High-Resolution ADCs Design in Image Sensors

This paper presents design considerations for high-resolution and high-linearity ADCs for biomedical imaging ap-plications. The work discusses how to improve dynamic spec-ifications such as Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) in ultra-low power and high-resolution analog-to-digital converters (ADCs) including successive approximation register (SAR) for biomedical imaging application. The results show that with broad range of mismatch error, the SFDR is enhanced by about 10 dB with the proposed performance enhancement technique, which makes it suitable for high resolution image sensors sensing systems

High Linearity SAR ADC for Smart Sensor Applications

This paper presents capacitive array optimization technique to improve the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) of Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) for smart sensor application. Monte Carlo simulation results show that capacitive array optimization technique proposed can make the SFDR, SNDR and (Signal-to-Noise Ratio) SNR more concentrated, which means the differences between maximum value and minimum value of SFDR, SNDR and SNR are much smaller than the conventional calibration techniques, more stable performance enhancement can be achieved, and the averaged SFDR is improved from 72.9 dB to 91.1 dB by using the capacitive array optimization method, 18.2 dB improvement of SFDR is obtained with only little expense of digital logic circuits, which makes it good choice for high resolution and high linearity smart sensing systems

Big data, big challenges: risk management of financial market in the digital economy

Purpose– The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It’s a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach– In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings– Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike Information Criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value– Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy

Optimizing Video Object Detection via a Scale-Time Lattice

High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem is to trade accuracy for efficiency in an effective way, i.e. reducing the computing cost while maintaining competitive performance. To seek a good balance, previous efforts usually focus on optimizing the model architectures. This paper explores an alternative approach, that is, to reallocate the computation over a scale-time space. The basic idea is to perform expensive detection sparsely and propagate the results across both scales and time with substantially cheaper networks, by exploiting the strong correlations among them. Specifically, we present a unified framework that integrates detection, temporal propagation, and across-scale refinement on a Scale-Time Lattice. On this framework, one can explore various strategies to balance performance and cost. Taking advantage of this flexibility, we further develop an adaptive scheme with the detector invoked on demand and thus obtain improved tradeoff. On ImageNet VID dataset, the proposed method can achieve a competitive mAP 79.6% at 20 fps, or 79.0% at 62 fps as a performance/speed tradeoff.Comment: Accepted to CVPR 2018. Project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice

ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training

Negative flips are errors introduced in a classification system when a legacy model is replaced with a new one. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy using model distillation, or use ensembles, which multiply inference cost prohibitively. We present a method to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model. Our method introduces a generalized distillation objective, Logit Difference Inhibition (LDI), that penalizes changes in the logits between the new and old model, without forcing them to coincide as in ordinary distillation. LDI affords the model flexibility to reduce error rate along with NFR. The method uses a homogeneous ensemble as the reference model for LDI, hence the name Ensemble LDI, or ELODI. The reference model can then be substituted with a single model at inference time. The method leverages the observation that negative flips are typically not close to the decision boundary, but often exhibit large deviations in the distance among their logits, which are reduced by ELODI.Comment: Tech repor

Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting. Document type: Articl

Strategies for promoting tendon-bone healing: Current status and prospects

Tendon-bone insertion (TBI) injuries are common, primarily involving the rotator cuff (RC) and anterior cruciate ligament (ACL). At present, repair surgery and reconstructive surgery are the main treatments, and the main factor determining the curative effect of surgery is postoperative tendon-bone healing, which requires the stable combination of the transplanted tendon and the bone tunnel to ensure the stability of the joint. Fibrocartilage and bone formation are the main physiological processes in the bone marrow tract. Therefore, therapeutic measures conducive to these processes are likely to be applied clinically to promote tendon-bone healing. In recent years, biomaterials and compounds, stem cells, cell factors, platelet-rich plasma, exosomes, physical therapy, and other technologies have been widely used in the study of promoting tendon-bone healing. This review provides a comprehensive summary of strategies used to promote tendon-bone healing and analyses relevant preclinical and clinical studies. The potential application value of these strategies in promoting tendon-bone healing was also discussed