Limited Storage Size Research Articles

High-speed readout system of X-ray CMOS image sensor for time domain astronomy

We developed an FPGA-based high-speed readout system for a complementary metal-oxide-semiconductor (CMOS) image sensor to observe soft X-ray transients in future satellite missions, such as HiZ-GUNDAM. Our previous research revealed that the CMOS image sensor has low-energy X-ray detection capability (0.4–4 keV) and strong radiation tolerance, which satisfies the requirements of the HiZ-GUNDAM mission. However, CMOS sensors typically have small pixel sizes (e.g., ∼10 µm), resulting in large volumes of image data. GSENSE400BSI has 2048×2048 pixels, producing 6 Mbyte per frame. These large volumes of observed raw image data cannot be stored in a satellite bus system with a limited storage size. Therefore, only X-ray photon events must be extracted from the raw image data. Furthermore, the readout time of CMOS image sensors is approximately ten times faster than that of typical X-ray CCDs, requiring faster event extraction on a timescale of ∼0.1 s. To address these issues, we have developed an FPGA-based image signal processing system capable of high-speed X-ray event extraction onboard without storing raw image data. The developed compact system enabled mounting on a CubeSat mission, facilitating early in-orbit operation demonstration. Here, we present the design and results of the performance evaluation tests of the proposed FPGA-based readout system. Utilizing X-ray irradiation experiments, the results of the X-ray event extraction with the onboard and offline processing methods were consistent, validating the functionality of the proposed system.

Over-the-Air Computation of Large-Scale Nomographic Functions in MapReduce Over the Edge Cloud Network

Motivated by increasing powerful edge devices with data-intensive computing and limited storage size, we study a MapReduce-based wireless distributed computing framework by allocating a portion of files in the remote data center to the network edge and utilizing computation and memory resources at the edge. Our framework is composed of three step phases: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Map</i> ; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shuffle</i> ; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reduce</i> . However, in the data shuffling stage, shuffling many data accounts for a large amount of the total running time over wireless interference networks will degrade its performance. Moreover, data shuffling between pervasive edge devices with limited spectrum bandwidth is very challenging. Today, many devices focus on computing functions rather than collecting all the individual wireless data centers. Therefore, we can use over-the-air computation (AirComp) technology to reliably compute multiple target functions by harnessing interference in the multiple-access channel with a higher computation efficiency than the traditional orthogonal multiaccess scheme that combats interference. We study a mixed-timescale optimization of the transmitting–receiving (Tx-Rx) policy and file allocation to minimize the averaged computation mean-squared error (MSE) under the power constraint of each device. File allocation control is adaptive to the long-term statistical channel state information (CSI), while the Tx-Rx policy is adaptive to the CSI and file allocation strategy. We decompose the problem into a short-term Tx-Rx policy and a long-term file allocation control problem to tackle the joint nonconvex optimization. Simulation results indicate the effectiveness of our proposed two-timescale algorithm and the advantages of our computation framework over the state-of-the-art baselines.

Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor.

Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms of the past are incapable of obtaining a correct sorted sequence because data to be sorted cannot be totally stored in the data storage. This paper proposes a new sorting algorithm called streaming data sort for streaming data on a uniprocessor constrained by a limited storage size and the correctness of the sorted order. Data continuously flow into the storage as consecutive chunks with chunk sizes less than the storage size. A theoretical analysis of the space bound and the time complexity is provided. The sorting time complexity is O (n), where n is the number of incoming data. The space complexity is O (M), where M is the storage size. The experimental results show that streaming data sort can handle a million permuted data by using a storage whose size is set as low as 35% of the data size. This proposed concept can be practically applied to various applications in different fields where the data always overflow the working storage and sorting process is needed.

Cooperative caching and delivery algorithm based on content access patterns at network edge

Mobile network performance and user Quality of Experience have been negatively affected by the explosion of mobile data traffic. This paper proposes mobile edge caching to alleviate the problem. Recent research has focused on local caching at the wireless edge, as motivated by the 80/20 rule regarding content popularity. By caching popular contents at base stations (BSs) closer to users, backhaul congestion and content access latency can be dramatically reduced. To address the limited storage size of BSs in the context of the massive amount of available content, an algorithm optimizing cooperative caching has been highlighted. Contents requested by mobile users that cannot be obtained locally could be transferred by cooperative BSs. In this paper, we propose a cooperative caching algorithm based on BS content access patterns. We use tensor decompositions with distance constraint to analyze interaction between users, contents and base stations. Thus, BSs with small geographical distances and similar content access patterns constitute a cooperative caching domain. The distributed content placement and delivery algorithm is optimized based on simultaneous consideration of the caching hit ratio and cooperative cost. Simulation results based on a real dataset of usage detail records demonstrate the superior performance and promising practical gains in caching of the proposed caching method compared to user clustering and BS clustering.