Search

Quantum key distribution (QKD) gives a way to generate unconditionally secure keys for two remote users, Alice and Bob. Information reconciliation (IR), which can correct the errors caused by the imperfections of the QKD systems, is a critical component in QKD. Due to the high-security requirements and large volumes of data processing, robustness and efficiency are two main factors that must be considered for the implementation of IR. The polar codes-based IR has several potential advantages, such as capacity to reach Shannon-limit, high IR efficiency, and low computational complexity. Although CPU-based IR is always implemented in most of the previous works, it is not the optimal implementation in terms of performance and power dissipation. To the best of our knowledge, there is still no work to build a special-purpose hardware module for polar codes-based IR. In this paper, a dedicated design of hardware accelerator is first proposed for polar codes-based IR, in which the block-checked successive cancellation list (SCL) algorithm is used to verify the consistency of the sifted keys, and the overall failure probability of IR is significantly reduced. The proposed design is constructed into a partially-unrolled parallel architecture to accelerate the core decoder as well as balance the resource utilization. Furthermore, the hardware implementation is completed based on Xilinx Zynq UltraScale+ XCZU5EV MPSoC platform and achieves an IR throughput of 15 Mbps with a block length of 212, while less than 20 % of the amount of on-chip resources are used in other previous designs of SCL decoder. The proposed design can provide a real-time, low-cost solution for IR in QKD systems, and enhance the performance of QKD.

K-Nearest-Neighbor search (KNN) has been extensively used in the localization and mapping based on 3D laser point clouds in smart vehicles. Considering the real-time requirement of localization and stringent battery constraint in smart vehicles, it is a great challenge to develop highly energy-efficient KNN implementations. Unfortunately, previous KNN implementations either cannot efficiently build search data structures or cannot search efficiently in massive and unevenly distributed point clouds. To solve the issue, we propose a new framework to optimize the implementation of KNN on FPGAs. First, we propose a novel data structure with a spatial subdivision method, which can be built efficiently even for massive point clouds. Second, based on our data structure, we propose a KNN search algorithm which is able to search in unevenly distributed point clouds efficiently. We have implemented the new framework on both FPGA and GPU. Energy efficiency results show that our proposed method is on average 2.1 times and 6.2 times higher than the state-of-the-art implementations of KNN on FPGA and GPU platform, respectively.

Upconversion photodetectors convert photons from the infrared to the visible light spectrum and are of use in applications such as infrared detection and imaging. High-performance upconversion devices are, however, typically based on vacuum-deposited materials, which are expensive and require high operating voltages, which limits their implementation in flexible systems. Here we report solution-processed optical upconversion photodetectors with a high photon-to-photon conversion efficiency of 6.5% and a low turn-on voltage of 2.5 V. Our devices consist of a colloidal lead sulfide quantum dot layer for harvesting infrared light that is monolithically coupled to a cadmium selenide/zinc selenide quantum dot layer for visible-light emission. We optimized the charge-extraction layers in these devices by incorporating silver nanoparticles into the electron transport layers to enable carrier tunnelling. Our photodetectors exhibit a low dark current, high detectivity (6.4 × 1012 Jones) and millisecond response time, and are compatible with flexible substrates. We also show that the devices can be used for in vitro bioimaging. By embedding silver nanoparticles in the electron transport layer, solution-processed quantum-dot-based photodetectors with a high photon-to-electron conversion efficiency of 6.5% and a low turn-on voltage of 2.5 V can be created for use in infrared imaging applications.

International audience; Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling highthroughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed.

Recent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate

Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy >99.6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high-speed analysis reaches 346 frames per second, outperforming the state-of-the-art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications. Ministry of Education (MOE) National Research Foundation (NRF) Public Utilities Board (PUB) Submitted/Accepted version This work was supported by the Singapore National Research Foun-dation under the Competitive Research Program (NRF-CRP13-2014-01), the Incentive for Research & Innovation Scheme(PUB-1804-0082) administered by the PUB, and Ministry of Educa-tion Tier 1 RG39/19

We demonstrate a smart sensor for label-free multicomponent chemical analysis using a single label-free ring resonator to acquire the entire resonant spectrum of the mixture and a neural network model to predict the composition for multicomponent analysis. The smart sensor shows a high prediction accuracy with a low root-mean-squared error ranging only from 0.13 to 2.28 mg/mL. The predicted concentrations of each component in the testing dataset almost all fall within the 95% prediction bands. With its simple label-free detection strategy and high accuracy, the smart sensor promises great potential for multicomponent analysis applications in many fields. Ministry of Education (MOE) National Research Foundation (NRF) Published version

Recent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate, e.g., rare bioparticle detection, in which the representative image data is hard to collect, the training data is imbalanced, and the input images in inference time could be different from the training images. Deep metric learning offers a better generatability by using distance information to model the similarity of the images and learning function maps from image pixels to a latent space, playing a vital role in rare object detection. In this paper, we propose a robust model based on a deep metric neural network for rare bioparticle (Cryptosporidium or Giardia) detection in drinking water. Experimental results showed that the deep metric neural network achieved a high accuracy of 99.86% in classification, 98.89% in precision rate, 99.16% in recall rate and zero false alarm rate. The reported model empowers imaging flow cytometry with capabilities of biomedical diagnosis, environmental monitoring, and other biosensing applications. Ministry of Education (MOE) National Research Foundation (NRF) Published version This work was supported by the Singapore National Research Foundation under the Competitive Research Program (NRFCRP13- 2014-01), Ministry of Education Tier 1 RG39/19, and the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001).

