Automatic Identification of Braille Blocks by Neural Network Using Multi-Channel Pressure Sensor Array

Wrist pulse has long been recognized as an important physiological symbol for judging the health status of patients. It is regarded as one of the four diagnosis techniques in traditional Chinese medicine and plays an important role in disease analysis. However, the pulse diagnosis skill is subjective and needs decades of experience to master. To make the pulse diagnosis more objective and easily understood by practitioners, many pulse-taking platforms were invented, but several disadvantages, including the simplicity and insufficiency, limit the applications. This paper proposes a novel wrist pulse system for the pulse diagnosis to overcome the shortcomings in previous pulse platforms. We integrated a pressure sensor with a photoelectric sensor to make a fusion sensor to feel pulse in different approaches and designed the multichannel sensor arrays structure to acquire more pulse information. Finally, pulse analysis algorithm and classification methods are introduced. Experiments on disease classification are carried out to test the system performance with multichannel and different sensor arrays. The results show that the novel system is not only able to distinguish between healthy pulse samples and subjects suffering from diabetes, but also good at obtaining more information than the conventional pulse system with single channel or simplex-type sensor.

This paper develops and presents a new neural network approach to model trip distribution, which is one of the important phases of conventional four-step travel demand modelling. The trip distribution problem has been investigated using back-propagation artificial neural networks in a number of studies and it was concluded that back-propagation artificial neural networks underperform when compared to traditional models. Such underperformance is due to the thresholding of the linearly combined inputs by utilising a non-linear function and carrying out this operation both in hidden and output layers. The proposed neural trip distribution model does not threshold the linearly combined outputs from the hidden layer. This makes it different from back-propagation artificial neural networks where combined inputs from the hidden layer are activated once more in the output layer. In addition, the neuron in the output layer is used as a summation unit in contrast to the methodologies cited in the neural network applications literature. At the same time, the bias neuron is not connected to the output neuron in the output layer. When this model is compared with various approaches such as the gravity model, modular neural networks and back-propagation neural networks, it was concluded that this new model provides better prediction of trip distribution and therefore, outperforms all the existing approaches.

In this paper, we propose a passage guidance method for the visually impaired person through a braille block detection. The proposed method recognizes the passage information by detecting the braille blocks individually through a neural network based on a captured image. The braille blocks are detected through YOLOv7 which is a state-of-the-art object detection network. Then, the placements of the detected braille blocks are analyzed to find the groups of the straight blocks arranged in a single line and of the dot blocks gathered in a square shape. The passage information is recognized by comparing between the analyzed block placement and predefined placements. Objects in a sidewalk are detected together with the braille block to warn obstacles on the sidewalk. The passage information is guided in voice for the visually impaired person. In simulation results, the proposed method recognized the passage information with about 85% accuracy.

The chemical elements of proteins are similar to that of DNA (e.g., C, H, O, and N), and DNA shows different knotted architectures. So we imagine that proteins may show a wealth of highly complex structures, especially when proteins interact with each other. The imagination was proved by synthesizing gold nanoparticles (GNPs) to capture the lifelike protein structures. The optical responses (i.e., color) of as-prepared GNPs are found to be characteristic to a given protein (or heavy metal ion). Based on the “three colors” principle of Thomas Young, we extracted the red, green, and blue (RGB) alterations of as-synthesized GNPs to fabricate multichannel sensor arrays for proteins (or heavy metal ions) discrimination. The designed multichannel sensor arrays demonstrate possibilities in semiquantitative analysis of multiple analytes (e.g., proteins and heavy metal ions). This work is believed to open new opportunities for GNPs-based label-free sensing.

Visual impairment often becomes noticeable to others through the visibility of a white cane. This public sign of blindness is an imperative signal that people understand, interpret and act in accordance with; however, the white cane is more than an external iconic aid. This study describes, explains and reflects on movement, assemblages, relationality, companionship, interconnectedness and learning opportunities that resonated across multiple white cane users’ experiences. Biographical pieces of two visually impaired people and their close networks, literature based on authors’ lived experiences and testimonies of the first author who is also visually impaired are brought together into a coherent narrative, where the white cane is the protagonist. This narrative deconstructs the omnipresent metanarrative ‘all blind people always use a white cane’ into a complete evocation that acknowledges the complexity of daily living that materiality and blindness entail. We use the white cane to rethink blindness and open a multisensory and multidimensional understanding.

