Data-Driven Deep Learning-Based Analysis on THz Imaging

Breast cancer affects about 12.5% of women population in the United States. Surgical operations are often needed post diagnosis. Breast conserving surgery can help remove malignant tumors while maximizing the remaining healthy tissues. Due to lacking effective real-time tumor analysis tools and a unified operation standard, re-excision rate could be higher than 30% among breast conserving surgery patients. This results in significant physical, physiological, and financial burdens to those patients. This work designs deep learning-based segmentation algorithms that detect tissue type in excised tissues using pulsed THz technology. This work evaluates the algorithms for tissue type classification task among freshly excised tumor samples. Freshly excised tumor samples are more challenging than formalin-fixed, paraffin-embedded (FFPE) block sample counterparts due to excessive fluid, image registration difficulties, and lacking trustworthy pixelwise labels of each tissue sample. Additionally, evaluating freshly excised tumor samples has profound meaning of potentially applying pulsed THz scan technology to breast conserving cancer surgery in operating room. Recently, deep learning techniques have been heavily researched since GPU based computation power becomes economical and stronger. This dissertation revisits breast cancer tissue segmentation related problems using pulsed terahertz wave scan technique among murine samples and applies recent deep learning frameworks to enhance the performance in various tasks. This study first performs pixelwise classification on terahertz scans with CNN-based neural networks and time-frequency based feature tensors using wavelet transformation. This study then explores the neural network based semantic segmentation strategy performing on terahertz scans considering spatial information and incorporating noisy label handling with label correction techniques. Additionally, this study performs resolution restoration for visual enhancement on terahertz scans using an unsupervised, generative image-to-image translation methodology. This work also proposes a novel data processing pipeline that trains a semantic segmentation network using only neural generated synthetic terahertz scans. The performance is evaluated using various evaluation metrics among different tasks

Control Design of a Single-Phase DC/AC Inverter for PV Applications

This thesis presents controller designs of a 2 kVA single-phase inverter for photovoltaic (PV) applications. The demand for better controller designs is constantly rising as the renewable energy market continues to rapidly grow. Some background research has been done on solar energy, PV inverter configurations, inverter control design, and hardware component selection. Controllers are designed both for stand-alone and grid-connected modes of operation. For stand-alone inverter control, the outer control loop regulates the filter capacitor voltage. Combining the synchronous frame outer control loop with the capacitor current feedback inner control loop, the system can achieve both zero steady-state error and better step load performance. For grid-tied inverter control, proportional capacitor current feedback is used. This achieves the active damping needed to suppress the LCL filter resonance problem. The outer loop regulates the inverter output current flowing into the grid with a proportional resonant controller and harmonic compensators. With a revised grid synchronization unit, the active power and reactive power can be decoupled and controlled separately through a serial communication based user interface. To validate the designed controllers, a scaled down prototype is constructed and tested with a digital signal processor (DSP) TMS320F28335

Glucosyl anthranilate

In the crystal structure of the title compound, C21H25NO11, the hexopyranosyl ring adopts a chair conformation and the five substituents are in equatorial positions. An intra­molecular hydrogen bond between the amino group and a neighbouring carbonyl group is found. Two carbonyl groups are disordered and were refined using a split model

SAMP: A Toolkit for Model Inference with Self-Adaptive Mixed-Precision

The latest industrial inference engines, such as FasterTransformer1 and TurboTransformers, have verified that half-precision floating point (FP16) and 8-bit integer (INT8) quantization can greatly improve model inference speed. However, the existing FP16 or INT8 quantization methods are too complicated, and improper usage will lead to performance damage greatly. In this paper, we develop a toolkit for users to easily quantize their models for inference, in which a Self-Adaptive Mixed-Precision (SAMP) is proposed to automatically control quantization rate by a mixed-precision architecture to balance efficiency and performance. Experimental results show that our SAMP toolkit has a higher speedup than PyTorch and FasterTransformer while ensuring the required performance. In addition, SAMP is based on a modular design, decoupling the tokenizer, embedding, encoder and target layers, which allows users to handle various downstream tasks and can be seamlessly integrated into PyTorch.Comment: 6 page