Two low-cost methods of estimating the road surface condition are presented in the paper, the first one

based on the use of accelerometers and the other on the analysis of images acquired from cameras installed

in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of

the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver

and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken

place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined

road sections. The data were gathered for various vehicle body types and afterwards successful attempts

were made in constructing the road surface classification employing the created algorithm. In turn, in the

video method, a set of algorithms processing images from a depth camera and RGB cameras were created.

A representative sample of the material to be analysed was obtained and a neural network model for classification

of road defects was trained. The research has shown high effectiveness of applying the digital image

processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification

of road defects amounted to 70%. The paper presents the methods of collecting and processing the

data related to surface damage as well as the results of analyses and conclusions.

In this covid19 pandemic the number of people gathering at public places and festivals are restricted and maintaining social distancing is practiced throughout the world. Managing the crowd is always a challenging task. It requires monitoring technology. In this paper, we develop a device that detects and provide human count and detects people who are not maintaining social distancing . The work depicted above was finished using a Raspberry Pi 3 board with OpenCV-Python. This method can effectively manage crowds.

The article presents the power consumptions measurements performed for three wireless routers operating in IEEE 802.11n standard. A typical consumer-class device Asus RTAC66U was chosen, an operator-class Gateworks Laguna GW2387 and a router built based on the Raspberry Pi3 platform. The aim of experiments was to test the influence of the beacon interframe interval, a client association (joining) in the network and the transmission itself, on the lifetime of battery-powered devices. Theoretical calculations were also performed for the influence of the analyzed scenarios on the battery-powered devices.