Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

OCM 2021 - Optical Characterization of Materials

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

OCM 2021 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Uncertainty Quantification in Biophotonic Imaging using Invertible Neural Networks

Owing to high stakes in the field of healthcare, medical machine learning (ML) applications have to adhere to strict safety standards. In particular, their performance needs to be robust toward volatile clinical inputs. The aim of the work presented in this thesis was to develop a framework for uncertainty handling in medical ML applications as a way to increase their robustness and trustworthiness. In particular, it addresses three root causes for lack of robustness that can be deemed central to the successful clinical translation of ML methods: First, many tasks in medical imaging can be phrased in the language of inverse problems. Most common ML methods aimed at solving such inverse problems implicitly assume that they are well-posed, especially that the problem has a unique solution. However, the solution might be ambiguous. In this thesis, we introduce a data-driven method for analyzing the well-posedness of inverse problems. In addition, we propose a framework to validate the suggested method in a problem-aware manner. Second, simulation is an important tool for the development of medical ML systems due to small in vivo data sets and/or a lack of annotated references (e. g. spatially resolved blood oxygenation (sO 2 )). However, simulation introduces a new uncertainty to the ML pipeline as ML performance guarantees generally rely on the testing data being sufficiently similar to the training data. This thesis addresses the uncertainty by quantifying the domain gap between training and testing data via an out-of-distribution (OoD) detection approach. Third, we introduce a new paradigm for medical ML based on personalized models. In a data-scarce regime with high inter-patient variability, classical ML models cannot be assumed to generalize well to new patients. To overcome this problem, we propose to train ML models on a per-patient basis. This approach circumvents the inter-patient variability, but it requires training without a supervision signal. We address this issue via OoD detection, where the current status quo is encoded as in-distribution (ID) using a personalized ML model. Changes to the status quo are then detected as OoD. While these three facets might seem distinct, the suggested framework provides a unified view of them. The enabling technology is the so-called invertible neural network (INN), which can be used as a flexible and expressive (conditional) density estimator. In this way, they can encode solutions to inverse problems as a probability distribution as well as tackle OoD detection tasks via density-based scores, like the widely applicable information criterion (WAIC). The present work validates our framework on the example of biophotonic imaging. Biophotonic imaging promises the estimation of tissue parameters such as sO 2 in a non-invasive way by evaluating the “fingerprint” of the tissue in the light spectrum. We apply our framework to analyze the well-posedness of the tissue parameter estimation problem at varying spectral and spatial resolutions. We find that with sufficient spectral and/or spatial context, the sO 2 estimation problem is well-posed. Furthermore, we examine the realism of simulated biophotonic data using the proposed OoD approach to gauge the generalization capabilities of our ML models to in vivo data. Our analysis shows a considerable remaining domain gap between the in silico and in vivo spectra. Lastly, we validate the personalized ML approach on the example of non-invasive ischemia monitoring in minimally invasive kidney surgery, for which we developed the first-in-human laparoscopic multispectral imaging system. In our study, we find a strong OoD signal between perfused and ischemic kidney spectra. Furthermore, the proposed approach is video-rate capable. In conclusion, we successfully developed a framework for uncertainty handling in medical ML and validated it using a diverse set of medical ML tasks, highlighting the flexibility and potential impact of our approach. The framework opens the door to robust solutions to applications like (recording) device design, quality control for simulation pipelines, and personalized video-rate tissue parameter monitoring. In this way, this thesis facilitates the development of the next generation of trustworthy ML systems in medicine

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Mixed grey wolf optimizer for the joint denoising and unmixing of multispectral images

International audienc

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered