Everyday many lives are lost due to accidents on roads. Normally death happens due to the injury suffered by the passenger in the road accident but most of the time it also has been seen that the information of the accident, reach the emergency department very late, consequently the injured person could not sustain. So there must be an automatic system in every car which not only detect a road accident efficiently but also notify it to the emergency department very instantly. Researchers have proposed a number of automatic road accident detection methods. These methods typically use airbags, smart-phones, infrared sensors (IR sensor) and mobile application. None of these methods are perfect for the automatic detection of road accident. The filters used in the smart-phones, can cause difficulty in detecting low speed fender bender. Using IR sensor, the threshold distances, on which it will react is not mentioned in the proposed system. The method using airbags are not designed for motorbikes and cycles. If mobile application is used for accident detection, then different smart-phone models have different processors of various speeds so the application may not perform efficiently in all the smart-phone models. So in this paper, a novel approach based on ultrasonic sensor is proposed. Accident detection using ultrasonic sensor provides the facility to detect an accident not only in various street situations but also it might perform well under various natural conditions like rains.

The computer science perspective of ontology refers to ontology as a technology, however, with a different perspective in terms of interrogations and concentrations to construct engineering models of reality. Agriculture-centered architectures are among rich sources of knowledge that are developed, preserved, and released for farmers and agro professionals. Many researchers have developed different variants of existing ontology-based information systems. These systems are primarily picked agriculture-related ontological strategies based on activities such as crops, weeds, implantation, irrigation, and planting, to name a few. By considering the limitations on agricultural resources in the ONTAgri scenario, in this paper, an extension of ontology is proposed. The extended ONTAgri is a service-oriented architecture that connects precision farming with both local and global decision-making methods. These decision-making methods are connected with the Internet of Things systems in parallel for the input processing of system ontology. The proposed architecture fulfills the requirements of Agriculture 4.0. The significance of the proposed approach aiming to solve a multitude of agricultural problems being faced by the farmers is successfully demonstrated through SPARQL queries.

An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.

Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.

Medical images provide an excellent way to identify the presence of diseases in a highly acceptable and correct manner. These images, however, due to the physical limitations of imagin ...

Many mathematical operations are implemented easily through transform domain operations. Multiple transform domain operations are used independently in large and complex applications. There is a need to develop integrated representations for multiple transform domain operations. This paper presents an integrated mathematical representation for the discrete Fourier transformation and the discrete wavelet transformation. The proposed combined representation utilizes the powerful vector notation. A mathematical operator, called the star operator, is formulated that merges coefficients from different transform domains. The star operator implements both convolution and correlation processes in a weighted fashion to compute the aggregated representation. The application of the proposed mathematical formulation is demonstrated successfully through merging transform domain representations of time-domain and image-domain representations. Heart sound signals and magnetic resonance images are used to describe transform-domain data merging applications. The significance of the proposed technique is demonstrated through merging time-domain and image-domain representations in a single- stage that may be implemented as the primary processing engine inside a typical digital image processing and analysis system.