Nighttime driving presents significant challenges, including detecting pedestrians, cyclists, and potholes in low-visibility conditions, leading to frequent road accidents. In Sri Lanka, there is a lack of automated systems capable of addressing these challenges. This research proposes a system leveraging AI and real-time video processing to enhance driver safety by improving pedestrian and cyclist visibility and developing a reliable pothole detection system for night time. These enhancements rely on extensive data collection and preprocessing for training specialized computer vision and machine learning models tailored for real-time, low-light scenarios. The application provides real-time alerts and visual cues to reduce nighttime driving accidents. Additionally, a software framework is proposed to offer real-time feedback for drivers leveraging available hardware capabilities, enhancing awareness of vulnerable road users. Through rigorous data collection, testing, and development, this approach significantly helps to enhance nighttime driving safety in Sri Lanka.

The majority of crop vision datasets include domain-specific annotations for use in advanced deep learning applications in both offline and online agricultural settings. In this study, we present BellCrop, a novel dataset of 4,860 high-resolution images of bell pepper leaves divided into three categories: Healthy, Powdery mildew-infected, and Magnesium-deficient leaves. Unlike previous datasets, which were primarily collected in controlled environments, these images were captured in greenhouses under natural conditions. Images were taken with a camera with a resolution of 3468 × 4624 pixels. Deep learning-based classifiers such as VGG19, Xception, and ResNet50 were used to detect these diseases during the early stages of plant growth. Despite the challenging nature of the dataset, the results show detection accuracies of 87%, 81%, and 80%. BellCrop study demonstrates the potential of deep learning models in automating the early detection of leaf diseases in bell peppers. It has significant implications for precision agriculture, allowing for more timely interventions and improved crop management efficiency. Additionally, the study looks into the use of advanced models known as Transformers. EfficientNet-B7 and Vision Transformers (DeiT) achieved more than 90% accuracy rates, improving the reliability of disease classification in agricultural settings.

This review paper discusses the usage of Computer Vision (CV) and Machine Learning (ML) in greenhouse environments for crop grading. It focuses on the progress of autonomous quality gardening using crop image acquisition, preprocessing, and grading over the past five years. The review focused on greenhouse crops: apple, mango, bell pepper, papaya, tomatoes, strawberry, carrot, and chilli. The study focuses on noise reduction, image segmentation, and feature extraction techniques. To improve the assessment and classification of crop quality. Classification using ML algorithms, such as Random Forest, Support Vector Machines (SVM), and K-nearest Neighbours (KNN), are discussed. The addition of AI-driven approaches in agricultural practices is prioritized to enhance crop quality, minimize environmental impact, and satisfy market standards. This review is a comprehensive resource for researchers and practitioners interested in utilizing AI technologies to achieve sustainable and efficient crop production.

This paper presents a machine learning based, cost effective approach to accurately identify the Nitrogen fertilizer demand in paddy fields based on the leaf color. A smart mobile phone mounted on a UAV (Unmanned Aerial Vehicle) was used to capture aerial images of different areas in paddy fields. The proposed approach was used to decide the Nitrogen fertilizer demand for each captured region based on the overall color level identified for the corresponding image, with the help of the color levels and the fertilizer recommendations specified in LCC (Leaf Color Chart). Evaluation results reveal an overall accuracy of 86.5% for the predictions of LCC color levels. Average time taken to predict a single instance was 6.64 seconds.

