A Cost Effective Block Framing Scheme for Underwater Communication

In this paper, the Selective Multiple Acknowledgement (SMA) method, based on Multiple Acknowledgement (MA), is proposed to efficiently reduce the amount of data transmission by redesigning the transmission frame structure and taking into consideration underwater transmission characteristics. The method is suited to integrated underwater system models, as the proposed method can handle the same amount of data in a much more compact frame structure without any appreciable loss of reliability. Herein, the performance of the proposed SMA method was analyzed and compared to those of the conventional Automatic Repeat-reQuest (ARQ), Block Acknowledgement (BA), block response, and MA methods. The efficiency of the underwater sensor network, which forms a large cluster and mostly contains uplink data, is expected to be improved by the proposed method

High-Throughput Screening of Acyl-CoA Thioesterase I Mutants Using a Fluid Array Platform

Screening target microorganisms from a mutated recombinant library plays a crucial role in advancing synthetic biology and metabolic engineering. However, conventional screening tools have several limitations regarding throughput, cost, and labor. Here, we used the fluid array platform to conduct high-throughput screening (HTS) that identified Escherichia coli ???TesA thioesterase mutants producing elevated yields of free fatty acids (FFAs) from a large (106) mutant library. A growth-based screening method using a TetA-RFP fusion sensing mechanism and a reporter-based screening method using high-level FFA producing mutants were employed to identify these mutants via HTS. The platform was able to cover >95% of the mutation library, and it screened target cells from many arrays of the fluid array platform so that a post-analysis could be conducted by gas chromatography. The ???TesA mutation of each isolated mutant showing improved FFA production in E. coli was characterized, and its enhanced FFA production capability was confirmed

A Novel Method for Service Differentiation in IEEE 802.15.4 : Priority Jamming

IEEE 802.15.4 employs carrier sense multiple access with collision avoidance (CSMA/CA), which is known to have difficulty for supporting quality of service (QoS). In this paper, a new priority scheme called priority jamming (PJ) is proposed for service differentiation for IEEE 802.15.4. The main idea of the proposed scheme is deferring the low priority packet transmission for the high priority packet. Clear channel assessment of IEEE 802.15.4 is modified to support the proposed PJ. The efficiency of the proposed scheme is validated by comparing the delay, throughput, and energy-per-bit with those of standard CSMA/CA. Simulations results showed that PJ improves the delay and throughput simultaneously while maintaining marginal difference in energy efficiency

An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

Advancements in artificial intelligence (AI) gives a great opportunity to develop an autonomous devices. The contribution of this work is an improved convolutional neural network (CNN) model and its implementation for the detection of road cracks, potholes, and yellow lane in the road. The purpose of yellow lane detection and tracking is to realize autonomous navigation of unmanned aerial vehicle (UAV) by following yellow lane while detecting and reporting the road cracks and potholes to the server through WIFI or 5G medium. The fabrication of own data set is a hectic and time-consuming task. The data set is created, labeled and trained using default and an improved model. The performance of both these models is benchmarked with respect to accuracy, mean average precision (mAP) and detection time. In the testing phase, it was observed that the performance of the improved model is better in respect of accuracy and mAP. The improved model is implemented in UAV using the robot operating system for the autonomous detection of potholes and cracks in roads via UAV front camera vision in real-time.Comment: 10 pages, 11 figures, journa