The field of nanosatellites is constantly evolving and growing at a very fast speed. This creates a growing demand for more advanced and reliable EDAC systems that are capable of protecting all memory aspects of satellites. The Hamming code was identified as a suitable EDAC scheme for the prevention of single event effects on-board a nanosatellite in LEO. In this paper, three variations of Hamming codes are tested both in Matlab and VHDL. The most effective version was Hamming [16, 11, 4]2. This code guarantees single-error correction and double-error detection. All developed Hamming codes are suited for FPGA implementation, for which they are tested thoroughly using simulation software and optimized.
Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.
This article starts by introducing the ongoing South Africa electricity crisis followed by thermoelectricity, in which eighteen miscellaneous applicable case studies are structurally analysed in detail. The aim is to establish best practices for the R&D of an efficient thermoelectric (TE) and fuel cell (FC) CCHP system. The examined literature reviews covered studies that focused on the thermoelectricity principle, highlighting TE devices’ basic constructions, TEGs and TECs as well as investigations on the applications of thermoelectricity with FCs, whereby thermoelectricity was applied to recover waste heat from FCs to boost the power generation capability by ~7–10%. Furthermore, nonstationary TEGs whose generated power can be increased by pulsing the DC-DC power converter showed that an output power efficiency of 8.4% is achievable and that thicker TEGs with good area coverage can efficiently harvest waste heat energy in dynamic applications. TEG and TEC exhibit duality and the higher the TEG temperature difference, the more the generated power—which can be stabilised using the MPPT technique with a 1.1% tracking error. A comparison study of TEG and solar energy demonstrated that TEG generates more power compared to solar cells of the same size, though more expensively. TEG output power and efficiency in a thermal environment can be maximised simultaneously if its heat flux is stable but not the case if its temperature difference is stable. The review concluded with a TEC LT-PEM-FC hybrid CCHP system capable of generating 2.79 kW of electricity, 3.04 kW of heat, and 26.8 W of cooling with a total efficiency of ~77% and fuel saving of 43.25%. The presented research is the contribution brought forward, as it heuristically highlights miscellaneous thermoelectricity studies/parameters of interests in a single manuscript, which further established that practical applications of thermoelectricity are possible and can be innovatively applied together with FC for efficient CCHP applications.
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