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research was funded by the Singapore National Research Foundation under the Competitive Research Program (NRF-CRP13-2014-01), and Ministry of Education Tier 1 RG39/19.

On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy.

Single cell imaging enables new applications such as biomedical diagnostics, food inspection, and water quality monitoring. In this paper, we study the cell imaging-based machine learning techniques for high-performance cell detection. By taking the advantage of deep learning and imaging flow cytometry, we manage to detect Cryptosporidium and Giardia cells in the bright-field images with high accuracy and high speed on embedded GPU system. Our experiments demonstrate that the newly developed deep learning-based algorithms surpasses the hand-crafted features and SVM-based algorithms. We achieved above 99 percentage in accuracy and 580+fps in speed on embedded Jetson TX2 platform. Our research will lead to a highly accurate real-time single cell level detection system in future.

Compared to fuel based vehicles, Electric Vehicles (EVs) have the advantages of fuel-saving, lower maintenance and reduced pollution etc. However, EVs bring new challenges for security as modern vehicles extensively interact with their environments, such as Wi-Fi, LTE and keyless entry system. These communication systems are cataloged into in-vehicle network and car to car/infrastructure (C2X) network. In this paper, we have addressed the importance of embedded security for electric vehicular communication, which provides confidentiality and privacy of the critical controlling information. In addition, we present the security solutions for electric charge station, which becomes another intrusion point because EVs must be charged frequently. Finally, we summarize the challenges and future trends of embedded security for electric vehicular communications.

Heterogeneous computing is gaining attention from both industry and academia nowadays. One driving factor for heterogeneous computing is the power efficiency. GPU and FPGA have been reported to achieve much higher power efficiency over CPU on many applications. Comparisons between GPU and FPGA show different characteristics of GPU and FPGA in accelerated computing. Some tasks run better on GPU, some run better on FPGA. Combining GPU and FPGA in one heterogeneous computing platform may provide us the advantages from both sides. This paper presents a heterogeneous computing platform with GPU and FPGA that we have built for power efficient high performance computing. The experimental results of 4 application examples show that different applications have different favorite computing architectures, which suggests a matching of the characteristics between the computation task and the computing architecture is the key to the power efficient high performance computing on heterogeneous computing platforms.

Adaptability as an emerging design trend for embedded systems has been of increasing interest. As the representative QoS-adaptable application/software model, Imprecise-Computation (IC) gains growing attentions on its ability to provide scalable execution quality by trading-off system resources. Tight coupling between application temporal characteristics and execution quality makes scheduling imperative. While existing QoS-aware scheduling methods are applicable for uniprocessor scenarios, few efforts address the multiprocessor problem. Moreover, significant leakage contribution to energy consumption, due to advanced silicon processing technology, has further complicated the scheduling problem. In this paper, we present a dynamic scheduling algorithm for IC-modeled tasks on real-time embedded multiprocessor systems, aiming at maximizing runtime QoS under timing and energy constraints. Based on the idea of runtime slack reclamation, we firstly analyze the key factors that affect the QoS optimality during slack distribution. The optimality analysis, then, serves as a guideline for a dual-phase slack receiver selection scheme that identifies the best slack receiver candidates. Compared to state-of-the-art dynamic algorithms, our approach can achieve 54.9 more QoS increase with negligible runtime overhead.

Hitherto discovered approaches analyze the execution time of a real-time application on all the possible cache hierarchy setups to find the application specific optimal two-level inclusive data cache hierarchy to reduce cost, space and energy consumption while satisfying the time deadline in real-time Multi-Processor Systems on Chip (MPSoC). These brute-force like approaches can take years to complete. Alternatively, application's memory access trace driven crude estimation methods can find a cache hierarchy quickly by compromising the accuracy of results. In this article, for the first time, we propose a fast and accurate application's trace driven approach to find the optimal real-time application specific two-level inclusive data cache hierarchy. Our proposed approach “TRISHUL” predicts the optimal cache hierarchy performance first and then utilizes that information to find the optimal cache hierarchy quickly. TRISHUL can suggest a cache hierarchy, which has up to 128 times smaller size, up to 7 times faster compared to the suggestion of the state-of-the-art crude trace driven two-level inclusive cache hierarchy selection approach for the application traces analyzed.

A nuisance parameter is introduced to the semimartingale regression model proposed by Aalen (1980), and we construct two estimators for this nuisance parameter based on the results of parametric estimation which were given by Mckeague (1986) using the method of sieves. The consistency of the estimators is also provided.