This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO₂, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R²). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R² of 0.9981.

Several image recoloring methods have been proposed to compensate for the loss of contrast caused by color vision deficiency (CVD). However, these methods only work for dichromacy (a case in which one of the three types of cone cells loses its function completely), while the majority of CVD is anomalous trichromacy (another case in which one of the three types of cone cells partially loses its function). In this paper, a novel degree-adaptable recoloring algorithm is presented, which recolors images by minimizing an objective function constrained by contrast enhancement and naturalness preservation. To assess the effectiveness of the proposed method, a quantitative evaluation using common metrics and subjective studies involving 14 volunteers with varying degrees of CVD are conducted. The results of the evaluation experiment show that the proposed personalized recoloring method outperforms the state-of-the-art methods, achieving desirable contrast enhancement adapted to different degrees of CVD while preserving naturalness as much as possible.

Bunch shape and berry size indicate the quality of table grapes and crucially affect their market value. Berry thinning is one of the most important tasks in grape cultivation to achieve an ideal bunch shape and to make sufficient space for individual berries. A successful practice by skilled grape farmers in Japan is using the number of berries in a bunch to guide the thinning process; hence, a technique for automatically counting the number of berries in a working bunch has been long desired by farmers to improve the efficiency of the thinning task. This research presents a novel end-to-end berry-counting technique based on a deep neural network (DNN), and its contributions are as follows. First, because a DNN requires massive training data, a novel data augmentation technique simulating the thinning process is proposed. Second, a new location-sensitive object detection model that integrates explicit location information and supplementary classification loss into a state-of-the-art instance segmentation model was proposed for detecting the number of berries in a working bunch with a high accuracy. Third, a set of features, together with their extraction algorithms, is designed for predicting the number of berries in a bunch (3D counting) using the berries detected on a single 2D image. Experiments using data collected from farmers’ grape-thinning process have been conducted to validate the accuracy and effectiveness of the proposed methods.

In this paper, we present a novel patch-based match and fusion algorithm by taking account of moving scene in a multiple exposure image sequence using optimization. A uniform iterative approach is developed to match and find the corresponding patches in different exposure images, which are then fused in each iteration. Our approach does not need to align the input multiple exposure images before the fusion process. Considering that the pixel values are affected by various exposure time, we design a new patch-based energy function that will be optimized to improve the matching accuracy. An efficient patch-based exposure fusion approach using the random walker algorithm is developed to preserve the moving objects from the input multiple exposure images. To the best of our knowledge, our algorithm is the first patch-based exposure fusion work to preserve the moving objects of dynamic scenes that does not need the registration process of different exposure images. Experimental results of moving scenes demonstrate that our algorithm achieves visually pleasing fusion results without ghosting artifacts, while the results produced by the state-of-the-art exposure fusion and tone mapping algorithms exhibit different levels of ghosting artifacts.