Tomas Novak

The article explains approaches to modelling classic outdoor lighting systems, such as road lighting, but also new approaches to implementing the radiating characteristics of vertical light sources such as windows and advertisements. All these lighting systems are subsequently implemented in the residential area model with the specification of individual obstacles and their reflective surfaces. A light-technical model is calculated on the points located on the sphere surface named “virtual” software goniophotometer. The output is a lighting calculating model made from available sources simulating the most faithful copy of the residential area with regarding radiation into the upper hemisphere. All lighting systems that radiate at night into the outdoor space are included in the model (public lighting, windows, advertisements and cars). The performed calculations determine both the direct and total radiation of individual lighting systems into the upper hemisphere and thus enable a comparison of their influence on light pollution.

The paper deals with a model situation of a traffic area illuminated by a combination of vehicle low beam headlamps and luminaires of road lighting. In the model situation, the distribution of vertical illuminance in front of the vehicle located directly below the luminaire was evaluated. When the vehicle is positioned below the luminaire, the shortest distances in front of the vehicle occur, in which conditions are sufficient for obstacle detection. The model boundary conditions chosen represented the least favorable situation in terms of visibility in the traffic area. The aim of the study was to evaluate the visibility in the area in front of the vehicle during the operation of the luminaires of road lighting with the regulation of luminous flux output. The main motivation for the operation of luminaires of road lighting is to save electrical energy and are justified by the decrease in traffic volume during late night hours. However, is it appropriate to implement dimming of luminaires without taking into account the visibility of obstacles in the traffic area [1], [2] depending on the stopping distance affected by the maximum permissible vehicle speed?

The article follows on from the article presented at the LS17 conference entitled “Case study – Modelling of Visible Light Communication (VLC) on Luminous Intensity Distribution Curve (LIDC) of LED Low Beam Car Headlamps”. This post dealt with the possibility of using VLC on roads with road lighting systems. In particular, it addressed the minimum light levels at which VLC can still be operated in the outdoor traffic area. These have been compared to the illuminance levels that can be achieved using LED low beam cars headlamps. It was mainly about the distance in front of the car at which it is able to distribute a useful VLC signal. The post addressed whether this technology is even possible to implement in relation to the distances that cars need to stop safely [1]. Since road lighting is also part of the night-time traffic space in cities and towns, this article will deal with its possibilities for implementing VLC in relation to the car communication with this infrastructure. This is made possible by the fundamental expansion of LED technologies, which have already penetrated not only light sources in automotive, but also road lighting [1]. The existing lighting systems of road lighting did not allow the application of VLC, because they were dominantly fitted with high-pressure sodium discharge lamps, which did not allow even slow regulation of the luminous flux, let alone fast switching (modulation) of the luminous flux at kHz levels and above. To demonstrate the possibilities of road lighting, a standard LED lighting system of road lighting with low levels of requirements according to European road lighting standards [EN 13201-2 Road lighting - Part 2: Performance requirements] and classification M5 was chosen for modelling.

Electrical lighting has transformed human society, enabling activities to take place before sunset and improving safety and productivity in many contexts. However, the generation and use of electricity can also have significant environmental impacts, including greenhouse gas emissions and pollution. To mitigate these impacts, many countries are increasingly turning to renewable energy sources, such as Photovoltaic (PV) power. PV power is an attractive source of energy because it is renewable, abundant, and widely distributed. However, the amount of PV energy available varies depending on a range of factors, including latitude, weather patterns, and time of year. In order to better understand the potential for PV power in different regions, it is important to compare the amount of power that can be generated from the sun at different locations. In this paper, we compare the power from the sun during a year in two countries: Czechia and Vietnam. These two countries were chosen because they have different latitudes, climates, and levels of Solar irradiation. Czechia is a landlocked country in central Europe, with a temperate climate and significant seasonal variations in sunlight. Vietnam, on the other hand, is a tropical country in Southeast Asia, with a year-round humid climate and high levels of sunlight. By comparing the power from the sun in these two countries, we aim to provide insights into the potential for PV power in different regions, and to identify factors that influence the amount of power that can be generated from the sun. Overall, this study aims to contribute to our understanding of the PV energy potential of different regions and the need for tailored approaches to PV power generation that take into account the specific environmental, economic, and social contexts.

Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer’s profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.