With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture "client-cloud" face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture. By offloading part of the computing tasks to the edge server closer to the data source, edge computing resources are used for image preprocessing to reduce the number of images to be transmitted, thus reducing the network transmission overhead. Moreover, the application code does not need to be rewritten and can be easily migrated to the edge server. We evaluate our schemes based on the open source Azure IoT Edge, and the experimental results show that the three-tier architecture "Client-Edge-Cloud" face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.
Background
Multiprotein bridging factor 1 are transcription factors that play critical roles in plant life cycle and in plant tolerance to environmental stresses. Medicago sativa is an important perennial legume forage grass, whereas the potential information in the MBF1 genes associated with stress resistance remains poorly understood.
Results
Three MBF1 genes were identified from each of the M. truncatula and M. sativa genomes. Multiple sequence alignment analysis showed that all these members contain conserved MBF1 and HTH domains. The MBF1 genes showed similar exon-intron organizations, and similar architectures in their conserved motifs. A number of cis-acting elements associated with drought, MeJA and light stress were identified in their promoter regions. In addition, these MBF1 genes were shown in genechip and transcriptome data to exhibit divergent expression patterns in various tissues or in response to drought and salt treatments. In particular, qRT-PCR results showed that the expression of MtMBF1b and MtMBF1c were significantly induced by NaCl treatment, indicating that they are likely to play a role in salt stress response.
Conclusions
Our comprehensive analysis provides valuable information for elucidating the evolutionary process of MBF1 genes and their expression patterns in different tissues and under four stresses. This work will facilitate the application of MBF1 genes in molecular breeding of highly resistant alfalfa.
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