Andrea L. Dunbar

Swiss Center for Electronics and Microtechnology (Switzerland)

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Pedram Pad

Swiss Center for Electronics and Microtechnology (Switzerland)

Sébastien Blanc

Hôpital Femme Mère Enfant

N. Cantale

Swiss Center for Electronics and Microtechnology (Switzerland)

Clément Kündig

Swiss Center for Electronics and Microtechnology (Switzerland)

Ross P. Stanley

Swiss Center for Electronics and Microtechnology (Switzerland)

Amina Chebira

Swiss Center for Electronics and Microtechnology (Switzerland)

Sareh Saeedi

Swiss Center for Electronics and Microtechnology (Switzerland)

David Honzátko

Swiss Center for Electronics and Microtechnology (Switzerland)

Nemanja Niketić

Paul Scherrer Institute

Konstantinos Makantasis

University of Malta

Cooperative Institutions

Swiss Center for Electronics and Microtechnology (Switzerland)

École Polytechnique Fédérale de Lausanne

ETH Zurich

Sigint Solutions (Cyprus)

University of Neuchâtel

Technical University of Crete

Idaho National Laboratory

Swiss Federal Institute of Aquatic Science and Technology

École Normale Supérieure - PSL

Board of the Swiss Federal Institutes of Technology

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Benchmarking Neuromorphic Computing for Inference

River Publishers eBooks (2023)

Simon Narduzzi Loreto Mateu Petar Jokic Erfan Azarkhish Andrea L. Dunbar

In the last decade, there has been significant progress in the IoT domain due to the advances in the accuracy of neural networks and the industrialization of efficient neural network accelerator ASICs. However, intelligent devices will need to be omnipresent to create a seamless consumer experience. To make this a reality, further progress is still needed in the low-power embedded machine learning domain. Neuromorphic computing is a technology suited to such low-power intelligent sensing. However, neuromorphic computing is hampered today by the fragmentation of the hardware providers and the difficulty of embedding and comparing the algorithms' performance. The lack of standard key performance indicators spanning across the hardware-software domains makes it difficult to benchmark different solutions for a given application on a fair basis. In this paper, we summarize the current benchmarking solutions used in both hardware and software for neuromorphic systems, which are in general applicable to low-power systems. We then discuss the challenges in creating a fair and user-friendly method to benchmark such systems, before suggesting a clear methodology that includes possible key performance indicators.

Neuromorphic engineering

Benchmarking

10.1201/9781003377382-1

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VIVALDI: steel billet tracking with advanced computer vision

Sébastien Blanc Pedram Pad Engin Türetken N. Cantale David Honzátko

Our advanced computer vision system allows for the precise tracking of serial numbers on steel billets in challenging industrial settings. It combines cutting-edge hardware and machine learning, excelling in character recognition (99.8%) and localization while adapting to dynamic ambient lighting conditions (104 ). Moreover, it accurately measures crucial geometric parameters such as side sizes, bulging, and skewness. This multifaceted technology promises to elevate material tracking, quality assessment, and production optimization in the steel industry to unprecedented levels.

Tracking (education)

10.1117/12.3001270

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Hyperspectral imaging using a commercial light-field camera (Conference Presentation)

Ross P. Stanley Amina Chebira Alireza Ghasemi Andrea L. Dunbar

Hyperspectral imaging allows the collection of both spectral and spatial information. This modality is naturally fitted for object and material identification or detection processes, and has encountered a large success in the agriculture and food industries to name a few. In snapshot spectral imaging, the 3D cube of images is taken in one shot, with the advantage that dynamic scenes can be analyzed. The simplest way to make a hyperspectral camera is to put an array of wavelength filters on the detector and then integrate this detector with standard camera objectives. The technical challenge is to make arrays of N wavelength filters and repeat this sequence up to 100'000 times across the detector array, where each individual filter is matched to the pixel size and can be as small as a few microns. In this work, we generate the same effect with just one N wavelength filter array which is then multiplied and imaged optically onto the detector to achieve the same effective filter array. This was first outlined by Levoy and Hoystmeyer using microlens arrays in a light field camera (plenoptics 1.0). Instead of building our own light field camera we used an existing commercial camera, Lytro™ and used it as the engine for our telecentric hyperspectral camera. In addition, the tools to extract and rebuild the raw data from the Lytro™ camera were developed. We demonstrate reconstructed hyperspectral images with 9 spectral channels and show how this can be increased to achieve 81 spectral channels in a single snapshot.

Presentation (obstetrics)

10.1117/12.2253599

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A fast simple-to-use and inexpensive multispectral camera to detect skin conditions (Conference Presentation)

Andrea L. Dunbar Sébastien Blanc Pedram Pad Pierre-Alain Beuchat Αθανάσιος Παπαθανασίου

We demonstrate an intelligent multispectral system for various applications. The multispectral camera is handheld ideal for high resolution (5 Mpixel) applications, the system has 16 bands from 365 to 1020 nm that works at 40 fps, and can collect a hyperspectral cube in less the 500 milliseconds. The system uses deep convolution neural networks trained in a supervised learning fashion to characterize up to 7 skin conditions including skin cancer. Given a specific skin image the system characterizes the underline condition along with a confidence score.

