The Web has shifted towards a mobile-first ecosystem with tools, frameworks, and forums explicitly discussing and catering for the mobile users, both mobile apps and mobile web-pages. Unfortunately, much of the studies and designs are often based on analysis and findings from developed regions (e.g., N. America and Europe) or based on user-generated data (introducing bias). In this paper, we present one of the first studies to understand the interplay between hardware characteristics (e.g., cellular and mobile) on expected network and application level performance in Brazil (the largest developing region in S. America). We analyze more than 170 million measurement sessions collected from within the network of one of the largest Mobile Network Operators in Brazil. Our findings (1) illustrate limitations of existing crowdsourced measurements and inaccuracies in assumptions about adoption patterns and performance in the global south, (2) highlight the differences between recommendations made by standardization bodies and real world performance, (3) disclose a significant change pre- and post-pandemic, and (4) quantify the benefits of using both client side and network data for analysis.

To deal with the massive traffic produced by video applications, mobile operators rely on offloading technologies such as Small Cells, Content Delivery Networks and, shortly, Cloud Edge and 5G Device to Device communications. Although these techniques are fundamental for improving network efficiency, they produce a multitude of paths onto which the user traffic can be forwarded. Thus, a critical problem arises about how to handle the increasing video traffic while managing the interplay between infrastructure optimization and the user's Quality of Experience (QoE). Solving this problem is remarkably difficult, and recent investigations do not consider the large-scale context of mobile operator networks. To address this issue, we present a novel QoE-aware path deployment scheme for large-scale SDN-based mobile networks. The scheme relies on both a polynomial-time algorithm for composing multiple QoS metrics and a scalable QoS to QoE translation strategy. Considering real mobile operator network and video traffic traces, we show that the proposed algorithm outperformed state-of-the-art approaches by reducing impaired videos in aggregate MOS by at least 37% and lowering accumulated video stall length four times.

Due to the fact that video streaming is the current "killer" application and for competitiveness, telecommunication service providers need to be able to answer a fundamental question: to which extent is the available network infrastructure able to successfully provide users with a satisfactory experience when running video streaming applications? Answering this question is far from trivial because existing techniques are neither scalable nor accurate enough. To address this issue, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. To accomplish this objective, the proposed model - created using LTE networks as case study - leverages low network consumption active measurements and machine learning techniques. Obtained results show that the proposed solution produces accurate estimates (average error of less than 10%) while keeping intrusiveness around twenty times lower than traditional techniques.

This paper presents a low cost solution for Digital Operating Rooms called MIR (Multimedia Integrated Room). This solution offers a wider variety of useful features compared to other available digital rooms. MIR is capable of all-IP real time transmission and recording of endoscopic procedures using multiple HD cameras, integration with the hospital information system and also can be easily controlled by a wireless mobile device. The developed system main application areas are: a) medical students and/or specialist preceptor to follow the surgical procedure remotely with high quality images; b) the surgical team to better visualize the surgery through the multiple camera views.

Repetitive indoor exercises as running on a treadmill or cycling on an exercise bike use to be tedious and hard to keep in diary basis. In sports, social relationships are an important source of stimulus to achieve a goal. Better performances can be achieved when one compete against others, as well as when a group collaborate to achieve a common target. In this work we propose a motivational system to help people to perform better in exergames using a virtual environment and testing the effects of collaboration and competition in such environment.

In this paper, we propose a novel method to perform the classification of electrocardiogram (ECG) beats on mobile phones. The Discrete Wavelet Transform and Higher Order Statistics are used to extract a set of eleven features from each ECG beat, and a Multilayer Perceptron is used to classify it between six types of beats. Our experiments show that a mobile phone can classify the ECG beats with an overall accuracy of 99.83%, and the sensitivity rates are greater than 99.48% for all beat. Running in a mobile phone phone, it needs only 27ms to classify each heart beat, what makes this method a reliable choice to perform computer-aided diagnosis on remote and critical situations.

The ITU-T H.264 can be considered currently as the most efficient professional video coding standard. This codec combines distinct complex techniques in order to encapsulate raw video into efficient compressed streams. Unfortunately, the improved H.264 coding efficiency increases significantly the computational complexity, making real-time applications difficult to be achieved. Particularly the motion prediction is an important and complex algorithm used to remove temporal redundancy of image sequences. Considering that, we propose an optimized high performance small diamond topology zonal search motion prediction algorithm. The proposed method has been designed to operate with orthogonal linear vectors, which are specially aligned with 4:1 sub sampled memories organization reducing the number of operations and data manipulations. The proposed solution was implemented in the H.264 JM reference software obtaining results at least two times faster than other available fast algorithms with negligible quality losses.

OWD is becoming increasingly important nowadays, as SLA agreements use it as a parameter to ensure QoS levels. However, its measurement is still a much debated topic due to the difficulty in the clock synchronization process. The paper approaches a new methodology to measure OWD, using NTP protocol with virtual clocks and bigger packets. This approach works over a measurement tool developed by the authors, called Netmetric, and is flexible enough to be applied in full-mesh networks, without lack in clock consistency during concurrent NTP synchronizations. The proposed method was validated using NTCUns simulator, tested over Vivo/Telefonica's 3G Network, and compared to OWAMP.

The computational module of several MPEG-based video encoders, which includes the known algorithms of Discrete Cosine Transform, Hadamard Transform and Quantization, is widely used to identify and compress spatial redundancy in intra (raw input) or inter (computed residue) data pixel matrices. For some modern multimedia applications, like high definition (HD H.264/AVC) or scalable (H.264/SVC) encoder solutions, the demand for fast module implementations becomes critical. Practical experiments indicate that, inside a H.264 computational module, the quantization module normally represents a real bottleneck for fast hardware implementations. Considering that we propose a complete integrated solution of H.264 computational module, which incorporates the direct and inverse algorithms of Discrete Cosine Transform, Hadamard and Quantization with minimal communication delays. Also in this paper it is presented a practical study, considering distinct levels of parallelism for the quantization to demonstrate its influence in order to optimize global encoder complexity and performance. All proposed alternatives were designed using hardware description language VHDL and implemented into commercial FPGA boards to obtain experimental results.