Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions for the broader public because video cameras are available on almost any smartphone nowadays. Unfortunately, most OCC systems that have been proposed until now require either expensive and specialized high-frame-rate cameras as receivers, which are unavailable on smartphones, or they rely on the rolling shutter effect, being sensitive to camera movement and pointing direction, they produce light flicker when low-frame-rate cameras are used, or they must discern between more than two light intensity values, affecting the robustness of the decoding process. This paper presents in detail the design of an OCC system that overcomes these limitations, being designed for receivers capturing 120 frames per second and being easily adaptable for any other frame rate. The system does not rely on the rolling shutter effect, thus making it insensitive to camera movement during frame acquisition and less demanding about camera resolution. It can work with reflected light, requiring neither a direct line of sight to the light source nor high resolution image sensors. The proposed communication is invariant to the moment when the transmitter and the receiver are started as the communication is self-synchronized, without any other exchange of information between the transmitter and the receiver, without producing light flicker, and requires only two levels of brightness to be detected (light on and light off). The proposed system overcomes the challenge of not producing light flicker even when it is adapted to work with very low-frame-rate receivers. This paper presents the statistical analysis of the communication performance and discusses its implementation in an indoor localization system.

No abstract

Methods for inspecting the integrity of audio recordings become a necessity. The evolution of technology allowed the manufacturing of small, performant, recording devices and significantly decreased the difficulty of audio editing. Any person that participates in a conversation can secretly record it, obtaining their own version of the audio captured using their personal device. The recordings can be easily edited afterwards to change the meaning of the message. The challenge is to prove if recordings were tampered with or not. A reliable solution for this was the highly acclaimed Electrical Network Frequency (ENF) criterion. Newer recording devices are built to avoid picking up the electrical network signal because, from the audio content point of view, it represents noise. Thus, the classic ENF criterion becomes less effective for recordings made with newer devices. The paper describes a novel sonic watermarking (i.e., the watermark is acoustically summed with the dialogue) solution, based on an ambient sound that can be easily controlled and is not suspicious to listeners: the ticking of a clock. This signal is used as a masker for frequency-swept (chirp) signals that are used to encode the ENF and embed it into all the recordings made in a room. The ENF embedded using the proposed watermark solution can be extracted and checked at any later moment to determine if a recording has been tampered with, thus allowing the use of the ENF criterion principles in checking the recordings made with newer devices. The experiments highlight that the method offers very good results in ordinary real-world conditions.

Face detection has multiple applications including recognition, people identification and detection of facial expressions. With the current pandemic crisis and due to the measures imposed to prevent Covid-19 spreading, the wearing of a protection mask became mandatory. The object of interest of this paper is to detect the wearing of an approved mask face using Viola Jones algorithm, aggregate channel features (ACF) and mathematical morphology. COVID-19 virus spread through the air, so it is necessary that all the materials used for manufacturing of the face masks to filter properly the air, and only the approved face masks to be used in order to control the spread infection. The algorithm used for face detection is Viola Jones with notable success in real time face detection and real time impression speed in face detection. Identification of the approved face mask against Covid-19 virus is made with a trained ACF detector. Eye detection, necessary to check if the face mask is properly placed, is based on mathematical morphology operators. These operations used together are robust with high results on the image processing.

No abstract