An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Resistive flex sensors can be used to measure bending or flexing with relatively little effort and a
relativelylow budget. Their lightness, compactness, robustness, measurement effectiveness and
low power consumption make these sensors useful for manifold applications in diverse fields.
Here, we provide a comprehensive survey of resistive flex sensors, taking into account their
working principles, manufacturing aspects, electrical characteristics and equivalent models,
useful front-end conditioning circuitry, and physic-bio-chemical aspects. Particular effort is
devoted to reporting on and analyzing several applications of resistive flex sensors, related to the
measurement of body position and motion, and to the implementation of artificial devices. In
relation to the human body, we consider the utilization of resistive flex sensors for the
measurement of physical activity and for the development of interaction/interface devices driven
by human gestures. Concerning artificial devices, we deal with applications related to the
automotive field, robots, orthosis and prosthesis, musical instruments and measuring tools. The
presented literature is collected from different sources, including bibliographic databases,
company press releases, patents, master’s theses and PhD theses.

https://iopscience.iop.org/article/10.1088/0964-1726/25/1/013001/pdf

Resistive flex sensors: a survey

Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples and the recognition capability of gesture samples in complex background needs to be improved.

/pdf/jointly-network-a-network-based-on-cnn-and-rbm-for-gesture-3b5rcortsw.pdf

Jointly network: a network based on CNN and RBM for gesture recognition

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO2, NO2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture.

IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

Highway obstacle detection is one of the most challenging task in real time for autonomous vehicle navigation system. The basic idea is to design an effective system for real time environment, which detects the presence of obstacles in the track of the vehicle. In the proposed work Raspberry Pi Camera module is employed for object detection and image acquisition. A thorough investigation is performed on a test image in order to validate the best algorithm suitable for edge detection of images. Sufficient analysis is performed to consolidate the results.

Raspberry - pi based cost effective vehicle collision avoidance system using image processing

Accelerometer Based Static Gesture Recognition and Mobile Monitoring System Using Neural Networks

Background/Objectives: PID controllers are one of the first solutions often considered in the control of process industries. It has always been very difficult to treat the level anomalies n the real time processes. Particularly the non linear systems and processes have been a challenge in relation with their dynamics and flow properties. Methods/Statistical analysis: Dual Spherical Tank Liquid Level System (DSTLLS) has the characteristics of nonlinearity due to the dynamic behaviour and area of cross section of tank. One of the major problems in process industries is to control of liquid level for Second Order System Plus Delay (SOSPD) system because the presence of time delay in the system can lead to destabilization of the system. This simulation aims at portraying the performance of the designed Internal Model Control (IMC) PID Controller. Using the black box modelling technique the SOSPD is mathematically modelled experimentally, assuming the system to be a Single Input Single Output (SISO) model. Findings: The aim of this paper is to design a PID controller using IMC tuning method for a (DSTLLS) whose modelling has been done in real time. This simulation briefly explains about stabilizing problem for SOSPD system using an IMC PID controller. The designed controller performance is analysed in terms of performance indices like Integral Squared Error (ISE) and Integral Absolute Error (IAE) and time domain specifications like Rise time, Settling time and Peak time. Conclusion: The validation of the performance of the designed controller is performed under MATLAB environment. There can different controllers which can be also experimented on the same mathematical model for different configurations of the system, namely MISO and MIMO.

Design and Simulation of Internal Model Controller for a Real Time Nonlinear Process

In this paper a real time Single Spherical Tank Liquid Level System (SSTLLS) has been chosen for investigation. This paper describes the design and development of a Multi Model PI Controller (MMPIC) using classical controller tuning techniques for a single spherical nonlinear tank system. System identification of these different regions of nonlinear process are done using black box modeling, which is identified to be nonlinear and approximated to be a First Order Plus Dead Time (FOPDT) model. A proportional and integral controller is designed using LabVIEW and Chen-Hrones-Reswick (CHR), Zhuang-Atherton (ZA), and Skogestad’s Internal Model Controller (SIMC) tuning methods are implemented in real time. The paper provides the details about the data acquisition unit, shows the implementation of the controller, and comparision of the results of PI tuning methods used for an MMPI Controller.

Experimental Validation of a Multi Model PI Controller for a Non Linear Hybrid System in LabVIEW

Developing successful embedded vision applications necessitates a detailed review of various algorithmic optimization trade-offs and a wide variety of hardware design options. This makes it difficult for developers to navigate the solution space and find design points with the best performance trade-offs. In neural machine translation, large Transformer frameworks have produced state-of-the-art results and have become the industry standard. We will clarify the mathematical mechanisms behind the paper's efficient interpretation for neural machine translations and look for the best combination of known techniques to improve interpretation speed without compromising recognition accuracy in this paper. We perform an empirical study that compares various approaches and shows that using a hybrid of decoder self-attention replacement with simplified recurrent units, a deep encoder and shallow decoder architecture, and multiple head attention reseeding can achieve up to higher accuracy. By replacing heavy functions with lighter ones and enhancing the autoencoder's layers structure, excellent results can be achieved in a harmonious mix of time series, network architecture, and probabilistic solutions.

M. Kalyan Chakravarthi

Papers

Raspberry - pi based cost effective vehicle collision avoidance system using image processing

Accelerometer Based Static Gesture Recognition and Mobile Monitoring System Using Neural Networks

Design and Simulation of Internal Model Controller for a Real Time Nonlinear Process

Experimental Validation of a Multi Model PI Controller for a Non Linear Hybrid System in LabVIEW

Efficient Classification for Neural Machines Interpretations based on Mathematical models