Introduction Atmospheric corrosion is the main cause of metallic heritage degradation when they are exposed to relative humidity, temperature cycle and air contaminants. Corrosivity maps are one of the preventive strategies for atmospheric corrosion mitigation, but they do not provide enough information about corrosion mechanism, besides that long experimentation periods are required [1]. Moreover, electrochemical techniques are alternatives to speed up results, but their relationship with constantly changing environmental variables is difficult to control.Currently, Artificial Intelligence has been introduced in corrosion engineering studies, because of its capability to make predictions based on big data. Also, experimental data of Electrochemical Impedance Spectroscopy had been used in Artificial Neural Network training to obtain Nyquist Diagrams [2]. The objective of this work is to obtain computational models of Artificial Neural Networks to predict atmospheric corrosion in Bronze with nanostructured patina with SiO2, in marine environments. Method Environmental parameters were taken from database previously reported [3,4], and from Civil Protection Secretariat of Veracruz. Whilst electrochemical evaluation consists in Polarization Resistance and Electrochemical Impedance Spectroscopy; they were carried out in a Gamry Interface 1000 Potentiostat with a Saturated Calomel Reference Electrode (SCE) and a graphite bar as counter electrode in an agar gelled cell. Meanwhile, working electrode was bronze with nanostructured patina with SiO2. Two patina were prepared, the first with CuSO4 solution (0.015 mol L-1) and the second applying a CuNO3 solution (20% wt).The 13 variables used in the database for Artificial Neural Networks (ANN) training were: exposure time, chloride concentration, sulfur compounds concentration, relative humidity, precipitation level, windspeed, temperature, nanocoating presence, corrosion potential, corrosion rate, frequency, real and imaginary component of impedance. The values of each variable were treated with measures of central tendency and dispersion, as well as correlation matrix and histograms, which allowed to find a relationship between variables, and choosing the inputs for ANN. The prediction models consisted in 5, 9, 10 and 12 neurons in the input layer (R5E, R9E, R10E and R12E, respectively), a hidden layer, and Zima as only neuron in the output layer. The number of neurons in hidden layer varied from 1 to 8 until the highest coefficient of determination was achieved. The computational model was performed using the toolbox in MATLAB with a feedforward ANN. It is worth mention that the hidden and output layers used a hyperbolic tangent and a linear transfer function, respectively, and a Levenberg-Marquardt algorithm as training function was used in the models. Results and Conclusions Figure 1 presents the coefficient of determination (R2) and root mean squared error (RMSE) with different ANN architectures. In the four models, the highest value was reached with 8 neurons in the hidden layer. Then, the linear regressions for R5E, R9E, R10E and R12E models and 8 neurons in the input layer were compared in Figure 2. In that sense, the Nyquist Diagrams in Figure 3 were simulated with the R9E and R12E models. Also, they were compared with experimental Nyquist Diagrams for Bronze with patina of CuSO4 and CuNO3, with and without SiO2 nanoparticles, at 56 days of exposure in marine atmospheres. The simulations demonstrated how an ANN model with 12 neurons in the input layer, 8 neurons in the hidden layer and 1 neuron in the output layer can be used as a corrosion prediction model of bronze with nanostructured patina with SiO2.

Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period (1986-2012) were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013-2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range.

When the visually impaired people go out alone, they rely on white canes and Braille blocks. However, there are problems on the route, such as obstacles on the sidewalk Braille blocks. As a result, a traffic accident or a fall accident from the station’s platform has occurred. That is the cause many cases where people go out without worrying about troubles. In this research and development, those obstacles are detected using a stereo camera linked to a smartphone, and the content is guided to the user by a wearable speaker. In addition, the location of the user is captured by GPS and guided to the destination. There are two stages, one for “Mobility Support Service” and “Watch Over Service”.

Aiming at promptly and accurately detecting falls and drag-to gaits induced by asynchronous human-robot movement speed during assisted walking, a noncontact interactive approach with generality, compliance and safety is proposed in this paper, and is applied to a wheeled walking aid robot. Firstly, the structure and the functions of the wheeled walking aid robot, including gait rehabilitation robot (GRR) and walking aid robot (WAR) are illustrated, and the characteristic futures of falls and the drag-to gait are shown by experiments. To obtain gait information, a multichannel proximity sensor array is developed, and a two-dimensional gait information detection system is established by combining four proximity sensors groups which are installed in the robot chassis. Additionally, a node-iterative fuzzy Petri net algorithm for abnormal gait recognition is proposed by generating the network trigger mechanism using the fuzzy membership function. It integrates the walking intention direction vector by taking gait deviation, frequency, and torso angle as input parameters of the system. Finally, to improve the compliance of the robot during human-robot interaction, a PID_SC controller is designed by integrating the gait speed compensation, which enables the WAR to track human gait closely. Abnormal gait recognition and assisted walking experiments are carried out respectively. Experimental results show that the proposed algorithm can accurately identify abnormal gaits of different groups of users with different walking habits, and the recognition rate of abnormal gait reaches 91.2%. Results also show that the developed method can guarantee safety in human robot interaction because of user gate follow-up accuracy and compliant movements. The noncontact interactive approach can be applied to robots with similar structure for usage in walking assistance and gait rehabilitation.

