Improved field programmable gatearraybased accelerator of deep neural networkusing opencl

Being compute-intensive and memory expensive, it is hard to deploy Deep Neural Network (DNN) based models into the embedded devices. Despite recent studies that have shown the efforts to explore the Field Programmable Gate Array (FPGA) as an alternative to deploy DNN-based models such as AlexNet and VGG, there is still a lot of challenges to implement DNN-based object detection model on Field Programmable Gate Array (FPGA). Hence, in this research, the design of a scalable parameterised DNN-based object detection model: Tiny YOLOv2 targeting on FPGA: Cyclone V PCIE Development Kit using High-Level-Synthesis (HLS) tool is explored. Considering the hardware resource limitations in term of computational resources and memory bandwidth, data quantization is proposed to convert the floating point (32-bit) of Tiny YOLOv2 into fixed-point (8-bit) design. To achieve the good performance, an in-depth analysis on the computation complexity and memory footprint of the Tiny YOLOv2 is also studied to find the best quantization scheme for Tiny YOLOv2. The proposed quantization scheme improves the memory requirements to store the parameter from 60 MB to 15 MB, which is around ×4 times improvement compared to the original floating-point design. Finally, the proposed implementation achieves a peak performance density of 0.29 Giga-Operation Per Second (GOPS)/Digital Signal Processing Block (DSP) with only 0.4% loss in the accuracy, which the performance is comparable to all other previous works

A scalable FPGA based accelerator for Tiny-YOLO-v2 using OpenCL

Deep Convolution Neural Network (CNN) algorithm have recently gained popularity in many applications such as image classification, video analytic, object recognition and segmentation. Being compute-intensive and memory expensive, CNN computations are common accelerated by GPUs with high power dissipations. Recent studies show implementation of CNN on FPGA and it gain higher advantage in term of energy-efficient and flexibility over Software-configurable-GPUs. The proposed framework is verified by implement Tiny-YOLO-v2 on De1SoC. The design development in this project is HLS approach to ease effort from writing complex RTL codes and provide fast verification through emulation and profiling tools provided in the OpenCL SDK. To best of our knowledge, this is the first implementation of Tiny-YOLO-v2 CNN based object detection algorithm on a small scale De1SoC board using Intel FPGA SDK for OpenCL approach

Augmented Lagrangian method for optimal control of interconnected systems

This paper explores the application of the augmented Lagrangian method (ALM) for constructing optimal control of some interconnected systems. The ALM proves to be a robust technique in handling the stability and observability restrictions arising from interconnections among subsystems. By segregating Lagrange multipliers from the solution process, the method effectively solves the optimal control problems in a simpler unconstrained setting. The proposed approach is substantiated through numerical simulation, demonstrating its efficacy in obtaining optimal control strategy for the interconnected networks of a power grid model

Development of a Rapid and High-Throughput Multiplex Real-Time PCR Assay for Mycoplasma hominis and Ureaplasma Species

Bacterial commensals of the human genitourinary tract, Mycoplasma hominis and Ureaplasma species (parvum and urealyticum) can be sexually transmitted, and may cause nongonococcal urethritis, pelvic inflammatory disease, and infertility. Mycoplasma hominis and Ureaplasma species may also cause severe invasive infections in immunocompromised patients. Current culture-based methods for Mycoplasma/Ureaplasma identification are costly and laborious, with a turnaround time between 1 and 2 weeks. We developed a high-throughput, real-time multiplex PCR assay for the rapid detection of M. hominis and Ureaplasma species in urine, genital swab, body fluid, and tissue. In total, 282 specimens were tested by PCR and compared with historic culture results; a molecular reference method was used to moderate discrepancies. Overall result agreement was 99% for M. hominis (97% positive percentage agreement and 100% negative percentage agreement) and 96% for Ureaplasma species (96% positive percentage agreement and 97% negative percentage agreement). Specimen stability was validated for up to 7 days at room temperature. This multiplex molecular assay was designed for implementation in a high-complexity clinical microbiology laboratory. With this method, >90 samples can be tested in one run, with a turnaround time of 4 to 5 hours from specimen extraction to reporting of results. This PCR test is also more labor effective and cheaper than the conventional culture-based test, thus improving laboratory efficiency and alleviating labor shortages

