103 research outputs found

    Certain subclasses of multivalent functions defined by new multiplier transformations

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    In the present paper the new multiplier transformations \mathrm{{\mathcal{J}% }}_{p}^{\delta }(\lambda ,\mu ,l) (\delta ,l\geq 0,\;\lambda \geq \mu \geq 0;\;p\in \mathrm{% }%\mathbb{N} )} of multivalent functions is defined. Making use of the operator JpÎŽ(λ,ÎŒ,l),\mathrm{% {\mathcal{J}}}_{p}^{\delta }(\lambda ,\mu ,l), two new subclasses Pλ,ÎŒ,lÎŽ(A,B;σ,p)\mathcal{% P}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p) and P~λ,ÎŒ,lÎŽ(A,B;σ,p)\widetilde{\mathcal{P}}% _{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)\textbf{\ }of multivalent analytic functions are introduced and investigated in the open unit disk. Some interesting relations and characteristics such as inclusion relationships, neighborhoods, partial sums, some applications of fractional calculus and quasi-convolution properties of functions belonging to each of these subclasses Pλ,ÎŒ,lÎŽ(A,B;σ,p)\mathcal{P}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p) and P~λ,ÎŒ,lÎŽ(A,B;σ,p)\widetilde{\mathcal{P}}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p) are investigated. Relevant connections of the definitions and results presented in this paper with those obtained in several earlier works on the subject are also pointed out

    SAR automatic target recognition based on convolutional neural networks

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    We propose a multi-modal multi-discipline strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) imagery. Our architecture relies on a pre-trained, in the RGB domain, Convolutional Neural Network that is innovatively applied on SAR imagery, and is combined with multiclass Support Vector Machine classification. The multi-modal aspect of our architecture enforces the generalisation capabilities of our proposal, while the multi-discipline aspect bridges the modality gap. Even though our technique is trained in a single depression angle of 17°, average performance on the MSTAR database over a 10-class target classification problem in 15°, 30° and 45° depression is 97.8%. This multi-target and multi-depression ATR capability has not been reported yet in the MSTAR database literature

    Thermal analysis of space debris for infrared based active debris removal

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    In space, visual based relative navigation systems suffer from dynamic illumination conditions of the target (Eclipse conditions, solar glare...etc.) where most of these issues are addressed by advanced mission planning techniques. However, such planning would not be always feasible or even if it is, it would not be straightforward for Active Debris Removal (ADR) missions. On the other hand, using an infrared based system would overcome this problem, if a guideline to predict infrared signature of space debris based on the target thermal profile could be provided for algorithm design and testing. Spacecraft thermal design is unique to every platform. This means every ADR target will have a different infrared signature which changes over time not just only due to orbital dynamics but also due to its thermal surface coatings. In order to provide a space debris infrared signature guideline for most of the possible ADR targets, we introduce an innovative grouping system for thermal surface coatings based on their behaviour in Space environment. Through the use of this grouping system, we propose a space debris infrared signature estimation method which was extensively verified by our simulations and experiments. During our verifications, we have also found out very important problem so called ”Signature Ambiguity” that is unique to Infrared Based Active Debris Removal (IR-ADR) systems which we have also discussed in our work

    Using infrared based relative navigation for active debris removal

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    A debris-free space environment is becoming a necessity for current and future missions and activities planned in the coming years. The only means of sustaining the orbital environment at a safe level for strategic orbits (in particular Sun Synchronous Orbits, SSO) in the long term is by carrying out Active Debris Removal (ADR) at the rate of a few removals per year. Infrared (IR) technology has been used for a long time in Earth Observations but its use for navigation and guidance has not been subject of research and technology development so far in Europe. The ATV-5 LIRIS experiment in 2014 carrying a Commercial-of-The-Shelf (COTS) infrared sensor was a first step in de-risking the use of IR technology for objects detection in space. In this context, Cranfield University, SODERN and ESA are collaborating on a research to investigate the potential of IR-based relative navigation for debris removal systems. This paper reports the findings and developments in this field till date and the contributions from the three partners in this research
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