Convolution (computer science)

10.1117/12.2546246

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Versatile, intelligent multispectral imaging camera made with off-the-shelf components

Andrea L. Dunbar Pedram Pad Nemanja Niketić Amina Chebira Ross P. Stanley

Multispectral imagers collect a hypercube of data, where the spatial image is along two-dimensions and the spectral information is in the third. Two main technologies are used for multispectral imaging: sweeping, where the hypercube is built by scanning through different wavelengths or spatial positions and snapshot multispectral spectral imaging, where the 3D cube of images is taken in one shot. Sweeping imaging systems tend to have more lines and better spectral resolutions whilst snapshot cameras are often used for dynamic analysis of scenes. A common method to obtain the hypercube in snapshot imagers is by pixel level filtering on the sensor chip. Pixel level filtering, where the filter is placed directly on the pixels are intergrated into the wafer-level making processing making them difficult to customize. Therefore, these sensors tend to aim for equally spaced spectral lines in order to cover many applications. This results in an often in an unnecessarily large data cube when only a few spectral lines are needed, moreover the spectral lines are not adapted to the specific application. In this work we propose a multispectral camera based on plenoptic imaging, where the filtering is done in a front-end optics module. Our camera has the usual advantages of a snapshot imager, and the added advantage that the spectral lines can be both reduced and tailored to the specific application by customizing the filter. This procedure reduces the hypercube whilst keeping performance by selecting the relevant data. Moreover, the filter is interchangeable for different applications The camera presented here is built with off-the-shelf components, shows >40 spectral channels, image sizes are 260x260 pixels, with pixel limited spatial resolution. We demonstrate this technology by fruit quality control using machine learning algorithms.

Off the shelf

10.1117/12.2290792

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Intelligent multispectral vision system for contactless water quality monitoring for wastewater

Karen Preitner Sébastien Blanc David Honzátko Clément Kündig Pedram Pad

Water quality monitoring in sewer networks remains a technical challenge even though water pollution and control are high priorities since decades. Current water quality monitoring usually analyzes samples in laboratories, allowing only sporadic measurements, or uses immersed sensors in the wastewater, leading to clogging and sensor fouling resulting in expense due to intensive maintenance. Both techniques thus have serious limitations. Previous research showed that UV-Vis reflectance spectrometry can be used for non-contact monitoring of turbidity (TUR) and Chemical Oxygen Demand (COD), which are two key water quality indicators. Although spectrometer achieve high spectral resolution their limited spatial field of view is problematic for highly inhomogeneous surfaces as is the case wastewater In this study, we obtain beyond state-of-art measurement accuracies by combining machine learning techniques with increased spatial field-of-view Multi-Spectral Imaging (MSI) whilst substantially reducing the spectral resolution. We designed and built a dedicated setup with a monochromatic camera and an active illumination of thirteen LEDs covering the spectrum range of 200-700 nm. We acquired and calibrated data on 27 samples with different concentrations of TUR and COD. Machine learning regression models were trained and evaluated with the extracted spectra. We tested the Partial Least Square (PLS), Support Vector Machine (SVM) and Random Forest (RF). PLS regression performed best with excellent correlation coefficients (R² ) of the 0.99 for TUR and 0.93 for COD. We obtained similar results with the SVM algorithm (R² = 0.99 and 0.92), whilst RF had lower scores (R² = 0.96 and 0.71).

10.1117/12.2649921

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Real time eye gaze tracking for human machine interaction in the cockpit

Engin Türetkin Sareh Saeedi Siavash Arjomand Bigdeli Patrick Stadelmann N. Cantale

The Aeronautics industry has pioneered safety from digital checklists to moving maps that improve pilot situational awareness and support safe ground movements. Today, pilots deal with increasingly complex cockpit environments and air traffic densification. Here we present an intelligent vision system, which allows real-time human-machine interaction in the cockpits to reduce pilot's workload. The challenges for such a vision system include extreme change in background light intensity, large field-of-view and variable working distances. Adapted hardware, use of state-of-the-art computer vision techniques and machine learning algorithms in eye gaze detection allow a smooth, and accurate real-time feedback system. The current system has been over-specified to explore the optimized solutions for different use-cases. The algorithmic pipeline for eye gaze tracking was developed and iteratively optimized to obtain the speed and accuracy required for the aviation use cases. The pipeline, which is a combination of data-driven and analytics approaches, runs in real time at 60 fps with a latency of about 32ms. The eye gaze estimation error was evaluated in terms of the point of regard distance error with respect to the 3D point location. An average error of less than 1.1cm was achieved over 28 gaze points representing the cockpit instruments placed at about 80-110cm from the participants' eyes. The angular gaze deviation goes down to less than 1° for the panels towards which an accurate eye gaze was required according to the use cases.

Cockpit

Situation Awareness

Headset

10.1117/12.2607434

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