Isorange pairwise orthogonal transform

Spectral transforms are tools commonly employed in multi- and hyperspectral data compression to decorrelate images in the spectral domain. The Pairwise Orthogonal Transform (POT) is one such transform that has been specifically devised for resource-constrained contexts like those found on board satellites or airborne sensors. Combining the POT with a 2D coder yields an efficient compressor for multi- and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion -i.e., the increase in bit depth of transformed images- is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2D standard CCSDS 122.0-B-1. This paper introduces the Isorange Pairwise Orthogonal Transform, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance

On the hardware implementation of the arithmetic elements of the pairwise orthogonal transform

The pairwise orthogonal transform (POT) is an attractive alternative to the Kahrunen-Loève transform for spectral decorrelation in on-board multispectral and hyperspectral image compression due to its reduced complexity. This work validates that the low complexity of the POT makes it feasible for a space-qualified field-programmable gate array (FPGA) implementation. A register transfer level description of the arithmetic elements of the POT is provided with the aim of achieving a low occupancy of resources and making it possible to synthesize the design on a space-qualified RTAX2000S and RTAX2000S-DSP. In order to accomplish these goals, the operations of the POT are fine-tuned such that their implementation footprint is minimized while providing equivalent coding performance. The most computationally demanding operations are solved by means of a lookup table. An additional contribution of this paper is a bit-exact description of the mathematical equations that are part of the transform, defined in such a way that they can be solved with integer arithmetic and implementations that can be easily cross-validated. Experimental results are presented, showing that it is feasible to implement the components of the POT on the mentioned FPGA

Lossless hyperspectral image compression using binary tree based decomposition

A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing "original pixel intensity"based coding approaches using traditional image coders (e.g. JPEG) to the "residual" based approaches using a predictive coder exploiting band-wise correlation for better compression performance. Moreover, as HS images are used in detection or classification they need to be in original form; lossy schemes can trim off uninteresting data along with compression, which can be important to specific analysis purposes. A modified lossless HS coder is required to exploit spatial- spectral redundancy using predictive residual coding. Every spectral band of an HS image can be treated like they are the individual frame of a video to impose inter band prediction. In this paper, we propose a binary tree based lossless predictive HS coding scheme that arranges the residual frame into integer residual bitmap. High spatial correlation in HS residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmetic coding. On the standard HS data set, the proposed lossless predictive coding has achieved compression ratio in the range of 1.92 to 7.94. In this paper, we compare the proposed method with mainstream lossless coders (JPEG-LS and lossless HEVC). For JPEG-LS, HEVCIntra and HEVCMain, proposed technique has reduced bit-rate by 35%, 40% and 6.79% respectively by exploiting spatial correlation in predicted HS residuals

Constant-SNR, rate control and entropy coding for predictive lossy hyperspectral image compression

Predictive lossy compression has been shown to represent a very flexible framework for lossless and lossy onboard compression of multispectral and hyperspectral images with quality and rate control. In this paper, we improve predictive lossy compression in several ways, using a standard issued by the Consultative Committee on Space Data Systems, namely CCSDS-123, as an example of application. First, exploiting the flexibility in the error control process, we propose a constant-signal-to-noise-ratio algorithm that bounds the maximum relative error between each pixel of the reconstructed image and the corresponding pixel of the original image. This is very useful to avoid low-energy areas of the image being affected by large errors. Second, we propose a new rate control algorithm that has very low complexity and provides performance equal to or better than existing work. Third, we investigate several entropy coding schemes that can speed up the hardware implementation of the algorithm and, at the same time, improve coding efficiency. These advances make predictive lossy compression an extremely appealing framework for onboard systems due to its simplicity, flexibility, and coding efficiency

Almost fixed quality rate-allocation under unequal scaling factors for on-board remote-sensing data compression

This article extends a rate-allocation method based on the near-lossless-rate (NLR) complexity that is designed to operate on-board spacecrafts, to include support for distortion scaling factors, such as those that are needed to code multi- and hyperspectral image when a spectral transform is employed. In this article, the conditions to achieve global minimum distortion are derived under the rate-distortion model based on the NLR complexity for the case of varying distortion scaling factors. Practical implementation issues are dealt with, and a rate-allocation method capable of operating under the constraints of on-board operation is provided. An exhaustive experimental validation of the rate-allocation method is performed, reporting modest performances for low rates and close to optimal performances for high rates

Design of a phased array antenna for a DVB-T based passive bistatic radar

This thesis was initiated by the Norwegian Defence Research Establishment's military air surveillance project on passive radar systems. The main objective of the thesis is the design of a phased array antenna for a DVB-T based passive bistatic radar.\\Design specifications for this array has been derived based on the radar's required sectoral coverage available DVB-T transmitters in the vicinity of the Oslo fjord. An 11 element horizontal linear array with dual polarized elements was found suitable for the application, where the bandwidth of the array should at least cover 622 MHz to 726 MHz, corresponding to DVB-T channels 40-52.\\\noindent A crossed bowtie antenna was found suitable as an array element and modelling and simulations were done using CST Microwave Studio. The resulting simulated bandwidth was from 624 MHz to 748 MHz for the horizontally polarized elements and 600 MHz to 800 MHz for the vertically polarized elements with an input reflection coefficient below -10 dB. In terms of radiation patterns, the center element of the array showed a half-power beamwidth in the horizontal plane of $122^{\circ}$ and $130^{\circ}$ for the horizontally and vertically polarized elements respectively. In the vertical plane the corresponding beamwidths was $120^{\circ}$ and $88^{\circ}$. When the array was scanned, the grating-lobe free scan range was $\theta_s = \pm 50^{\circ}$ at the highest operating frequency of 750 MHz, where the active reflection coefficient at the center element was lower than -7dB throughout the whole band when scanned to this angle.\\\noindent In order to verify the results from simulations in CST, a 5-element prototype array was produced with the objective of comparing simulations on a 5-element array in CST with those obtained from the prototype. The hypothesis was that if the measured performance on the prototype array was within acceptable limits of the simulated results, then one can presume that a full 11 element array will perform according to the simulation results given above. The center element of the 5-element array showed almost identical performance in terms of radiation patterns for both horizontal and vertical polarization, however with a higher level of cross polarization. A shift in center frequency of 30 MHz was found in measurements and it was found that this most likely stems from the fact that the quarter-wave balun used in the prototype, was not included in CST simulations. Apart from this, the measurements on the prototype array suggests that the CST simulations on the full 11 element array are valid, thus serving as a motivational factor to build a full 11 element prototype and characterize it through measurements.\\\noindent To summarize, the designed phased array antenna could potentially used as a sensor for a DVB-T based passive bistatic radar, covering channels 40-55 with a horizontal plane scan range of $\pm 50^{\circ}$. For future work it is recommended that the issue related to the frequency shift is sorted out and another row of elements in the vertical plane should be considered to further reduce the beamwidth in this plane