High negative predictive value of 68Ga PSMA PET-CT for local lymph node metastases in high risk primary prostate cancer with histopathological correlation

Background: Current guidelines highlight the importance of accurate staging in the management and prognostication of high risk primary prostate cancer. Conventional radiologic imaging techniques are insufficient to reliably detect lymph node metastases in prostate cancer. Despite promising results, there is limited published data on the diagnostic accuracy of PSMA PET-CT to assess local nodal metastases prior to radical prostatectomy. This study aims to assess the diagnostic efficacy of 68Ga PSMA PET-CT in local lymph node staging of high risk primary prostate cancer when compared to histopathological findings following radical prostatectomy with pelvic lymph node dissection. Methods: We retrospectively analysed consecutive patients with high risk primary prostate cancer referred by urologists for primary staging PSMA PET-CT using a 68Ga-labeled PSMA ligand, Glu-NH-CO-NHLys-(Ahx)-[HBEDDCC], from October 2015 to October 2017. The scans of patients who underwent radical prostatectomy with pelvic lymph node dissection were interpreted by the consensus reading of two experienced nuclear medicine physicians blinded to clinical and histopathological data. The contemporaneous records of the referring urologists were retrospectively reviewed for noteworthy unexpected PET findings that altered their personal preference for surgical management. Results: Seventy-one patients were recruited and analysed. PSMA PET-CT showed findings compatible with local disease in 47 patients (66.2%), lymph node metastases in 10 patients (14.1%) and distant metastases in 14 patients (19.7%). Twenty-eight patients (twenty-seven of whom had local disease only) underwent surgery yielding 214 lymph nodes, all of which were negative on histopathological analysis. On a node-based analysis, 213 of 214 lymph nodes were accurately identified as negative for disease with a negative predictive value of 100%. 11 patients had unexpected PET findings contemporaneously documented by urologists to alter their preference for surgical management. Conclusions: PSMA PET-CT appears to have a high negative predictive value for local lymph node metastases in high risk primary prostate cancer when compared to histopathological findings following radical prostatectomy with pelvic lymph node dissection

Serial imaging using [18F]Fluorodeoxyglucose positron emission tomography and histopathologic assessment in predicting survival in a population of surgically resectable distal oesophageal and gastric adenocarcinoma following neoadjuvant therapy

Background and objectives: We retrospectively evaluated the value of PET/CT in predicting survival and histopathological tumour-response in patients with distal oesophageal and gastric adenocarcinoma following neoadjuvant treatment. Methods: Twenty-one patients with resectable distal oesophageal adenocarcinoma and 14 with gastric adenocarcinoma between January 2002 and December 2011, who had undergone serial PET before and after neoadjuvant therapy followed by surgery, were enrolled. Maximum standard uptake value (SUVmax) and metabolic tumour volume were measured and correlated with tumour regression grade and survival. Results: Histopathological tumour response (PR) is a stronger predictor of overall and disease-free survival compared to metabolic response. ∆%SUVmax ≥70% was the only PET metric that predicted PR (82.4% sensitivity, 61.5% specificity, p = 0.047). Histopathological non-responders had a higher risk of death (HR 8.461, p = 0.001) and recurrence (HR 6.385, p = 0.002) and similarly in metabolic non-responders for death (HR 2.956, p = 0.063) and recurrence (HR 3.614, p = 0.028). Ordinalised ∆%SUVmax showed a predictive trend for OS and DFS, but failed to achieve statistical significance. Conclusions: PR was a stronger predictor of survival than metabolic response. ∆%SUVmax ≥70% was the best biomarker on PET that predicted PR and survival in oesophageal and gastric adenocarcinoma. Ordinalisation of ∆%SUVmax was not helpful in predicting primary outcomes

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects