A method of accurate position control with a pneumatic cylinder driving apparatus

In this paper, a method of accurate position control using a pneumatic cylinder driving apparatus is presented. To overcome the effect of friction force and transmission line, low friction type cylinder applied externally pressurized air bearing structure is used and two control valves attached both side of the cylinder directly. To compensate nonlinear characteristics of control valves, linearized control input derived from the relation between control input and effective area of control valve, and dither signal are applied to the valve. The controller applied to the pneumatic cylinder driving apparatus is composed of a state feedback controller and a disturbance observer. Experimental results show that the effectiveness of the proposed method and position control error of 5 μm accuracy could be obtained easily.     

Tunnel lane‐positioning system for autonomous driving cars using LED chromaticity and fuzzy logic system

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three‐lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.     

Parallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition,Inspired by the Human Somatosensory System

Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S₁₁) spectrum, and the applied pressure changes the magnitude of the S₁₁ pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S₁₁ spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability.     

High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video.     

Distributed beamforming with one-bit feedback and clustering for multi-node wireless energy transfer

To resolve energy depletion issues in massive Internet of Things sensor networks, we developed a set of distributed energy beamforming methods with one-bit feedback and clustering for multi-node wireless energy transfer, where multiple single-antenna distributed energy transmitters (Txs) transfer their energy to multiple nodes wirelessly. Unlike previous works focusing on distributed information beamforming using a single energy receiver (Rx) node, we developed a distributed energy beamforming method for multiple Rx nodes. Additionally, we propose two clustering methods in which each Tx node chooses a suitable Rx node. Furthermore, we propose a fast distributed beamforming method based on Tx sub-clustering. Through computer simulations, we demonstrate that the proposed distributed beamforming method makes it possible to transfer wireless energy to massive numbers of sensors effectively and rapidly with small implementation complexity. We also analyze the energy harvesting outage probability of the proposed beamforming method, which provides insights into the design of wireless energy transfer networks with distributed beamforming.     

Improved reutilization of industrial crude lysine to 1,5-diaminopentane by enzymatic decarboxylation using various detergents and organic solvents

World-wide production of l-lysine has rapidly increased in recent years. In the industrial scale production, it is cost effective to minimize waste as many waste materials are generated during downstream processing. Therefore, the conversion of crude lysine to a more valuable product reduces waste emission. In this study, 1,5-diaminopentane (DAP, trivial name: cadaverine) was produced by l-lysine decarboxylation using Hafnia alvei. The conditions of enzymatic reaction were determined. In particular, the addition of specific detergent (Brij 56) was significantly affected in the bioconversion system. Addition of hydrophobic organic solvent improved the mixing of the reactants. Finally, an industrial crude form of lysine served as a substrate. The DAP conversion by analytical, feed and industrial crude l-lysine was 93.9%, 90.3%, and 63.8%, respectively.     

A Single Droplet‐Printed Double‐Side Universal Soft Electronic Platform for Highly Integrated Stretchable Hybrid Electronics

Soft features in electronic devices have provided an opportunity of gleaning a wide spectrum of intimate biosignals. Lack of data processing tools in a soft form, however, proclaims the need of bulky wires or low‐performance near‐field communication externally linked to a “rigid” processor board, thus tarnishing the true meaning of “soft” electronics. Furthermore, although of rising interest in stretchable hybrid electronics, lack of consideration in multilayer, miniaturized design and system‐level data computing limits their practical use. The results presented here form the basis of fully printable, system‐level soft electronics for practical data processing and computing with advanced capabilities of universal circuit design and multilayer device integration into a single platform. Single droplet printing‐based integration of rigid islands and core–shell vertical interconnect access (via) into a common soft matrix with a symmetric arrangement leads to a double‐side universal soft electronic platform that features site‐selective, simultaneous double‐side strain isolation, and vertical interconnection, respectively. Systematic studies of island‐morphology engineering, surface‐strain mapping, and electrical analysis of the platform propose optimized designs. Commensurate with the universal layout, a complete example of double‐side integrated, stretchable 1 MHz binary decoders comprised of 36 logic gates interacting with 9 vias is demonstrated by printing‐based, double‐side electronic functionalization.     

TCP-friendly congestion control for streaming real-time applications over wireless networks

Congestion control for streaming real-time applications, which need smoothness of the transmission rate, should be transmission control protocol (TCP)-friendly. Moreover, in wireless networks, TCP-friendly congestion control should be based on differentiation of packet losses due to congestion and wireless link error to improve network utilisation. The authors propose a TCP-friendly congestion control algorithm based on explicit congestion notification over the wireless networks. The simulation results show that the proposed algorithm utilises the link bandwidth efficiently, providing smoothness of the transmission rate.