop these models are fuzzy logic and the neuro-fuzzy technique. The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developed models. The classification accuracy of a multilayered fuzzy inference system is 94%. The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System (ANFIS) classifies the result corresponding to the given input is 95.55%. The comparison of both developed models on the basis of their performance parameters has been made. It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system. Furthermore, the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease. In other words, the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

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Section: Comparison Of Resultsmentioning

confidence: 99%

Intelligent Medical Diagnostic System for Hepatitis B

Singh¹,

Prashar²,

Singla³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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“…In the past, researchers were considering structural, color and texture features to identify and classify fish species. With the evolution of wirless [3][4][5][6][7] and electronic technologies [8,9], several automated methods were developed to visualize and identify fish species and place them in various classes. In these methods, it was easy to identify and extract information from the images having simple white and black background, but recognizing the fish species was challenging for underwater conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture

Ahmad¹,

Ali²,

Khan³

et al. 2023

Computer Systems Science and Engineering

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Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with the aim of averaging the results of predictions along with having more information compared to the dense layer. To check the validity of our model, we validate our model on the validation set which achieves satisfactory results. Also, for the localization task, we propose an architecture that consists of localization aware block, which captures localization information for better prediction and residual connections to handle the over-fitting problem. Actually, the residual connections help the layer to combine missing information with the relevant one. In addition, we use class weights and Focal Loss (FL) to handle class imbalance problems along with reducing false predictions. Actually, class weights assign less weights to classes having fewer instances and large weights to classes having more number of instances. During the localization, the qualitative assessment shows that we achieve 57% Mean Intersection Over Union (IoU) on testing data, and the classification results show 75% precision, 70% recall, 78% accuracy and 74% F1-Score for 468 fish species.

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“…Hence, they also emit CO 2 as the result of combustion from their engines, which increases the emission of CO 2 by 115 g/km/person [1]. Actually, all over the world, researchers are aiming to reduce the foot prints of carbon from different technologies, such as internet technologies [2][3][4][5][6], construction, electronics, power generation technologies etc. Furthermore, an electric motor cycle service is introduced in the campus, named as MoreSai@SUT, with mobile phone application having the same name.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

Poochaya¹,

Uthansakul²,

Uthansakul³

et al. 2022

Computers, Materials &Amp; Continua

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The number of accidents in the campus of Suranaree University of Technology (SUT) has increased due to increasing number of personal vehicles. In this paper, we focus on the development of public transportation system using Intelligent Transportation System (ITS) along with the limitation of personal vehicles using sharing economy model. The SUT Smart Transit is utilized as a major public transportation system, while MoreSai@SUT (electric motorcycle services) is a minor public transportation system in this work. They are called Multi-Mode Transportation system as a combination. Moreover, a Vehicle to Network (V2N) is used for developing the Multi-Mode Transportation system in the campus. Due to equipping vehicles with On Board Unit (OBU) and 4G LTE modules, the real time speed and locations are transmitted to the cloud. The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival (ETA). In terms of vehicle classifications and counts, we deployed CCTV cameras, and the recorded videos are analyzed by using You Only Look Once (YOLO) algorithm. The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed. Contrary to the existing researches, the proposed system is implemented in the real environment. The final results unveil the attractiveness and satisfaction of users. Also, due to the proposed system, the CO 2 gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

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Energy Efficient Design of Massive MIMO Based on Closely Spaced Antennas: Mutual Coupling Effect

Cited by 16 publications

References 36 publications

Intelligent Medical Diagnostic System for Hepatitis B

Intelligent Medical Diagnostic System for Hepatitis B

Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

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