In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, c...
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In this work, we propose computationally efficient solvers for novel extensions of Wyner common information. By separating information sources into bipartite, the proposed Bipartite common information framework has di...
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This paper presents a non-isolated DC-DC boost converter using voltage lift techniques. The proposed structure offers continuous input current. Substantial voltage gain along-side low voltage stress across semiconduct...
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This paper presents a non-isolated DC-DC boost converter using voltage lift techniques. The proposed structure offers continuous input current. Substantial voltage gain along-side low voltage stress across semiconductors in this topology contribute to utilizing an MOSFET with lower RDS-ON and devices with low nominal voltage values. Consisting of a switch makes the control procedure of the converter uncomplicated. Voltage analysis for the converter has been done in continuous and discontinuous conduction modes (CCM)-(DCM). Furthermore, current calculation, design process and comparison study are provided. Dynamic performance of the proposed circuit is scrutinized by applying the state space average technique and small signal model. In order to accredit performance of the converter, an archetype with 26V input and 222V output voltages and approximately 196W power level at 50 kHz switching frequency has been built and tested.
Optical filters have always been a critical challenge for advancing communication systems. Despite significant progress in optical filters, the process of designing, fabricating, and characterizing filters has predomi...
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Microblogging websites have massed rich data sources for sentiment analysis and opinion mining. In this regard, sentiment classification has frequently proven inefficient because microblog posts typically lack syntact...
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Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scru...
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Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.
Photovoltaic (PV) system is one of the trending and alternative sources of energy. Harnessing reliable energy in these PV panels is a cumbersome task equipped with several challenges such as continuous monitoring, ada...
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In this article, a renovated patch array antenna is developed that achieves features such as high gain and circular polarization by introducing a bi-layered eight-shaped metasurface on top of the feed. The antenna is ...
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The ubiquitous integration of computer vision technologies has revolutionised various application domains, ranging from agriculture and transportation to healthcare and the military. In this context, real-time anomaly...
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Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance function...
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Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance functions. These diseases usually manifest as conditions that affect the internal components of the ear structure and can manifest themselves with symptoms such as hearing loss, ear pain, balance problems, and fluid accumulation in the ear. The accuracy of the diagnosis depends on expert knowledge and subjective opinion. This method is prone to human error. This study presents a novel computer-aided diagnosis system for otoscope images of ear diseases, utilizing a vision transformer-based feature extractor combined with machine learning classifiers to provide accurate second opinions for ENT specialists. For this purpose, a new model based on state-of-the-art vision transformer feature extractor and machine learning models is proposed. In the experimental study, the dataset, comprising 880 eardrum images categorized into four classes (CSOM, earwax, myringosclerosis, and normal), was split into training (70%), validation (10%), and testing (20%) subsets. Each image was preprocessed to 420 × 380 pixels to fit the input dimensions of the models. The vision transformer architecture was utilized for feature extraction, followed by classification using various machine learning algorithms including kNN, SVM, and random forest. As a result, the model using vision transformer feature extractor and k-nearest neighbors (kNN) algorithm achieved 99.00% accuracy. In this study, a deep learning-based and computer-aided diagnosis system, in other words, a computational model, was developed instead of the current human error-prone disease diagnosis method used by ear nose throat (ENT) specialists. The main purpose of the deep learning-based decision support system is to support the diagnosis process where expert knowledge is difficult to access and to provide an alternative opi
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