Today's virtual reality (VR) applications such as gaming, multisensory entertainment, remote dining, and online shopping are mainly based on audio, visual, and touch interactions between humans and virtual worlds. Integrating the sense of taste into VR is difficult since humans are dependent on chemical-based taste delivery systems. This paper presents the 'Thermal Taste Machine', a new digital taste actuation technology that can effectively produce and modify thermal taste sensations on the tongue. It modifies the temperature of the surface of the tongue within a short period of time (from 25°C to 40 °C while heating, and from 25°C to 10 °C while cooling). We tested this device on human subjects and described the experience of thermal taste using 20 known (taste and non-taste) sensations. Our results suggested that rapidly heating the tongue produces sweetness, fatty/oiliness, electric taste, warmness, and reduces the sensibility for metallic taste. Similarly, cooling the tongue produced mint taste, pleasantness, and coldness. By conducting another user study on the perceived sweetness of sucrose solutions after the thermal stimulation, we found that heating the tongue significantly enhances the intensity of sweetness for both thermal tasters and non-thermal tasters. Also, we found that faster temperature rises on the tongue produce more intense sweet sensations for thermal tasters. This technology will be useful in two ways: First, it can produce taste sensations without using chemicals for the individuals who are sensitive to thermal taste. Second, the temperature rise of the device can be used as a way to enhance the intensity of sweetness. We believe that this technology can be used to digitally produce and enhance taste sensations in future virtual reality applications. The key novelties of this paper are as follows: 1. Development of a thermal taste actuation technology for stimulating the human taste receptors, 2. Characterization of the thermal taste produced by the device using taste-related sensations and non-taste related sensations, 3. Research on enhancing the intensity for sucrose solutions using thermal stimulation, 4. Research on how different speeds of heating affect the intensity of sweetness produced by thermal stimulation.

Olfactometers are kind of machines specially designed to deliver different odorants to the users, based on a timely protocol. Purchasing off the shelf systems are expensive and require additional customizations according to the type of the experiments they will be used for. In order to facilitate smell, taste, psychology, and neuroscience experiments we have developed our own laboratory olfactometer from the scratch. This paper provide a complete overview of the design and development process of our laboratory olfactometer. The key novelty of our work is the development of low cost, flexible, and easy maintenance olfactometer. Further, this olfactometer provides rich functionalities such as seven odor channels and a control air channel, flow controlling for the overall system and individual channels, air pressure control, air filtration, back flow prevention, and continuous operation of more than 8 hours.

Confucianism is commonly defined as “... a system of philosophical, ethical and political thought based on the teachings of Confucius,” which originated through the teachings of Confucius during the sixth-century BCE. It is a way of life or a philosophy of human nature that considers human relationships as the foundation of the society. Confucius teachings had highly influenced the development of several cultures in Asia, making Confucianism an intangible cultural heritage. In this paper, we are re-acquainting users with an intangible heritage that is part of their everyday, by developing a system that permits experiencing Confucius teachings virtually and interactively. The system can measure philosophical intent of the human and generate meaningful philosophical answers. It is also aimed for intergenerational sharing of Confucius heritage through a simple interactive process with the virtual sage making the experience enjoyable and entertaining. Previous research in natural language processing (NLP) mainly focused on the understanding and delivering of human natural language accurately. In this research, we explored how to apply NLP to model the knowledge and teachings of Confucius, through the natural conversation between human and computer. This virtual Confucius, a chat agent that generates outputs based on Confucius teachings, using a series of algorithms and techniques to improve the matching accuracy between user input and computer output, introduces a novel way of interacting with intangible cultures. Our user evaluation results revealed that there is a positive correlation between relevance and enjoyment, finding their experiences interacting with virtual Confucius very encouraging. Adults who participated in experiencing the virtual Confucius together with their children believed that this system has the potential to improve intergenerational interactions through shared play.

In this poster, we present our concept of magnetic dining table interface and magnetic foods. This interface allows new interactions for the food and utensils such as modifying weight, levitation, movement, and dynamic textures. We implement these interactions by manipulating a strong magnetic field formed by an array of Bitter electromagnets which placed under the surface of the table. Then we add edible magnetic materials to the foods, and make them interactive and controllable.

This poster paper discuss the concept of magnetic table interface and magnetic foods. This interface introduces new human-food interactions such as modifying weight, levitation, movement, and dynamic textures for our daily used food and utensils using a strong magnetic field formed by an array of Bitter electromagnets. To make foods and cutleries interactive, we add edible magnetic material on the food such as iron and iron oxides, and make cutleries with magnetic materials. We expect that this system will alter our food consumption behaviours and make the whole experience much more interactive and enjoyable.

