Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system

High-throughput cell sorting with flow cytometers is an important tool in modern clinical cell studies. Most cytometers use biomarkers that selectively bind to the cell, but induce significant changes in morphology and inner cell processes leading sometimes to its death. This makes label-based cell sorting schemes unsuitable for further investigation. We propose a label-free technique that uses a digital inline holographic microscopy for cell imaging and an integrated, optical neural network for high-speed classification. The perspective of dense integration makes it attractive to ultrafast, large-scale cell sorting. Network simulations for a ternary classification task (monocytes/granulocytes/lymphocytes) resulted in 89% accuracy

MULTICUBE: Multi-objective design space exploration of multi-core architectures

n/

Hardware/software partitioning of embedded system in ocapi-xl

The implementation of embedded networked appliances requires a mix of processor cores and HW accelerators on a single chip. When designing such complex and heterogeneous SoCs, the HW / SW partitioning decision needs to be made prior to refining the system description. With OCAPI-xl, we developed a methodology in which the partitioning decision can be made anywhere in the design flow, even just prior to doing code-generation for both HW and SW. This is made possible thanks to a refinable, implementable, architecture independent system description. The OCAPI-xl model was used to develop a stand alone, networked camera, with onboard GIF engine and network layer. 1

Real-time depth extraction and viewpoint interpolation on FPGA

info:eu-repo/semantics/publishe

Neural network for blood cell classification in a holographic microscopy system

Modern clinical laboratories are equipped with high-throughput flow cytometers for fast and accurate cell sorting. Most cytometers use selective biomarkers which often induce significant changes in the cell morphology, sometimes leading to cell death. However, for purposes like cell imaging there exist label-free techniques, for example digital inline holographic microscopy. Yet the image reconstruction algorithms needed to analyze the images do not scale up easily to large numbers of cells. We suggest an integrated, optical neural network to deal with the high-speed image classification with the promise of dense integration for ultrafast, cell sorting. A ternary classification task, distinguishing between monocytes, granulocytes, and lymphocytes resulted in 89% accuracy

Design Space Exploration for Run-time Management of a Reconfigurable System for Video Streaming

This Chapter reports a case study of Design Space Exploration for supporting Run-time Resource Management (RRM). In particular the management of system resources for an MPSoC dedicated to multiple MPEG4 encoding is addressed in the context of an Automotive Cognitive Safety System (ACSS). The run-time management problem is defined as the minimization of the platform power consumption under resource and Quality of Service (QoS) constraints. The Chapter provides an insight of both, design-time and run-time aspects of the problem. During the prelimiary design-time Design Space Exploration (DSE) phase, the best configurations of run-time tunable parameters are statically identified for providing the best trade-offs in terms of run-time costs and application QoS. To speed up the optimization process without reducing the quality of final results, a multi-simulator framework is used for modeling platform performance. At run-time, the RRM exploits the design-time DSE results for deciding an operating configuration to be loaded for each MPEG4 encoder. This operation is carried out dynamically, by following the QoS requirements of the specific use-case

Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer

A compelling clinical need exists for inexpensive, portable haematology analyzers that can be utilized at the point-of-care in emergency settings or in resource-limited settings. Development of a label-free, microfluidic blood analysis platform is the first step towards such a miniaturized, cost-effective system. Here we assemble a compact lens-free in-line holographic microscope and employ it to image blood cells flowing in a microfluidic chip, using a high-speed camera and stroboscopic illumination. Numerical reconstruction of the captured holograms allows classification of unlabeled leukocytes into three main subtypes: lymphocytes, monocytes and granulocytes. A scale-space recognition analysis to evaluate cellular size and internal complexity is also developed and used to build a 3-part leukocyte differential. The lens-free image-based classification is compared to the 3-part white blood cell differential generated by using a conventional analyzer on the same blood sample and is found to be in good agreement with it

Stereo Matching and Viewpoint Synthesis 3D-HDTV chipset

info:eu-repo/semantics/publishe