Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications

Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.

An Improved Heap-Based Optimizer for Optimal Design of a Hybrid Microgrid Considering Reliability and Availability Constraints

The hybrid microgrid system is considered one of the best solution methods for many problems, such as the electricity problem in regions without electricity, to minimize pollution and the depletion of fossil sources. This study aims to propose and implement a new algorithm called improved heap-based optimizer (IHBO). The objective of minimizing the microgrid system cost is to reduce the net present cost while respecting the reliability, power availability, and renewable fraction factors of the microgrid system. The results show that the PV/diesel/battery hybrid renewable energy system (HRES) gives the best solution, with a net present cost of MAD 120463, equivalent to the energy cost of MAD 0.1384/kWh. The reliability is about 3.89%, the renewable fraction is about 95%, and the power availability is near to 99%. The optimal size considered is represented as 167.3864 m2 of PV area, which is equivalent to 44.2582 kW and 3.8860 kW of diesel capacity. The study results show that the proposed optimization algorithm of IHBO is better than the artificial electric field algorithm, the grey wolf optimizer, Harris hawks optimization, and the original HBO algorithm. A comparison of the net present cost with a different fuel price is carried out, in which it is observed that the net present cost is reduced even though its quantity used is mediocre.

Solar Energy Applications in Agriculture with Special Reference to North Western Himalayan Region

The UN Sustainability Goals emphasise on use of renewable sources of energy viz wind, solar, hydro power, biomass etc which are increasingly becoming important in the global energy mix. India with a 900 GW potential, aims to have 175 GW by 2022 and about 40% of total power production from renewable sources by 2030 with solar source contributing the most (83 %). Solar energy is the most fundamental renewable energy resource with many agricultural applications. The abundance of solar energy makes it suitable for electricity and thermal applications and hence can be used in agriculture in photovoltaic electricity generation, powering irrigation, crop and grain drying, pesticide application, green house heating and ventilation, cold storages etc. North western Himalayan regions are energy-poor with high energy requirements. Low ambient temperature, high Global Horizontal Irradiance (GHI) and Direct Net Irradiance (DNI) of 4.8-6.43 kWh per square metre per day indicate huge solar potential, higher solar photovoltaic electricity and solar thermal production efficiency. Solar energy can replace or supplement conventional sources used for domestic and agricultural applications in the region. However, the use of solar energy is limited by policy and regulatory obstacles, financial obstacles, land availability constraints and low PV conversion efficiency. Hence a robust policy, financial measures and technological refinement are needed to remove the bottlenecks. In this paper, attempts have been made to discuss solar energy use in agriculture, scope in the north western Himalayan region of India and future recommended strategies.

An Arctic transformation; from an in-person international summer school to a digital MOOC

<p>The Arctic, Nordic, Scandinavian and “global north” regions have, individually and collectively, gained increased public, political, commercial, and academic interest over the last decade. For example, regarding issues ranging from climate change to polar ecosystems, and from shipping routes to indigenous knowledge. As such, there is an increasing demand for state-of-the-art knowledge about the region from truly interdisciplinary viewpoints and multi-scale perspectives (e.g. past, present and future changes, as well as feedbacks between and within the environment and society).</p><p>To address such issues, members of the University of Oslo (UiO) and the <strong>UiO International Summer School (ISS) developed an interdisciplinary MSc-level course, titled "A Changing Arctic" </strong>[1] worth 15 ECTS. The course was structured around three major modules with the opportunity for cross-thematic discussions and knowledge transfer;  Natural Sciences and Technology, Law and Legal Regimes, and Governance and Society. From 2014-2018, for 6-weeks over the northern hemisphere summer, this in-person course welcomed between 15-25 enrolled students annually. It was coordinated by representatives from the Faculties of Law, Natural Sciences (PI from the Department of Geosciences), and Humanities, and also involved a number of guest lecturers from Norway, Europe, and internationally.</p><p>Since 2018, we have been in discussions to develop an additional digital, or hybrid (in-person and online), version of the course to alleviate financial and summertime availability constraints. In 2020, the pandemic further brought to light the need for more flexible, wide-reaching teaching options. A “MOOC” - Massive Open Online Course - offers a <strong>framework for a formal, high quality, free and widely accessible educational resource</strong>. This particularly exciting avenue for reaching people in remote Arctic areas, those who do not fit the traditional university-admissions profiles, as well as people in the global south who may not be familiar with northern processes.</p><p>In 2020, we secured funding from UArctic, and other partners, to begin this process, and as of Jan 2021 have begun with digital course preparations for a interdisciplinary Arctic MOOC to be released in late 2021 (in addition to an ISS enrolled-student stream in summer 2021). We aim to share some of the opportunities and challenges associated with this transition, including coordinating a very large thematic project and many international lecturers/contributers, switching from in-person lectures to "flipped-classroom" and video-style lectures, interdisciplinary pedagogical considerations, Nordic educational frameworks, financial challenges and funding opportunities, typical student profiles, as well as more practical filming and digital elements.</p><p>[1] https://www.uio.no/studier/emner/iss/sommerskolen/ISSMN4030/index.html </p>

Hybrid Model for PAPR Minimization in OFDM System

The technology has been growing rapidly in the form of portable wireless devices that can perform multiple functions to cope with the state-of-the-art technology and synchronization. A total device capacity must be increased to accommodate new wireless applications. This can be achieved by leveraging new technologies, with higher data rates. Spectrum pooling has gained immense popularity, with increased demand for frequency range and bandwidth availability constraints. Statistics suggest that much of the spectrum licensed is not used all the time. Because of the transmitter's nonlinearity nature, the large (peak to average power ratio (PAPR)) phenomenon is a drawback in orthogonal frequency division multiplexing (OFDM). Several hybrid approaches have recently been implemented to minimize PAPR's high value, at the expense of increasing the level of computational complexity in the system. In this paper, a new hybrid approach has been introduced in parallel to combine the selective mapping approach (SLM) with the partial transmit sequence (PTS) approach to improve the efficiency of PAPR reduction with lower numerical method complexity. The findings reveal that the OFDM systems with the proposed hybrid approach have better efficiency in terms of PAPR elimination, side-information, and computational complexity compared to current hybrid methods. Also, a hybrid approach proposed output could be maintained without degradation. Index Terms: OFDM, PTS, PAPR, SLM