Device quantization policy in variation-aware in-memory computing design.

Device quantization of in-memory computing (IMC) that considers the non-negligible variation and finite dynamic range of practical memory technology is investigated, aiming for quantitatively co-optimizing system performance on accuracy, power, and area. Architecture- and algorithm-level solutions are taken into consideration. Weight-separate mapping, VGG-like algorithm, multiple cells per weight, and fine-tuning of the classifier layer are effective for suppressing inference accuracy loss due to variation and allow for the lowest possible weight precision to improve area and energy efficiency. Higher priority should be given to developing low-conductance and low-variability memory devices that are essential for energy and area-efficiency IMC whereas low bit precision (< 3b) and memory window (< 10) are less concerned.

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning.

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller's health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller's health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

Room-temperature two-dimensional plasmonic crystal semiconductor lasers.

Room-temperature plasmonic-crystal lasers have been demonstrated with a square-lattice gold nano-pillar arrays on top of InGaAs/GaAs quamtum wells on a GaAs substrate. The lasing wavelength is tunable in the range of 865-1001 nm by varying the lattice period. The lasers exhibit an extremely narrow linewidth and small divergence angle so could have great potential for various applications. An unexpected mirror cavity effect has been observed and investigated. The mirror-cavity lasers have a very low threshold and could be developed to realize electrically-driven plasmonic lasers.

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning.

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird's eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Photon-Detection-Probability Simulation Method for CMOS Single-Photon Avalanche Diodes.

Single-photon avalanche diodes (SPADs) in complementary metal-oxide-semiconductor (CMOS) technology have excellent timing resolution and are capable to detect single photons. The most important indicator for its sensitivity, photon-detection probability (PDP), defines the probability of a successful detection for a single incident photon. To optimize PDP is a cost- and time-consuming task due to the complicated and expensive CMOS process. In this work, we have developed a simulation procedure to predict the PDP without any fitting parameter. With the given process parameters, our method combines the process, the electrical, and the optical simulations in commercially available software and the calculation of breakdown trigger probability. The simulation results have been compared with the experimental data conducted in an 800-nm CMOS technology and obtained a good consistence at the wavelength longer than 600 nm. The possible reasons for the disagreement at the short wavelength have been discussed. Our work provides an effective way to optimize the PDP of a SPAD prior to its fabrication.

Maintaining temporal coherence in video retargeting using mosaic-guided scaling.

Video retargeting from a full-resolution video to a lower resolution display will inevitably cause information loss. Content-aware video retargeting techniques have been studied to avoid critical visual information loss while resizing a video. Maintaining the spatio-temporal coherence of a retargeted video is very critical on visual quality. Camera motions and object motions, however, usually make it difficult to maintain temporal coherence using existing schemes. In this paper, we propose the use of a panoramic mosaic to guide the scaling of corresponding regions of video frames in a video shot to ensure good temporal coherence. In the proposed method, after aligning video frames in a shot to a panoramic mosaic constructed for the shot, a global scaling map for these frames is derived from the panoramic mosaic. Subsequently, the local scaling maps of individual frames are derived from the global map and is further refined according to spatial coherence constraints. Our experimental results show that the proposed method can effectively maintain temporal coherence so as to achieve good visual quality even a video contains camera motions and object motions.

HBeAg-negative chronic hepatitis B: cost-effectiveness of peginterferon alfa-2a compared to lamivudine in Taiwan.

OBJECTIVE: In Taiwan, the carrier rate of hepatitis B surface antigen is 15% to 20%, one of the highest in the world. Among chronic hepatitis B (CHB) patients, hepatitis B e antigen (HBeAg)-negative accounts for approximately 40% to 50% of these patients. A recent study found that peginterferon alfa-2a (40 KD) is more effective than lamivudine in treating HBeAg-negative CHB, but its cost-effectiveness has not been evaluated. Our objective is to evaluate the incremental cost-effectiveness of 48 weeks of peginterferon alfa-2a compared to 48 weeks of lamivudine, from the perspective of the Taiwan Bureau of National Health Insurance. METHODS: A Markov model was used to simulate the natural history of HBeAg-negative CHB in a cohort of 40-year-old patients. Efficacy, disease progression, economic, and quality-of-life data were derived from published literature and a survey of clinical experts in Taiwan. Life expectancy, quality-adjusted life expectancy, lifetime costs in New Taiwan Dollars (NTD) (1 USD = 31.96 NTD), and incremental cost-effectiveness ratios (ICERs) were calculated. RESULTS: The gain in quality-adjusted life-years (QALYs) for 48 weeks of peginterferon alfa-2a compared to 48 weeks of lamivudine was 0.45 at an additional cost of 157,000 NTD (4900 USD), resulting in an ICER of 347,000 NTD (10,900 USD) per QALY gained. The 95% central range for the ICER from a probabilistic sensitivity analysis was 228,000-566,000 NTD (7100-17,700 USD). CONCLUSIONS: In HBeAg-negative CHB, 48 weeks of treatment with peginterferon alfa-2a compared to 48 weeks of lamivudine appears to offer life expectancy and quality-of-life improvements at an acceptable cost-effectiveness ratio.