High-accuracy Position-aware Robot for Agricultural Automation using Low-cost IMU-coupled Triple-laser-guided (TLG) System

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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Deep Learning-based Selection Strategy of Polling Switch for Low-cost and High-accuracy Flow Statistics Collection in SDN

2020 International Conference on Internet of Things and Intelligent Applications (ITIA) ◽

10.1109/itia50152.2020.9312376 ◽

2020 ◽

Author(s):

Zhigeng Han ◽

Yiru Yao

Keyword(s):

Deep Learning ◽

Low Cost ◽

High Accuracy ◽

Selection Strategy

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High accuracy low-cost videogrammetric system: an application to 6DOF estimation of ship models

10.1117/12.2020922 ◽

2013 ◽

Author(s):

Erica Nocerino ◽

Fabio Menna ◽

Salvatore Troisi

Keyword(s):

Low Cost ◽

High Accuracy

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Design and Implement of High-Accuracy On-Line Metal Thickness Measuring Instrument Based on NiosII Processor

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.834-836.930 ◽

2013 ◽

Vol 834-836 ◽

pp. 930-934

Author(s):

Shou Liang Yang ◽

Bao Liang Yang

Keyword(s):

Eddy Current ◽

Low Cost ◽

High Accuracy ◽

Resolving Power ◽

Current Sensor ◽

Measuring Instrument ◽

Precision Data ◽

Eddy Current Sensor ◽

Metal Thickness ◽

On Line

The paper proposes a new design of high-accuracy On-line Metal Thickness Measuring Instrument, which was based on EP2C20 series FPGA chip, through adding NiosII soft processor and other interfaces to FPGA, equipped with high precision data collection system and TFT LCD module and so on. The key hardware blocks schematics and components of the RC Oscillation Circuit,eddy current sensor Circuit,rectifier and filter Circuit,A/D converting circuit,FPGA Circuit are described,software flow charts and sample codes are given. According to practice, The measurement range of this system is 1~100 mm and the resolving power is 0.1 μm. degree of linearity is 1%, The system has many features including small volume of hardware, low cost, high detecting precision, convenient operating, high intelligent and so on, leading to broad and bright future. Key words: NiosII processor; eddy current sensor; metal thickness

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Design and Development of a 5-Channel Arduino-Based Data Acquisition System (ABDAS) for Experimental Aerodynamics Research

Sensors ◽

10.3390/s18072382 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2382 ◽

Cited By ~ 12

Author(s):

Antonio Vidal-Pardo ◽

Santiago Pindado

Keyword(s):

Data Acquisition ◽

Low Cost ◽

Data Acquisition System ◽

High Accuracy ◽

Acquisition System ◽

Calibration System ◽

Experimental Aerodynamics ◽

Accuracy Level ◽

The Fourier Transform ◽

Cup Anemometer

In this work, a new and low-cost Arduino-Based Data Acquisition System (ABDAS) for use in an aerodynamics lab is developed. Its design is simple and reliable. The accuracy of the system has been checked by being directly compared with a commercial and high accuracy level hardware from National Instruments. Furthermore, ABDAS has been compared to the accredited calibration system in the IDR/UPM Institute, its measurements during this testing campaign being used to analyzed two different cup anemometer frequency determination procedures: counting pulses and the Fourier transform. The results indicate a more accurate transfer function of the cup anemometers when counting pulses procedure is used.

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CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

Journal of Information and Communication Technology ◽

10.32890/jict2019.18.1.8277 ◽

2018 ◽

Author(s):

Quang Thanh Tran ◽

Li Jun Hao ◽

Quang Khai Trinh

Keyword(s):

Network Traffic ◽

Cellular Network ◽

Low Cost ◽

Real Data ◽

Exponential Smoothing ◽

High Accuracy ◽

Training Data ◽

Traffic Prediction ◽

Data Traffic ◽

Smoothing Methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.

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Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽

10.3390/inventions6040070 ◽

2021 ◽

Vol 6 (4) ◽

pp. 70

Author(s):

Elena Solovyeva ◽

Ali Abdullah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Networks ◽

Low Cost ◽

Computational Cost ◽

High Accuracy ◽

Activation Functions ◽

Fully Connected ◽

Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

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