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Edge computing enables rapid data processing and decision-making on satellite payloads. Deploying deep learning-based techniques for low-light image enhancement improves early detection and tracking accuracy on satellite platforms, but it faces challenges due to limited computational resources. This article proposes an edge-computing-enabled inference model specifically designed onboard satellites. The proposed model follows an encoder–decoder architecture to generate the illumination map with low multiplication matrix complexity, 25.52 GMac of $1920 \times 1200$ image size. To reduce nanosatellite hardware consumption with a single-precision floating-point format, the edge-computing-enabled inference model proposes a quantized convolution that computes signed values. The proposed inference model is deployed on Arm Cortex-M3 microcontrollers onboard satellite payload (86.74 times faster than normal convolution model) but also has a similar quality with the low-light enhanced in full-precision computing of lightweight training model by using the peak signal-to-noise ratio (average of 28.94) and structural similarity index (average of 0.85) metrics.

Onboard real-time processing of remote sensing satellites is an important means of rapidly obtaining information, and the fusion processing of panchromatic and multispectral data is of great significance for optical satellites. In order to ensure the effect, most traditional algorithms perform statistical analysis or transformation on the entire image first and then perform subsequent processing. There are problems such as high algorithm complexity and resource occupation, and it is difficult to apply to onboard scenarios where the volume and power consumption are strictly limited. Aiming at the requirements of onboard fusion, a real-time processing approach for high-resolution optical satellites is proposed. First, through the implementation of real-time geometric positioning, ROI extraction is completed while the camera is imaging, avoiding the disadvantages of traditional methods for processing large amounts of data; then, based on the principle of object-space consistency, by fine-tuning virtual sensor parameters, the registration of a panchromatic multispectral image is completed in the sensor correction step, so that the relative accuracy of the two can meet the fusion requirements, and time-consuming pixel-level registration processing is avoided; finally, according to the characteristics of the algorithms and embedded hardware, an efficient algorithm mapping strategy is formulated, and deep optimization is implemented to achieve a significant improvement in performance. Experiments show that the performance of this method is improved by 156.23 times compared with the traditional method. Moreover, after building a parallel pipeline, it can meet the real-time fusion processing requirement of completing a 5000 × 5000 pixels ROI area every 2.4 s.

Given the constant demand for heavier, longer, faster, and more efficient rail freight vehicles, onboard fault detection systems appear as a good approach for enhanced railway asset exploitation. Real-time condition monitoring reduces inefficient preventive and reactive maintenance actions, decreases waste from replacing parts that still have a useful life, and improves availability and safety by real-time rolling stock diagnosis. There have been considerable advances in wayside monitoring applications, but these cannot achieve real-time continuous monitoring. With the price reduction and miniaturization trends of electronic devices, the cost of deploying wireless sensor networks onboard freight trains continues to become more feasible and accessible. On the other hand, the lack of onboard electric power availability on freight wagons appears as the major limitation for the implementation of these technologies. This paper reviews recent onboard condition monitoring sensors, systems, methods and techniques, aiming to define the present state of the art and its potential application for freight wagons without onboard electric power. [ABSTRACT FROM AUTHOR]

Significant advances in spaceborne imaging payloads have resulted in new big data problems in the Earth Observation (EO) field. These challenges are compounded onboard satellites due to a lack of equivalent advancement in onboard data processing and downlink technologies. We have previously proposed a new GPU accelerated onboard data processing architecture and developed parallelised image processing software to demonstrate the achievable data processing throughput and compression performance. However, the environmental characteristics are distinctly different to those on Earth, such as available power and the probability of adverse single event radiation effects. In this paper, we analyse new performance results for a low power embedded GPU platform, investigate the error resilience of our GPU image processing application and offer two new error resilient versions of the application. We utilise software based error injection testing to evaluate data corruption and functional interrupts. These results inform the new error resilient methods that also leverages GPU characteristics to minimise time and memory overheads. The key results show that our targeted redundancy techniques reduce the data corruption from a probability of up to 46 percent to now less than 2 percent for all test cases, with a typical execution time overhead of 130 percent. [ABSTRACT FROM AUTHOR]