Using computer vision to categorize tyres and estimate the number of visible tyres in tyre stockpile images

Pressures from environmental agencies contribute to the challenges associated with the disposal of waste tyres, particularly in South Africa. Recycling of waste tyres in South Africa is in its infancy resulting in the historically undocumented and uncontrolled existence of waste tyre stockpiles across the country. The remote and distant locations of such stockpiles typically complicate the logistics associated with the collection, transport and storage of waste tyres prior to entering the recycling process. In order to optimize the logistics associated with the collection of waste tyres from stockpiles, useful information about such stockpiles would include estimates of the types of tyres as well as the quantity of specific tyre types found in particular stockpiles. This research proposes the use of computer vision for categorizing individual tyres and estimating the number of visible tyres in tyre stockpile images to support the logistics in tyre recycling efforts. The study begins with a broad review of image processing and computer vision algorithms for categorization and counting objects in images. The bag of visual words (BoVW) model for categorization is tested on two small data sets of tread tyre images using a random sub-sampling holdout method. The categorization results are evaluated using performance metrics for multiclass classifiers, namely the average accuracy, precision, and recall. The results indicated that corner-based local feature detectors combined with speeded up robust features (SURF) descriptors in a BoVW model provide moderately accurate categorization of tyres based on tread images. Two feature extraction methods for extracting features for use in training neural networks (NNs) for tyre count estimations in tyre stockpiles are proposed. The two feature extraction methods are used to describe images in terms of feature vectors that can be used as input for NNs. The first feature extraction method uses the BoVW model with histograms of oriented gradients (HOG) features collected from overlapping sub-images to create a visual vocabulary and describe the images in terms of their visual word occurrence histogram. The second feature extraction method uses the image gradient magnitude, gradient orientation, and edge orientations of edges detected using the Canny edge detector. A concatenated histogram is constructed from individual histograms of gradient orientations and gradient magnitude. The histograms are then used to train NNs using backpropogation to approximate functions from the feature vectors describing the images to scalar count estimations. The accuracy of visible object count predictions are evaluated using NN evaluation techniques to determine the accuracy of predictions and the generalization ability of the fit model. The count estimation experiments using the two feature extraction methods for input to NNs showed that fairly accurate count estimations can be obtained and that the fit model could generalize fairly well to unseen images

Impact of communication appeals on recycling behaviors among undergraduate students

The present thesis aims to understand factors influencing student recycling behaviors, and to investigate effective communication approaches to increase such behaviors. An online survey was conducted to examine the relationships between student recycling frequency in different contexts, students’ attitudes toward the environment, barriers to their recycling, students’ perceptions of communication messages, and communication media they think to be effective. Descriptive statistics, ANOVAs, t-test, simple linear regressions, categorical multinomial logistic regression, and a chi-square test were conducted, and the data was collected from a large land-grant university in the Midwestern United States. A total of 537 questionnaires were answered. The main results of the present study are as follows: First, context as well as recycling barriers were factors that influenced student recycling behaviors. Most students who were likely to recycle at home would also recycle on campus, but students recycled more at home than on vacation. The main recycling barriers on campus were attitude barriers and knowledge barriers, while on vacation the main barriers were situational. Second, students thought positive messages were most effective in increasing recycling behavior, while students with less pro-environmental attitudes preferred neutral messages. “Clear, informative, and consistent bin infrastructure and bin labels” and “promotions such as recycling contests [and] competitions between departments or colleges” were found to be effective forms of communication. Additionally, when there were more significant factors such as the accessibility of recycling, student environmental attitudes did not play an important role in recycling behaviors on campus and on vacation. The study offers two practical recommendations. They are to increase recycling facilities and accessibility, and providing informative, clear recycling signs and labels with positive messages. Two suggestion are made for future research on the topic. They are to find factors that are more determinant than attitudes of environment about student recycling and to do more research on the usage of positive messages about student recycling behaviors

Probabilistic multiple kernel learning

The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels

MRI image segmentation using machine learning networks and level set approaches

The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones

Material Measurement Units: Foundations Through a Survey

Long-term availability of minerals and industrial materials is a necessary condition for sustainable development as they are the constituents of any manufacturing product. In particular, technologies with increasing demand such as GPUs and photovoltaic panels are made of critical raw materials. To enhance the efficiency of material management, in this paper we make three main contributions: first, we identify in the literature an emerging computer-vision-enabled material monitoring technology which we call Material Measurement Unit (MMU); second, we provide a survey of works relevant to the development of MMUs; third, we describe a material stock monitoring sensor network deploying multiple MMUs.Comment: In preparation for submission to ACM Computing Survey

From Classical to Advanced Use of Polymers in Food and Beverage Applications

Polymers are extensively used in food and beverage packaging to shield against contaminants and external damage due to their barrier properties, protecting the goods inside and reducing waste. However, current trends in polymers for food, water, and beverage applications are moving forward into the design and preparation of advanced polymers, which can act as active packaging, bearing active ingredients in their formulation, or controlling the head-space composition to extend the shelf-life of the goods inside. In addition, polymers can serve as sensory polymers to detect and indicate the presence of target species, including contaminants of food quality indicators, or even to remove or separate target species for later quantification. Polymers are nowadays essential materials for both food safety and the extension of food shelf-life, which are key goals of the food industry, and the irruption of smart materials is opening new opportunities for going even further in these goals. This review describes the state of the art following the last 10 years of research within the field of food and beverage polymer’s applications, covering present applications, perspectives, and concerns related to waste generation and the circular economy.This work was supported by the Regional Government of Castilla y León (Junta de Castilla y León) and by the Ministry of Science and Innovation MICIN and the European Union NextGeneration EU PRTR. The project leading to these results has received funding from “La Caixa” Foundation, under the agreement LCF/PR/PR18/51130007. We also gratefully acknowledge the grant PID2020-113264RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. Finally, we want to acknowledge the funding from Ministerio de UniversidadesEuropean Union in the frame of NextGenerationEU RD 289/2021 (Universidad Politécnica de Madrid and Universidad Autónoma de Madrid-CA1/RSUE/2021-00409)

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance