Towards a Smart Selection of Hybrid Platforms for Multimedia Processing

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.Nowadays, images and videos have been present everywhere, they can come directly from camera, mobile devices or from other peoples that share their images and videos. The latter are used to illustrate different objects in a large number of situations. This makes from image and video processing algorithms a very important tool used for various domains related to computer vision such as video surveillance, medical imaging and database (images and videos) indexation methods. The performance of these algorithms have been so reduced due the the high intensive computation required when using new image and video standards. In this paper, we propose a new framework that allows users to select in a smart and efficient way the processing units (GPU or/and CPU) within heterogeneous systems, when treating different kinds of multimedia objects : single image, multiple images, multiple videos and video in real time. The framework disposes of different image and video primitive functions that are implemented on GPU, such as shape (silhouette) detection, motion tracking using optical flow estimation, edges and corners detection. We have exploited these functions for several situations such as indexing videos, segmenting vertebrae in in X-ray and MR images, detecting and localizing event in multi-user scenarios. Experimentation showed interesting accelerations ranging from 6 to 118, by comparison with sequential implementations. Moreover, the parallel and heterogeneous implementations offered lower power consumption as a result for the fast treatment.European Cooperation in Science and Technology. COS

Internet of Things: learning and practices. Application to Smart City

peer reviewedInternet of Things is becoming widely present in our daily life. In fact, more and more devices able to interact together have been recently designed and launched in the market. Learning Internet of Things technologies is becoming unavoidable in education. In this paper, we propose a practical approach allowing to progressively learn, by practice the essential concepts of Internet of Things applied to Smart Cities. From basic knowledge of python language and the use of microcontrollers Pycom such as LoPy, students can develop skills and also smart applications in the field of Internet of Things

Edge Computing for Cattle Behavior Analysis

peer reviewedSmartphones, particularly iPhone, can be relevantinstruments for researchers because they are widely used aroundthe world in multiple domains of applications such as animalbehavior. iPhone are readily available on the market, containmany sensors and require no hardware development. They areequipped with high performance inertial measurement units(IMU) and absolute positioning systems analyzing user’s move-ments, but they can easily be diverted to analyze likewise thebehaviors of domestic animals such as cattle. Using smartphonesto study animal behavior requires the improvement of theautonomy to allow the acquisition of many variables at a highfrequency over long periods of time on a large number ofindividuals for their further processing through various modelsand decision-making tools. Indeed, storing, treating data at theiPhone level with an optimal consumption of energy to maximizebattery life was achieved by using edge computing on the iPhone.This processing reduced the size of the raw data by 42% onaverage by eliminating redundancies. The decrease in samplingfrequency, the selection of the most important variables andpostponing calculations to the cloud allowed also an increasein battery life by reducing of amount of data to transmit. Inall these use cases, the lambda architectures were used to ingeststreaming time series data from the Internet of Things. Cattle,farm animals’ behavior consumes relevant data from InertialMeasurement Unit (IMU) transmitted or locally stored on thedevice. Data are discharged offline and then ingested by batchprocessing of the Lambda Architecture

Edge AI-IoT Pivot Irrigation, Plant Diseases and Pests Identification

peer reviewedIn a growing population context with less soil and water resources, the irrigated agriculture allows to increase the yield and the production of several crops in order to meet the increase of the food and fibers demands. To be efficient, an irrigation system must correctly evaluate amount of water and moments to which to apply the irrigation doses. Monitoring systems are crucial in areas of the planet where water is scarce and in environmental harsh conditions to ensure an efficient crop growth. Moreover, plant diseases and pests impact the yields of crops, an early detection allows to treat the disease or pest quickly and reduce the impact of these latter. In this paper, we propose an integrated approach which optimize the water use, the supply of fertilizers, the treatment of plant diseases and pests with a center-pivot equiped with camera by means of IoT and AI algorithms

