In this paper, we proposed a new low-temperate polycrystalline silicon and oxide (LTPO) thin film transistor (TFT) pixel circuit for micro light-emitting diode (µLED) displays. This design considers the current-s...
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In this paper, we proposed a new low-temperate polycrystalline silicon and oxide (LTPO) thin film transistor (TFT) pixel circuit for micro light-emitting diode (µLED) displays. This design considers the current-sensitive attributes of µLEDs and employs a hybrid driving approach involving Pulse Width Modulation (PWM) and Pulse Amplitude Modulation (PAM). The proposed pixel circuit can not only compensate the threshold voltages of the two driving TFTs, but also reduce the voltage drop by current (IR drop) of the entire backplane through compensation and offset.
In the case of Neutral Point Clamped (NPC) converter, the 3rd harmonic component of the capacitor neutral point current causes voltage ripples to each DC link capacitor. The capacitor's low-order harmonic voltage ...
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Credit card fraud detection is a crucial task in fi-nancial institutions to safeguard against fraudulent transactions. This study investigates the performance of Random Forest and Gradient Boosting models on a Nigeria...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Credit card fraud detection is a crucial task in fi-nancial institutions to safeguard against fraudulent transactions. This study investigates the performance of Random Forest and Gradient Boosting models on a Nigerian credit card fraud detection dataset. Key features were selected using Chi-Square and Analysis of Variance (ANOVA) tests, and the models were evaluated across four data balancing techniques: no balancing (imbalanced), under-sampling, over-sampling, and hybrid sampling. The primary focus was on recall, as misclassifying fraudulent transactions can have significant financial implications. Gradient Boosting consistently outperformed Random Forest, achieving higher Recall, F1-Score, and AUC-ROC values across all experiments, particularly with over and hybrid sampling. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied to the Gradient Boosting model. SHAP analysis revealed that the amount of the transaction and average income expenditure were the most influential features in predicting fraud. Local and global explanations highlighted how specific feature values impacted the model’s decisions, while dependency plots uncovered interaction effects between key features. These findings contribute to the advancement of fraud detection methodologies, offering insights into effective strategies for mitigating financial risks associated with fraudulent transactions.
Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consump...
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Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, thoug...
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This research paper introduces a novel approach that leverages machine learning and natural language processing (NLP) techniques to detect emotions in text. The proposed system includes a Google Chrome extension that ...
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Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women...
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ISBN:
(数字)9798400704987
ISBN:
(纸本)9798350352115
Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a career in this industry, and those that do are less likely than men to stay in it. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement, unfair treatment and workplace culture. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals manifests in software engineering education at the university level. For this purpose, the study includes surveys and interviews. We use gender name perception as a survey instrument, and the results show small differences in perceptions of software engineering students based on their gender. Particularly, the survey respondents anchor the values of the male software engineer (Hans) to a variety of technical and non-technical skills, while the same description for a female software engineer (Hanna) is anchored mainly by her managerial skills. With interviews with women and non-binary students, we gain insight on the main barriers to their sense of ambient belonging. The collected data shows that some known barriers from the literature such as tokenism, and stereotype threat, do still exist. However, we find positive factors such as role models and encouragement that strengthen the sense of belonging among these students.
Wireless Sensor Networks (WSN) encompasses rapid development in research area through large scale applications. The main objective is to compare four different hardware platforms such as Rene, Mica, Spec and Blue in o...
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Heart disease, stroke, and other cardiovascular conditions are a major cause of death and disability worldwide. The negative effects of CVDs must be lessened through early prediction and efficient prevention technique...
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GaN field emitter arrays are being studied for use as vacuum channel transistors (VCTs). In this work, arrays of 150 x 150 GaN field emitters were characterized before and after UV exposure. Collector voltage was kept...
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