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

Stochastic dynamics of migrating cells

Cell migration is critical in many physiological phenomena, including embryogenesis, immune response, and cancer. In all these processes, cells face a common physical challenge: they navigate confining extra-cellular environments, in which they squeeze through thin constrictions. The motion of cells is powered by a complex machinery whose molecular basis is increasingly well understood. However, a quantitative understanding of the functional cell behaviours that emerge at the cellular scale remains elusive. This raises a central question, which acts as a common thread throughout the projects in this thesis: do migrating cells exhibit emergent dynamical 'laws' that describe their behavioural dynamics in confining environments? To address this question, we develop data-driven approaches to infer the dynamics of migrating cells directly from experimental data. We study the migration of cells in artificial confinements featuring a thin constriction across which cells repeatedly squeeze. From the experimental cell trajectories, we infer an equation of cell motion, which decomposes the dynamics into deterministic and stochastic contributions. This approach reveals that cells deterministically drive themselves into the thin constriction, which is in contrast to the intuition that constrictions act as effective barriers. This active driving leads to intricate non-linear dynamics that are poised close to a bifurcation between a bistable system and a limit cycle oscillator. We further generalize this data-driven framework to detect and characterize the variance of migration behaviour within a cell population and to investigate how cells respond to varying confinement size, shape, and orientation. We next investigate the mechanistic basis of these dynamics. Cell migration relies on the concerted dynamics of several cellular components, including cell protrusions and adhesive connections to the environment. Based on the experimental data, we systematically constrain a mechanistic model for confined cell migration. This model indicates that the observed deterministic driving is a consequence of the combined effects of the variable adhesiveness of the environment and a self-reinforcement of cell polarity in response to thin constrictions. These results suggest polarity feedback adaptation as a key mechanism in confined cell migration. Finally, we investigate the dynamics of interacting cells. To enable inference of cell-cell interactions, we develop Underdamped Langevin Inference, an inference method for stochastic high-dimensional and interacting systems. We apply this method to experiments of confined pairs of cells, which repeatedly collide with one another. This reveals that non-cancerous (MCF10A) and cancerous (MDA-MB-231) cells exhibit distinct interactions: while the non-cancerous cells exhibit repulsion and effective friction, the cancerous cells exhibit attraction and a surprising 'anti-friction' interaction. These interactions lead to non-cancerous cells predominantly reversing upon collision, while the cancer cells are able to efficiently move past one another by relative sliding. Furthermore, we investigate the effects of cadherin-mediated molecular contacts on cell-cell interactions in collective migration. Taken together, the data-driven approaches presented in this thesis may help to provide a new avenue to uncover the emergent laws governing the stochastic dynamics of migrating cells. We demonstrate how these approaches can provide key insights both into underlying mechanisms as well as emergent cell behaviours at