In this paper we describe practical implementation of FPGA based solution for converting signal from cameras with HD-SDI, analogue and camera link interfaces to common HDMI format. Main challenges like: frame rate equalization, image resolution adaptation and acts in case of camera connection loss are described in details. As a practical example we gave details about system with HD-SDI visible light camera, HD MWIR camera and analogue SWIR camera. In order to achieve target visibility in different day/night and meteorological conditions a multi-sensor imaging system combines signals from visible light camera, short wave infrared (SWIR) camera and medium (MWIR) or long (LWIR) wave infrared camera. Usually cameras originate from different vendor, with different interfaces, resolutions and frame rate. In order to enable further processing like image stabilization, enhancement, target tracking and image fusion, formats from all cameras should be converted to the same format.

In this paper, we present a hardware and software platform for signal processing (SPP) in long-range, multi-spectral, electro-optical systems (MSEOS). Such systems integrate various cameras such as lowlight color, medium or long-wave-infrared thermal and short-wave-infrared cameras together with other sensors such as laser range finders, radars, GPS receivers, etc. on rotational pan-tilt positioner platforms. An SPP is designed with the main goal to control all components of an MSEOS and execute complex signal processing algorithms such as video stabilization, artificial intelligence-based target detection, target tracking, video enhancement, target illumination, multi-sensory image fusion, etc. Such algorithms might be very computationally demanding, so an SPP enables them to run by splitting processing tasks between a field-programmable gate array (FPGA) unit, a multicore microprocessor (MCuP) and a graphic processing unit (GPU). Additionally, multiple SPPs can be linked together via an internal Gbps Ethernet-based network to balance the processing load. A detailed description of the SPP system and experimental results of workloads for typical algorithms on demonstrational MSEOS are given. Finally, we give remarks regarding upgrading SPPs as novel FPGAs, MCuPs and GPUs become available.

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Face detection systems with color cameras were rapidly evolving and have been well researched. In environments with good visibility they can reach excellent accuracy. But changes in illumination conditions can result in performance degradation, which is the one of the major limitations in visible light face detection systems. The solution to this problem could be in using thermal infrared cameras, since their operation doesn't depend on illumination. Recent studies have shown that deep learning methods can achieve an impressive performance on object detection tasks, and face detection in particular. The goal of this paper is to find an effective way to take advantages from thermal infrared spectra and provide an analysis of various image degradation influence on thermal face detection performance in a system based on R-CNN with special accent on implementation on a hardware platform for video signal processing that institute Vlatacom has developed, called vVSP.

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Computational methods are, among other things, widely used in operational research. Operational research is a complex interdisciplinary field that deals with the problems of decision-making in real conditions, considering all the factors that affect the problem directly or indirectly, in order to find the best, i.e. optimal solution. As there is a growing need for continuous process improvement, there is a growing presence of operational research methods for various real-life problems. The transportation problem is one of the segments of research within operational research. It aims to determine the optimal program of distribution of a certain type of commodity from sources (points of origin) to destinations. The sources are the places where the commodity leaves (the warehouse), while the destinations are the ending points to which the commodity should be transported (in our case -the store). As a criterion for optimizing the transportation of goods, the request for minimizing the total transportation costs is most often taken. In the case of transportation problem, the objective function expresses the total transportation costs, while the limiting conditions are determined by the supply of individual sources (warehouses), i.e. the demand of individual destinations (stores). This paper discusses the possibility of applying operational research methods in the service sector. The aim of the research part of the paper is to find the optimal solution for real data of a given problem, simulating different conditions and constraints. An experimental analysis was performed for the problem of warehouse operations, and the goal was to minimize the costs of transporting goods. Two different methods were applied in order to determine the optimal solution. Based on the obtained results and their analysis, conclusions were made as to whether the problem was solved.