This paper presents a new taste interface for multisensory communication called "Thermal Sweet Taste Machine". We developed this interface in order to create sweet sensations, by manipulating the temperature on the tongue, without using chemicals. This device device changes the temperature on the surface of the tongue (from 20°C to 40°C) within a short period of time using a computer controlled circuit. Our preliminary user studies suggested that this device would be effective in two ways; producing the sweet sensations without the aid of chemicals, and enhancing the sweetness of the food and drinks. Here we discuss our concept, development of the interface, and some preliminary studies that has been carried out. We believe our technology would enhance the experiences and capabilities in future multisensory communication in different disciplines such as Human-Computer Interaction, human robot interactions, gaming and interacting with artificial agents.

This is an introductory paper for the workshop entitled ‘Multi-Sensorial Approaches to Human-Food Interaction’ held at ICMI 2016, which took place the 16th of November, 2016 in Tokyo, Japan. Here we discuss our objectives and the relevance of the workshop, and summarize the key contributions of the position papers. We were able to gather a group of researchers from different countries in Europe and Asia who presented their research and discussed the current developments, trends, limitations, and future applications of the field. Whilst this is the first workshop of its kind, we anticipate that the field of multisensory Human-Food Interaction (HFI) will grow in the upcoming years in terms of research and development, and its products will impact our everyday eating experiences.

THIS tutorial paper describes the development of a new interface for multisensory communications called "Thermal Sweet Taste Interface". By rapidly changing the temperature of the surface of the tongue, this interface is capable of generating virtual sweet sensations. This device may enhance new user experiences in different disciplines such as Human-Computer Interaction, New Media Arts, Internet Communication and Medicine.

This workshop aims to increase awareness of the different ways in which new technologies (e.g. social media, virtual reality, digital, and neuroimaging technologies) can be used as input or means to develop multisensory experiences that improve consumer satisfaction. Consumers experience products and services via their senses (i.e. sight, hearing, smell, taste, touch). The specific multisensory content of those products and services can influence people's respective attitudes, purchase intentions, and consumption. Today, consumers experiment the world through new technologies, for example, by sharing their feelings via social media, or by creating avatar in a virtual world. The challenge for marketing consists of monitoring new technologies to better understand consumer sensory experiences and to develop innovative actions that will help to improve these experiences. We will begin with the definition of sensory marketing and the role of multisensory processes in consumer experience. Second, we will address the possibility of better understanding the consumer through social media and neuroimaging technologies. To finish, we will present some technologies that are already enriching sensory experiences of consumers.

The Penan people of Malaysian Borneo were traditionally nomads of the rainforest. They would leave messages in the jungle for each other by shaping natural objects into language tokens and arranging these symbols in specific ways -- much like words in a sentence. With settlement, the language is being lost as it is not being used by the younger generation. We report here, a tangible system designed to help the Penan preserve their unique object writing language. The key features of the system are that: its tangibles are made of real objects; it works in the wild; and new tangibles can be fabricated and added to the system by the users. Our evaluations show that the system is engaging and encourages intergenerational knowledge transfer and thus has the potential to help preserve this language.

We describe a real time authoring tool that is useful to supervise group of users in an augmented reality environment. The overall system is composed of a server; the Command Center (CC) and several clients; Wearable Systems (WS). The server is used to visualize the virtual representation of the real environment, 3d features map where augmentation are to occur, augmented content that will be rendered and users avatar that evolves on the field. The user's WS is equipped of a Head Mounted Display (HMD) along with sensors (GPS/DRM/Compass) to provide constant pose estimation (position and orientation) to the CC and a vision based tracking that uses natural features and pose estimation allgorithms to render virtual content accordingly. Alternatively a sensor based renderer is used to display AR guidance on the HMD based on the path defined and sent from the CC. The natural features system handles a large number of small 3D features maps. The overall system offers a complete system to train and supervise remote users through an AR environment.