This paper evaluates general purpose processor architecture for efficient implementation of signal processing algorithms to meet the demands of a real-time system. A multi-channel linear sensor array configuration has been considered for underwater surveillance and detection purpose. The paper defines mathematical formulations for calculating the signal processing requirements in terms of data rate and processor loading i.e. FLOPS (floating point operations) that help in selecting an appropriate processor having suitable capacity to carry out the computationally intensive processing algorithms. The paper presents execution times of signal processing algorithms like beam forming and spectral analysis on modern Intel processors that include Core2Quad and Core i7 processors. The results show a considerable reduction in the execution times on Intel Core i7 processor using multithreaded programming approach. An example of DSP architecture has also been discussed to establish the perception of implementing signal processing algorithms on latest Intel processors.

A new low-cost design for TDM interferometric interrogation of multi-channel fiber sensor arrays is presented. Interrogation elements comprise all-SMF components, SOA based optical switching for high optical extinction, and a new, highly efficient demodulation approach.

Rapid visual authentication of high-temperature Daqu Baijiu using porphyrin signal amplification and smartphone-based cloud machine learning.

While recent advances have successfully integrated neural networks with physical models to derive numerical solutions, there remains a compelling need to obtain exact analytical solutions. The ability to extract closed-form expressions from these models would provide deeper theoretical insights and enhanced predictive capabilities, complementing existing computational techniques. In this paper, we study the nonlinear Gardner equation and the (2+1)-dimensional Zabolotskaya–Khokhlov model, both of which are fundamental nonlinear wave equations with broad applications in various physical contexts. The proposed models have applications in fluid dynamics, describing shallow water waves, internal waves in stratified fluids, and the propagation of nonlinear acoustic beams. This study integrates a modified generalized Riccati equation mapping approach and a novel generalized G′G-expansion method with neural networks for obtaining exact solutions for the suggested nonlinear models. Researchers are currently investigating potential applications of these neural networks to enhance our understanding of complex physical processes and to develop new analytical techniques. The proposed strategies incorporate the solutions of the Riccati problem into neural networks. Neural networks are multi-layer computing approaches including activation and weight functions among neurons in input, hidden, and output layers. Here, the solutions of the Riccati equation are allocated to each neuron in the first hidden layer; thus, new trial functions are established. We evaluate the suggested models, which lead to the construction of exact solutions in different forms, such as kink, dark, bright, singular, and combined solitons, as well as hyperbolic and periodic solutions, in order to verify the mathematical framework of the applied methods. The dynamic properties of certain wave-related solutions have been shown using various three-dimensional, two-dimensional, and contour visualizations. This paper introduces a novel framework for addressing nonlinear partial differential equations, with significant potential applications in various scientific and engineering domains.

Now knowledge pre-processing, model and reasoning issues, power metrics, quality issues, post-processing of discovered structures, visualization, and on-line change is best challenge. In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the markets(9). The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform intelligent tasks similar to those performed by the human brain. Artificial Neural Networks are being counted as the wave of the future in computing. They are indeed self-learning mechanisms which don't require the traditional skills of a programmer. Back propagation is one of the approaches to implement concept of neural networks. Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most often used as training algorithm in current neural network applications. In this paper, we apply data mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy.

Summary In this work, the artificial neural network (ANN) is implemented for prediction slug frequency FS for low viscosity μL flow (μL ≤ 1.6 mPa·s) in vertical, horizontal, and inclined pipes. To the authors' knowledge, no ANN model in the literature exists for predicting FS. The input parameters for the suggested ANN are superficial liquid velocity VSL, pipe diameter D, superficial gas velocity VSG, and pipe inclination ϕ. Measured data (450 data points) are gathered from five different resources for developing the ANN model. The ranges of FS, VSL, D, VSG, and ϕ covered by the data set, are (0.03 to 3.167 1/s), (0.05 to 2.073 m/s), (0.01905 to 0.0779 m), (0.133 to 11.84 m/s), and (0 to 90°), respectively. The most popular transfer functions of tangent sigmoid and linear are used in the hidden and output layers, respectively, whereas the Levenberg and Marquardt back propagation algorithm is conducted to train ANN. The experimental data set is divided into 70% for training, 15% for validation, and 15% for testing processes. Due to the absence of a systematic way to find the optimal structure of ANN, an exhaustive search method has been suggested and implemented to find the optimal topology, which is (4-16-1); four neurons in input layer, 16 neurons in the hidden layer, and one neuron in output layer. The proposed ANN predicts correctly the effect of each of the previously mentioned parameters on FS, and it yields a satisfactory prediction and clearly outperforms all the existing models, with a mean square error (MSE) and R2 of 0.0097 and 0.977, respectively.