larger scales.Zellmigration ist ein Kernelement vieler physiologischer Phänomene wie der Embryogenese, dem Immunsystem und der Krebsmetastase. In all diesen Prozessen stehen Zellen vor einer physikalischen Herausforderung: Sie bewegen sich in beengten Umgebungen, in denen sie Engstellen passieren müssen. Die Zellbewegung wird von einer komplexen Maschinerie an- getrieben, deren molekulare Komponenten immer besser verstanden werden. Demgegenüber fehlt ein quantitatives Verständnis des funktionalen Migrationsverhaltens der Zelle als Ganzes. Die verbindende Fragestellung der Projekte in dieser Arbeit lautet daher: gibt es emergente dynamische 'Gesetze', die die Verhaltensdynamik migrierender Zellen in beengten Umgebungen beschreiben? Um dieser Frage nachzugehen, entwickeln wir datengetriebene Ansätze, die es uns erlauben, die Dynamik migrierender Zellen direkt aus experimentellen Daten zu inferieren. Wir untersuchen Zellmigration in künstlichen Systemen, in denen Zellen Engstellen wiederholt passieren müssen. Aus den experimentellen Zelltrajektorien inferieren wir eine Bewegungsgleichung, die die Dynamik in deterministische und stochastische Komponenten trennt. Diese Methode zeigt, dass sich Zellen deterministisch 'aktiv' in die Engstellen hineinbewegen, ganz entgegen der intuitiven Erwartung, dass Engstellen als Hindernis fungieren könnten. Dieser aktive Antrieb führt zu einer komplexen nichtlinearen Dynamik im Übergangsbereich zwischen einem bistabilen System und einem Grenzzyklus-Oszillator. Wir verallgemeinern diesen datenbasierten Ansatz, um die Varianz des Migrationsverhaltens innerhalb einer Zellpopulation zu quantifizieren, und analysieren, wie Zellen auf die Größe, Form und Orientierung ihrer Umgebung reagieren. Darauf aufbauend untersuchen wir die zugrundeliegenden Mechanismen dieser Dynamik. Zellmigration basiert auf verschiedenen zellulären Komponenten, wie unter Anderem den Zellprotrusionen und der Adhäsion mit der Umgebung. Auf Basis der experimentellen Daten entwickeln wir ein mechanistisches Modell für Zellmigration in beengten Systemen, welches zeigt, dass der beobachtete aktive Antrieb eine Konsequenz zweier Effekte ist: Einer variierenden Adhäsion mit der Umgebung und einer Zellpolarität, die sich in Engstellen selbst verstärkt. Diese Ergebnisse deuten darauf hin, dass die Anpassung der Zellpolarität an die lokale Geometrie ein Schlüsselmechanismus in beengter Zellmigration ist. Schließlich analysieren wir die Dynamik interagierender Zellen. Um Zell-Zell Interaktionen zu inferieren, entwickeln wir die Underdamped Langevin Inference, eine Inferenzmethode für stochastische hochdimensionale und interagierende Systeme. Wir wenden diese Methode auf Daten von eingeschlossenen Zellpaaren an, welche wiederholt miteinander kollidieren. Dies zeigt, dass gesunde (MCF10A) und krebsartige (MDA-MB-231) Zellen unterschiedliche Interaktionen aufweisen: Während gesunde Zellen mit Abstoßung und effektiver Reibung interagieren, zeigen Krebszellen Anziehung und eine überraschende 'Anti-Reibung'. Diese Interaktionen führen dazu, dass gesunde Zellen nach Kollisionen primär umkehren, während Krebszellen effizient aneinander vorbeigleiten. Darüberhinaus analysieren wir die Effekte von Cadherin-basierten Molekularkontakten auf Zell-Zell Interaktionen in kollektiver Migration. Zusammenfassend könnten die in dieser Arbeit präsentierten datengetriebenen Ans ̈atze dabei helfen, ein besseres Verständnis der emergenten stochastischen Dynamik migrierender Zellen zu erlangen. Wir zeigen, wie diese Methoden wichtige Erkenntnisse sowohl über die zugrundeliegenden Mechanismen als auch über das emergente Zellverhalten liefern können

