In the paper, we present an analytical study of multimedia stream transmission with the reservation-based channel access method called MCCA in IEEE 802.11s mesh networks. Various aspects of MCCA have been already studied under the assumption that the reservation guarantees successful transmission, no retries are needed and the period of reserved times is equal to the inter-arrival time of the input stream. However, recent papers reveal the fact that two-hop advertisement of reservations, as adopted in MCCA, fails to completely defeat the interference affecting transmissions in addition to random noise. To keep the packet loss ratio (PLR) acceptable for a stream, period of reserved times may be shortened to allow packet retries. However, a packet of a stream is usually discarded when the packet delay reaches its threshold, making its contribution to the PLR. Also, additional reservations is a burden. In this paper, we propose an analytical model to find the maximal period of reserved times to keep the packet loss ratio and delay below thresholds, given the input stream bit rate and packet error rate.

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In this paper we investigate the problem of finding the minimal operations number for the generalized Hough transform computation (GHT). We demonstrate that this problem is equivalent to the addition chain problem and is therefore NP-complete. Three greedy methods for generating GHT computation algorithms are proposed and their performance is compared against the fast Hough transform (FHT) for different discrete straight line pattern types. The additional result of this work is the experimental proof of the FHT non-optimality.

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Line detection is an important computer vision task traditionally solved by Hough Transform. With the advance of deep learning, however, trainable approaches to line detection became popular. In this paper we propose a lightweight CNN for line detection with an embedded parameter-free Hough layer, which allows the network neurons to have global strip-like receptive fields. We argue that traditional convolutional networks have two inherent problems when applied to the task of line detection and show how insertion of a Hough layer into the network solves them. Additionally, we point out some major inconsistencies in the current datasets used for line detection.