Matching Items (4)

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Thyroid nodule recognition in computed tomography using first order statistics

Description

Background
Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish

Background
Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish nodule from normal thyroid tissue. The pixel intensity in normal thyroid tissues is homogeneous and smooth. In the benign or malignant nodules, the pixel intensity is heterogeneous. Several studies have shown that the first order features in ultrasound image can be used as imaging biomarkers in nodule recognition.
Methods
In this paper, we investigate the feasibility of utilizing the first order texture features to identify nodule from normal thyroid tissue in CT image. A total of 284 thyroid CT images from 113 patients were collected in this study. We used 150 healthy controlled thyroid CT images from 55 patients and 134 nodule images (50 malignant and 84 benign nodules) from 58 patients who have undergone thyroid surgery. The final diagnosis was confirmed by histopathological examinations. In the presented method, first, regions of interest (ROIs) from axial non-enhancement CT images were delineated manually by a radiologist. Second, average, median, and wiener filter were applied to reduce photon noise before feature extraction. The first-order texture features, including entropy, uniformity, average intensity, standard deviation, kurtosis and skewness were calculated from each ROI. Third, support vector machine analysis was applied for classification. Several statistical values were calculated to evaluate the performance of the presented method, which includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area of under receiver operating characteristic curve (AUC).
Results
The entropy, uniformity, mean intensity, standard deviation, skewness (P < 0.05), except kurtosis (P = 0.104) of thyroid tissue with nodules have a significant difference from those of normal thyroid tissue. The optimal classification was obtained from the presented method. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.880, 0.821, 0.933, 0.917, 0.854, and 0.953 respectively.
Conclusion
First order texture features can be used as imaging biomarkers, and the presented system can be used to assist radiologists to recognize the nodules in CT image.

Contributors

Agent

Created

Date Created
  • 2017-06-02

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Current Sensing Amplifier Design for RRAM Crossbar Arrays

Description

Resistive Random Access Memory (RRAM) is an emerging type of non-volatile memory technology that seeks to replace FLASH memory. The RRAM crossbar array is advantageous in its relatively small cell

Resistive Random Access Memory (RRAM) is an emerging type of non-volatile memory technology that seeks to replace FLASH memory. The RRAM crossbar array is advantageous in its relatively small cell area and faster read latency in comparison to NAND and NOR FLASH memory; however, the crossbar array faces design challenges of its own in sneak-path currents that prevent proper reading of memory stored in the RRAM cell. The Current Sensing Amplifier is one method of reading RRAM crossbar arrays. HSpice simulations are used to find the associated reading delays of the Current Sensing Amplifier with respect to various sizes of RRAM crossbar arrays, as well as the largest array size compatible for accurate reading. It is found that up to 1024x1024 arrays are achievable with a worst-case read delay of 815ps, and it is further likely 2048x2048 arrays are able to be read using the Current Sensing Amplifier. In comparing the Current Sensing Amplifier latency results with previously obtained latency results from the Voltage Sensing Amplifier, it is shown that the Voltage Sensing Amplifier reads arrays in sizes up to 256x256 faster while the Current Sensing Amplifier reads larger arrays faster.

Contributors

Agent

Created

Date Created
  • 2016-12

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How UV photolysis accelerates the biodegradation and mineralization of sulfadiazine (SD)

Description

Sulfadiazine (SD), one of broad-spectrum antibiotics, exhibits limited biodegradation in wastewater treatment due to its chemical structure, which requires initial mono-oxygenation reactions to initiate its biodegradation. Intimately coupling UV photolysis

Sulfadiazine (SD), one of broad-spectrum antibiotics, exhibits limited biodegradation in wastewater treatment due to its chemical structure, which requires initial mono-oxygenation reactions to initiate its biodegradation. Intimately coupling UV photolysis with biodegradation, realized with the internal loop photobiodegradation reactor, accelerated SD biodegradation and mineralization by 35 and 71 %, respectively. The main organic products from photolysis were 2-aminopyrimidine (2-AP), p-aminobenzenesulfonic acid (ABS), and aniline (An), and an SD-photolysis pathway could be identified using C, N, and S balances. Adding An or ABS (but not 2-AP) into the SD solution during biodegradation experiments (no UV photolysis) gave SD removal and mineralization rates similar to intimately coupled photolysis and biodegradation. An SD biodegradation pathway, based on a diverse set of the experimental results, explains how the mineralization of ABS and An (but not 2-AP) provided internal electron carriers that accelerated the initial mono-oxygenation reactions of SD biodegradation. Thus, multiple lines of evidence support that the mechanism by which intimately coupled photolysis and biodegradation accelerated SD removal and mineralization was through producing co-substrates whose oxidation produced electron equivalents that stimulated the initial mono-oxygenation reactions for SD biodegradation.

Contributors

Agent

Created

Date Created
  • 2014-11-01

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Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

Description

Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for

Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications.

In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level.

Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well.

Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm.

Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.

Contributors

Agent

Created

Date Created
  • 2018