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.
This thesis work develops camera-based systems and algorithms to monitor several physiological waveforms and parameters, without having to bring the sensors in contact with a subject. Based on skin color change, photoplethysmogram (PPG) waveform is recorded, from which heart rate and pulse transit time are obtained. Using a dual-wavelength illumination and triggered camera control system, blood oxygen saturation level is captured. By monitoring shoulder movement using differential imaging processing method, respiratory information is acquired, including breathing rate and breathing volume. Ballistocardiogram (BCG) is obtained based on facial feature detection and motion tracking. Blood pressure is further calculated from simultaneously recorded PPG and BCG, based on the time difference between these two waveforms.
The developed methods have been validated by comparisons against reference devices and through pilot studies. All of the aforementioned measurements are conducted without any physical contact between sensors and subjects. The work presented herein provides alternative solutions to track one’s health and wellness under normal living condition.
expenditure, and environmental risk. Surfactant treatment to disrupt Scenedesmus biomass was evaluated
as a means to make solvent extraction more efficient. Surfactant treatment increased the recovery of fatty
acid methyl ester (FAME) by as much as 16-fold vs. untreated biomass using isopropanol extraction, and
nearly 100% FAME recovery was possible without any Folch solvent, which is toxic and expensive. Surfactant
treatment caused cell disruption and morphological changes to the cell membrane, as documented by
transmission electron microscopy and flow cytometry. Surfactant treatment made it possible to extract wet
biomass at room temperature, which avoids the expense and energy cost associated with heating
and drying of biomass during the extraction process. The best FAME recovery was obtained from highlipid
biomass treated with Myristyltrimethylammonium bromide (MTAB)- and 3-(decyldimethylammonio)-
propanesulfonate inner salt (3_DAPS)-surfactants using a mixed solvent (hexane : isopropanol = 1 : 1, v/v)
vortexed for just 1 min; this was as much as 160-fold higher than untreated biomass. The critical micelle
concentration of the surfactants played a major role in dictating extraction performance, but the growth
stage of the biomass had an even larger impact on how well the surfactants disrupted the cells and
improved lipid extraction. Surfactant treatment had minimal impact on extracted-FAME profiles and,
consequently, fuel-feedstock quality. This work shows that surfactant treatment is a promising strategy for
more efficient, sustainable, and economical extraction of fuel feedstock from microalgae.
The Combined Activated Sludge-Anaerobic Digestion Model (CASADM) quantifies the effects of recycling anaerobic-digester (AD) sludge on the performance of a hybrid activated sludge (AS)-AD system. The model includes nitrification, denitrification, hydrolysis, fermentation, methanogenesis, and production/utilization of soluble microbial products and extracellular polymeric substances (EPS). A CASADM example shows that, while effluent COD and N are not changed much by hybrid operation, the hybrid system gives increased methane production in the AD and decreased sludge wasting, both caused mainly by a negative actual solids retention time in the hybrid AD. Increased retention of biomass and EPS allows for more hydrolysis and conversion to methane in the hybrid AD. However, fermenters and methanogens survive in the AS, allowing significant methane production in the settler and thickener of both systems, and AD sludge recycle makes methane formation greater in the hybrid system.
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.
Anaerobic oxidation of methane (AOM) is an important process for understanding the global flux of methane and its relation to the global carbon cycle. Although AOM is known to be coupled to reductions of sulfate, nitrite, and nitrate, evidence that AOM is coupled with extracellular electron transfer (EET) to conductive solids is relatively insufficient. Here, we demonstrate EET-dependent AOM in a biofilm anode dominated by Geobacter spp. and Methanobacterium spp. using carbon-fiber electrodes as the terminal electron sink. The steady-state current density was kept at 11.0 ± 1.3 mA/m[superscript 2] in a microbial electrochemical cell, and isotopic experiments supported AOM-EET to the anode. Fluorescence in situ hybridization images and metagenome results suggest that Methanobacterium spp. may work synergistically with Geobacter spp. to allow AOM, likely by employing intermediate (formate or H[subscript 2])-dependent inter-species electron transport. Since metal oxides are widely present in sedimentary and terrestrial environments, an AOM-EET niche would have implications for minimizing the net global emissions of methane.