A supervised deep feedforward neural network (SDFNN)-based image reconstruction algorithm for radio tomographic imaging

Radio tomographic imaging (RTI) is an emerging imaging technique that utilizes the shadowing losses on links between multiple pairs of wireless nodes within the sensing area to estimate the attenuation of physical objects. By using an image reconstruction algorithm, the attenuations caused by the physical objects will be transformed into a tomographic image. The tomographic image provides information about the shape, size and position of an object. However, the process of reconstructing a tomographic image from the RSS measurements is an ill-posed inverse problem, meaning that a small number of errors or variations in measurements will lead to a significant impact on the image quality. The existing linear inverse solvers provide fast reconstruction, but the imaging results is non-satisfactory and inaccurate. On the other hand, the nonlinear inverse solvers produce a higher quality image but are computationally expensive. Studies of applying deep learning technique and neural networks in tomographic reconstructions to solve the ill-posed inverse problems have emerged in recent years. However, to the best of our knowledge, the studies conducted in solving the inverse problem of RTI system using deep learning technique are rare. Therefore, a supervised deep feedforward neural network (SDFNN)-based image reconstruction algorithm for the RTI system is explored in this study to determine the feasibility of deep learning technique in reconstructing a tomographic image using RSS measurements only

A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system

Image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system. It is commonly assumed that increasing the number of sensors in a tomography system will improve the ill-posed condition in image reconstruction and hence improve image quality. However, as the number of sensors increases, challenges such as more complicated and expensive hardware, slower data acquisition rates, longer image reconstruction times, and larger sensitivity matrices will arise, resulting in a greater ill-posed condition. Since deep learning (DL) is capable of expressing complex nonlinear functions, the majority of research efforts have been directed toward developing a robust DL-based inverse solver for image reconstruction. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for a large-scale tomography system. This paper proposed an image reconstruction algorithm based on Deep Neural Networks (DNN) to investigate its feasibility in solving the ill-posed inverse problem caused by the reduced sensor model for a large-scale tomography system. The proposed DNN model is based on a supervised, feed-forward, fully connected, backpropagation network. It comprises an input layer, three hidden layers and an output layer. Also, it was trained using large data samples obtained from COMSOL simulation. The relationship between the scattered electromagnetic field measurement and the corresponding true electromagnetic field distribution vector is determined. During the image reconstruction process, the untrained scattered electromagnetic field measurement samples are used as inputs to the trained DNN model, and the model output is an estimate of the electromagnetic field distribution. The results show that the proposed DNN can accurately describe the distribution of electromagnetic field and boundary shape of phantom compared to traditional algorithms (LBP, FBP, Noser and Tikhonov), regardless of the size and number of phantoms within the monitoring area. Hence, the proposed DNN is more robust and has a high degree of generalization

Deep CNN-Based Planthopper Classification Using a High-Density Image Dataset

Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilising deep CNN architectures on a high-density image dataset. This capability has the potential to serve as a tool for classifying and counting planthopper samples collected using light traps

Improved Maturity and Ripeness Classifications of <em>Magnifera Indica </em>cv.<em> </em>Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (<em>Magnifera Indica</em> cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied

Morphological and optical properties of porous hydroxyapatite/cornstarch (HAp/Cs) composites

This paper presents the correlation between the morphological characteristics and the diffuse reflectance (optical properties) of the porous hydroxyapatite/cornstarch (HAp/Cs) composites with various starch proportions (30, 40, 50, 60, 70, 80 and 90 wt%). The porous composites were measured via SEM and enhanced by image processing to find the average pore size, strut width, and average surface roughness. The average porosity of the porous composites was measured using liquid displacement method. The diffuse reflectance spectroscopy was implemented to investigate the diffuse reflectance and the corresponding optical band gap energy of the porous composites in the 500–900 nm range. A relationship between morphological characteristics and diffuse reflectance properties were established using Pearson's correlation coefficient. The findings of the study depict that a strong correlation can be noticed between optical band gap energy with porosity, pore sizes and surface roughness of the porous composites. Meanwhile, the strong correlations between the diffuse reflectance spectral gradient with surface roughness can be observed. The moderate correlations can be observed between the diffuse reflectance spectral gradient with pore sizes and strut width of the porous composites