Modelagem Cross-Layer de Perdas de Pacotes Sobre Perdas de PSNR em rede IEEE 802.11ac / Cross-Layer Modeling of Packet Losses Over PSNR in IEEE 802.11ac Network

Neste artigo é apresentado o comportamento da perda de qualidade de vídeo com resolução espacial de 3840x2160 pixel codificados em H.264/AVC e transmitidos pela rede sem fio no padrão IEEE 802.11ac. Para o estudo são realizadas transmissões em ambiente real, onde métricas de QoS (Quality of Service) e QoE (Quality of Experience) são extraídas para proposição da modelagem. Técnicas de regressão linear são utilizadas nos dados medidos, onde a partir da porcentagem de perda de pacote é estimado a perda de PSNR (Peak Signal-to-Noise). O valor de RMSE (Root Mean Square Error) e o Desvio Padrão do Erro (DPE) são calculados para avaliar o desempenho da proposta, e foram encontrados valores inferiores a 2,5 dB para ambas as métricas de desempenho. Validando a proposta

Avaliação de Desempenho de Transmissões de Vídeo 2K e 4K sobre Redes Sem Fio em um Cenário Indoor / Performance Evaluation of 2K and 4K Video Transmissions over Wireless Networks in an Indoor Setting

Com o crescimento do tráfego multimídia, com foco especial nos vídeos de alta resolução, percebe-se a existência de diversos desafios a serem contornados. Um destes pode trazer insatisfação ao usuário final diante do serviço prestado, se refere a qualidade percebida após o recebimento do streaming ou vídeo. Neste sentido, este trabalho realiza a avaliação de desempenho dos vídeos com resoluções em 2K e 4K, em cenário real indoor. Para este estudo empírico, utilizam-se métricas de análise objetiva como PSNR, VQM e SSIM, e uma subjetiva, o MOS, que representa uma opinião média do usuário após reprodução do vídeo. As principais ferramentas utilizadas neste estudo foram os frameworks EvalVid e MSU Video Quality. 

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Qualidade de vídeo baseada em perda de pacotes utilizando o padrão IEEE 802.11AC

The flow of digital multimedia information in wireless networks has grown exponentially due to the popularization of the IEEE 802.11 Standards as access technology, as well as the increase of devices (clients) that operate in this standard. High-resolution video streaming is also becoming commonplace in these networks, driving the development of more efficient CODECs. In this context, some QoE and QoS metrics must be met to deliver quality content to the end user. This dissertation studies the behavior of wireless video streaming using the IEEE 802.11ac standard operating in the 5.2 GHz range, the CODEC rated is the H.264 / AVC for the 720p, 1080p and 2160p resolutions. From the packet loss simulations during video transmission an estimate of the average loss of PSNR for each resolution was developed, finding the relation of the loss of video quality varying the resolution in function of the loss of packets. The proposed model presented good results when compared to the real data, obtaining RMSE of 2.32 dB and standard deviation of 2.2 dB. This modeling can aid in communication network planning as well as enhancement of new source encoders, resulting in a better quality of experience.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoO fluxo de informação multimídia digital em redes sem fio tem crescido de forma exponencial, devido à popularização dos padrões IEEE 802.11 como tecnologia de acesso, bem como ao aumento de dispositivos (clientes) que operam neste padrão. O streaming de vídeo em altas resoluções também está se tornando comum nessas redes, impulsionando o desenvolvimento de CODECs mais eficientes. Neste contexto, algumas métricas de QoE e QoS devem ser atendidas para entregar um conteúdo com qualidade ao usuário final. Esta dissertação estuda o comportamento do streaming de vídeo em rede sem fio utilizando o padrão IEEE 802.11ac operando na faixa de 5,2 GHz, o CODEC avaliado é o H.264/AVC para as resoluções 720p, 1080p e 2160p. A partir das simulações das perdas de pacotes durante a transmissão de vídeo foi desenvolvido uma estimativa da média da perda de PSNR para cada resolução, encontrando a relação da perda de qualidade de vídeo variando a resolução em função da perda de pacotes. O modelo proposto apresentou bons resultados quando comparado com os dados reais, obtendo RMSE de aproximadamente de 2,32 dB e desvio padrão de 2,2 dB. Esta modelagem pode auxiliar no planejamento da rede de comunicação bem como no aprimoramento de novos codificadores de fonte, resultando em uma melhor qualidade de experiencia

Video loss prediction model in wireless networks.

This work discusses video communications over wireless networks (IEEE 802.11ac standard). The videos are in three different resolutions: 720p, 1080p, and 2160p. It is essential to study the performance of these media in access technologies to enhance the current coding and communications techniques. This study sets out a video quality prediction model that includes the different resolutions that are based on wireless network terms and conditions, an approach that has not previously been adopted in the literature. The model involves obtaining Service and Experience Quality Metrics, such as PSNR (Peak Signal-to-Noise Ratio) and packet loss. This article outlines a methodology and mathematical model for video quality loss in the wireless network from simulated data and its accuracy is ensured through the use of performance metrics (RMSE and Standard Deviation). The methodology is based on two mathematical functions, (logarithmic and exponential), and their parameters are defined by linear regression. The model obtained RMSE values and standard deviation of 2.32 dB and 2.2 dB for the predicted values, respectively. The results should lead to a CODEC (Coder-Decoder) improvement and contribute to a better wireless networks design