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Full-Text Articles in Engineering
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
SMU Data Science Review
Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …
A High Performance Software Intensive Testbed For Rapid Prototyping And Controlled Testing Of Lte And Wi-Fi Radio Frequency Signals, Mirza Mohammad Maqbule Elahi
A High Performance Software Intensive Testbed For Rapid Prototyping And Controlled Testing Of Lte And Wi-Fi Radio Frequency Signals, Mirza Mohammad Maqbule Elahi
Open Access Theses & Dissertations
Long Term Evolution or LTE has gained interest for new applications that can benefit society including mobile broadband services like 802.11 or Wi-Fi. Both LTE and Wi-Fi uses similar modulation technique Orthogonal Frequency Division Multiplexing or OFDM. 6GHz sub carrier bands are crowded with LTE users. As wireless communications technology continues to develop, LTE technology in unlicensed bands (LTE-U) is a viable solution to the lack of spectrum resources. The competition between LTE-U and Wi-Fi will seriously impair their communication quality, so the friendly coexistence of both become an important research topic. This paper discusses the use of a software-defined …
An Efficient End-To-End Deep Neural Network For Interstitial Lung Disease Recognition And Classification, Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar Ahmed, Afm Shahen Shah
An Efficient End-To-End Deep Neural Network For Interstitial Lung Disease Recognition And Classification, Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar Ahmed, Afm Shahen Shah
Turkish Journal of Electrical Engineering and Computer Sciences
The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with …
Efficient Networks-On-Chip Communication Support Solutions For Deep Neural Network Acceleration, Binayak Tiwari
Efficient Networks-On-Chip Communication Support Solutions For Deep Neural Network Acceleration, Binayak Tiwari
UNLV Theses, Dissertations, Professional Papers, and Capstones
The increasing popularity of deep neural network (DNN) applications demands high computing power and efficient hardware accelerator architectures. DNN accelerators use a large number of processing elements (PEs) and on-chip memory for storing weights and other parameters. A significant challenge is faced when designing a many-core DNN accelerator to handle the data movement between the processing elements. As the communication backbone of a DNN accelerator, networks-on-chip (NoC) plays an important role in supporting various dataflow patterns and enabling processing with communication parallelism in a DNN accelerator. However, the widely used mesh-based NoC architectures inherently cannot efficiently support many-to-one (gather) and …
Remote Crop Disease Detection Using Deep Learning With Iot, Ivy Chung, Anoushka Gupta
Remote Crop Disease Detection Using Deep Learning With Iot, Ivy Chung, Anoushka Gupta
Electrical and Computer Engineering Senior Theses
Agriculture is such a vital part of our society, and according to the United Nations’ Food and Agricultural Organization (FAO), plant diseases are considered one of the two main causes of decreasing food availability. This paper explores not only the methods and findings of building a CNN-based disease detection model, but that of building a deployable remote crop disease detection system incorporating IoT technology. By using transfer learning with AlexNet, we were able to predict with 89.8% accuracy tomato plant images into one of the ten pre-defined disease classes. Our proposed system tracks plant health throughout the day by using …
Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin
Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin
Turkish Journal of Electrical Engineering and Computer Sciences
Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting conditions, and the resulting images often have low contrast. In this study, …
Segmenting Technical Drawing Figures In Us Patents, Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen
Segmenting Technical Drawing Figures In Us Patents, Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen
Computer Science Faculty Publications
Image segmentation is the core computer vision problem for identifying objects within a scene. Segmentation is a challenging task because the prediction for each pixel label requires contextual information. Most recent research deals with the segmentation of natural images rather than drawings. However, there is very little research on sketched image segmentation. In this study, we introduce heuristic (point-shooting) and deep learning-based methods (U-Net, HR-Net, MedT, DETR) to segment technical drawings in US patent documents. Our proposed methods on the US Patent dataset achieved over 90% accuracy where transformer performs well with 97% segmentation accuracy, which is promising and computationally …