Einheitliche Gütemaße für Clusterings, Layouts und Orderings von Graphen, und deren Anwendung als Software-Entwurfskriterien

How good is a given graph clustering, graph layout, or graph ordering --specifically, how well does it group densely connected vertices and separate sparsely connected vertices? How good is a given software design -- specifically, how well does it minimize the interdependence of the subsystems? This work introduces and validates simple and uniform measures for these two properties. Together with existing optimization algorithms, the introduced measures enable the automatic computation e.g. of communities in social networks and of design flaws in software systems. The first part derives, validates, and unifies quality measures for graph clusterings, graph layouts, and graph orderings, with the following results: - Identical quality measures can be applied to clusterings, layouts, and orderings; this enables the computation of consistent clusterings, layouts, and orderings. - Diverse existing and new measures can be unified into few general measures; this facilitates their comparison and validation. - Many existing measures are biased towards certain clusterings, layouts, or orderings, even for graphs without particularly dense or sparse subgraphs, and thus do not (only) measure quality in the above sense. - For example graphs, the minimization of new, unbiased (or weakly biased) measures reveals nonobvious groups, e.g. communities in social networks, subject areas in hypertexts, or closely interlocked countries in international trade. The second part derives, validates, and unifies dependency-based indicators of software design quality. It applies two quality measures for graph clusterings as measures for the coupling of software subsystems -- specifically for the coupling indicated by common changes and for the coupling indicated by references -- and shows: - The measures quantify the dependency-caused development costs, under well-defined simplifying assumptions. - The minimization of the measures conforms to existing dependency-related design principles (like locality of change, acyclicity of references, and stability of references), design rules, and design patterns. - For example software systems, the incremental minimization of the measures reveals nonobvious design flaws, like the distribution of coherent responsibilities over several subsystems, or references from low-level to high-level subsystems. In summary, this work shows that - simple measures can suffice to capture important aspects of graph clustering quality, graph layout quality, graph ordering quality, and software design quality, and - the optimization of simple measures can suffice to detect nonobvious and often useful structure in various real-world systems.Wie gut ist ein Graph-Clustering, Graph-Layout oder Graph-Ordering -- insbesondere, wie gut gruppiert es dicht verbundene Knoten? Wie gut ist ein Software-Entwurf -- insbesondere, wie gut minimiert er die Abhängigkeiten zwischen Subsystemen? Für diese beiden Eigenschaften definiert und validiert die vorliegende Arbeit einfache und einheitliche Maße. Zusammen mit existierenden Optimierungsalgorithmen ermöglichen diese Maße die automatische Entdeckung z.B. von kohäsiven Communities in sozialen Netzwerken und von Entwurfsfehlern in Software-Systemen. Der erste Teil definiert, validiert und vereinheitlicht Gütemaße für Graph-Clusterings, Graph-Layouts und Graph-Orderings, mit folgenden Ergebnissen: - Identische Gütemaße können auf Clusterings, Layouts und Orderings angewendet werden. Dies ermöglicht die Berechnung von konsistenten Clusterings, Layouts und Orderings. - Viele existierende und neue Gütemaße können zu wenigen allgemeinen Maßen vereinheitlicht werden; dies erleichtert ihren Vergleich und ihre Validierung. - Viele existierende Maße messen nicht (nur) Güte im obigen Sinne, da sie selbst für Graphen ohne ungewöhnlich dichte oder dünne Teilgraphen bestimmte Clusterings, Layouts oder Orderings bevorzugen. - Durch Optimierung verbesserter Maße lassen sich nicht-offensichtliche Gruppen in vielen realen Systemen finden, z.B. Communities in sozialen Netzwerken, Themengebiete in Hypertexten, und Integrationsräume in der Weltwirtschaft. Der zweite Teil definiert, validiert und vereinheitlicht abhängigkeitsbasierte Indikatoren für Software-Entwurfsqualität. Er verwendet zwei Gütemaße für Graph-Clusterings als Maße für die Kopplung von Software-Subsystemen -- insbesondere für Kopplung, deren Symptom gemeinsame Änderungen sind und für Kopplung, deren Ursache Referenzen sind -- und zeigt: - Die Maße quantifizieren die durch Abhängigkeiten verursachten Entwicklungskosten, unter vereinfachenden Annahmen. - Die Optimierung der Maße impliziert anerkannte Entwurfsprinzipien (z.B. Lokalität von Änderungen, Azyklizität von Referenzen, und Stabilität von Referenzen), Entwurfsregeln und Entwurfsmuster. - Durch Optimierung der Maße lassen sich nicht-offensichtliche Entwurfsfehler finden, z.B. die Verteilung kohärenter Verantwortlichkeiten über mehrere Subsysteme, oder Referenzen von allgemeinen zu speziellen Subsystemen. Zusammenfassend zeigt die Arbeit, dass - einfache Maße ausreichen, um wichtige Aspekte der Qualität von Graph-Clusterings, Graph-Layouts, Graph-Orderings und Software-Entwürfen zu formalisieren, und - die Optimierung einfacher Maße ausreicht, um nicht-offensichtliche und nützliche Struktur in verschiedensten Systemen zu finden

Advances in Ginsenosides

This book collected recent innovative research and review articles on analytical techniques, production protocols, biotechnological tools, and new insights into bioactivities of ginsenosides including the effects on epithelial-mesenchymal transition, hippocampal neurogenesis and inflammation as well as on diseases such as ischemic stroke, autoimmune diseases, and allergic disorders. Additionally, the analysis through molecular docking and an overview of the Panax ginseng pharmacopuncture were also presented