Real time web-based toolbox for computer vision

The last few years have been strongly marked by the presence of multimedia data (images and videos) in our everyday lives. These data are characterized by a fast frequency of creation and sharing since images and videos can come from different devices such as cameras, smartphones or drones. The latter are generally used to illustrate objects in different situations (airports, hospitals, public areas, sport games, etc.). As result, image and video processing algorithms have got increasing importance for several computer vision applications such as motion tracking, event detection and recognition, multimedia indexation and medical computer-aided diagnosis methods. In this paper, we propose a real time cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can be run in real time and in a secure way. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different algorithms without the need to download, install and configure software or hardware. Moreover, the platform offers the access to the integrated applications from multiple users thanks to the use of Docker (Merkel, 2014) containers and images. Experimentations were conducted within three kinds of algorithms: 1. image processing toolbox. 2. Video processing toolbox. 3. 3D medical methods such as computer-aided diagnosis for scoliosis and osteoporosis.  These experimentations demonstrated the interest of our platform for sharing our scientific contributions related to computer vision domain. The scientific researchers could be able to develop and share easily their applications fastly and in a safe way

Multi-GPU based framework for real-time motion analysis and tracking in multi-user scenarios

Video processing algorithms present a necessary tool for various domains related to computer vision such as motion tracking, event detection and localization in multi-user scenarios (crowd videos, mobile camera, scenes with noise, etc.). However, the new video standards, especially those in high definitions require more computation since their treatment is applied on large video frames. As result, the current implementations, even running on modern hardware, cannot provide a real-time processing (25 frames per second, fps). Several solutions have been proposed to overcome this constraint, by exploiting graphic processing units (GPUs). Although they exploit GPU platforms, they are not able to provide a real-time processing of high definition video sequences. In this work, we propose a new framework that enables an efficient exploitation of single and multiple GPUs, in order to achieve real-time processing of Full HD or even 4K video standards. Moreover, the framework includes several GPU based primitive functions related to motion analysis and tracking methods, such as silhouette extraction, contours extraction, corners detection and tracking using optical flow estimation. Based on this framework, we developed several real-time and GPU based video processing applications such as motion detection using moving camera, event detection and event localizatio

Mémoire Business Plan : développement d’une Spin-Off en imagerie médicale

Mémoire de Master [60] en sciences de gestion (horaire décalé), Université catholique de Louvain, 201

Heterogeneous Computing for Vertebra Detection and Segmentation in X-Ray Images

The context of this work is related to the vertebra segmentation. The method we propose is based on the active shape model (ASM). An original approach taking advantage of the edge polygonal approximation was developed to locate the vertebra positions in a X-ray image. Despite the fact that segmentation results show good efficiency, the time is a key variable that has always to be optimized in a medical context. Therefore, we present how vertebra extraction can efficiently be performed in exploiting the full computing power of parallel (GPU) and heterogeneous (multi-CPU/multi-GPU) architectures. We propose a parallel hybrid implementation of the most intensive steps enabling to boost performance. Experimentations have been conducted using a set of high-resolution X-ray medical images, showing a global speedup ranging from 3 to 22, by comparison with the CPU implementation. Data transfer times between CPU and GPU memories were included in the execution times of our proposed implementation

LE PIVOT INTELLIGENT SURVEILLE ET OPTIMISE LES BESOINS DES CULTURES

Les réseaux de capteurs sans fil, l’Internet des objets et l’intelligence artificielle apportent conjointement un appui non négligeable dans la gestion quotidienne des infrastructures agricoles et contribuent à relever les défis de demain liés à la raréfaction des ressources, l’augmentation de la population ainsi que les changements climatique

Distributed Deep Learning: From Single-Node to Multi-Node Architecture

During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered quite important in the design of a time-performing multi-node architecture because DDL depends on the time required by all the nodes. The impact of computational resources (CPU and GPU) is also discussed since the GPU is known to speed up computations. Experimental results show that the local parallelism impacts the global speedup of the DDL depending on the neural model complexity and the size of the dataset. Moreover, our approach achieves a better speedup than Horovod