Steven Bohez

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Mobile applications are evolving towards support for advanced interactivity and resource-demanding multimedia features. Mobile platforms are however struggling to cope with these new innovative application concepts, such as Augmented Reality, due to the inherent limitations on their available resources, such as CPU, memory and battery power. Offloading(More)
Measuring the distance between observed objects and the camera, depth cameras on mobile devices are a leverage to more accurate and innovative vision-based applications. In this article, we present the initial design of a distributed cloudlet-based system that integrates depth maps crowd-sourced from mobile devices and head-mounted displays into a global 3D(More)
Nowadays artificial neural networks are widely used to accurately classify and recognize patterns. An interesting application area is the Internet of Things (IoT), where physical things are connected to the Internet, and generate a huge amount of sensor data that can be used for a myriad of new, pervasive applications. Neural networks' ability to comprehend(More)
Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity, based on word overlap, mostly fail to produce good results in this case, since word overlap is little or non-existent.(More)
The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading(More)
Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback for neural networks. In order to exploit the(More)
Most of the research on deep neural networks so far has been focused on obtaining higher accuracy levels by building increasingly large and deep architectures. Training and evaluating these models is only feasible when large amounts of resources such as processing power and memory are available. Typical applications that could benefit from these models are,(More)
The deployment of highly interactive, media-rich applications on mobile devices is hindered by the inherent limitations on compute power, memory and battery capacity of these hand-held platforms. The cloudlet concept, opportunistically offloading computation to nearby devices, has proven to be a viable solution in offering resourceintensive applications on(More)