Considering the environmental field around the Congo Basin area (in Central Africa), many initiatives implement and communicate on their activities via the internet, yet the information they provide remains very little exploited, even inaccessible. The data is scattered on the internet and it is quite difficult to find information in a fairly precise way. Despite the existence of Internet research services (search engines) developed to facilitate the search for information in the vast data network that is the Internet, there are still concerns about quality, and the relevance of information provided in as research results. In this context, we present in this paper a construction approach and the architecture of a search engine dedicated to the environment around the Congo Basin area. This paper develops a theoretical approach that uses scientific analysis and empirical approaches to conceptualize the optimization of the relevance of the results of a thematic search engine by aggregating tools. This approach is a compilation of ideas, methods and tools that, put together, will improve the relevance of the results of a thematic research.
With the proliferation of relatively cheap Internet of Things (IoT) devices, smart environments have been highlighted as an example of how the IoT can make our lives easier. Each of these ‘things’ produces data which can work in unison to react to its users. Machine learning makes use of this data to make inferences about our habits and activities, such as our buying preferences or likely commute destinations. However, this level of human inclusion within the IoT relies on indirect inferences from the usage of these devices or services. Activity recognition is already a widely researched area and could provide a more direct way of including humans within this system. This research explores the feasibility of using a cost effective, unobtrusive, single modality ground-based sensor matrix to track subtle pressure changes to predict user activity, in an effort to assess its ability to act as an intermediary interface between humans and digital systems such as the IoT.
Low blood glucose (BG) or hypoglycemia (HYPO) can lead to severe health complications such as weakness and unconsciousness. To avoid problems BG self-management is needed. We developed a non-invasive breathing system (HYPOalert) to detect HYPO in human-breath, that sends warning alerts and data visualization to monitor progress. This paper presents two HYPOalert prototype iterations with testing results. Of 14 Type 1/2 diabetics tested, only 10% werepleased with existing monitoring systems and 85% expressed interest in using HYPOalert more than 20x a day. The usability study showed that 92% agreed-strongly agreed with the HYPOalert design, including color/menus/navigation/typography; and 64% felt positive about the apps consistency, flexibility, and info architecture. A post-test survey provided a satisfaction score: 6.64/10, with an open-ended interview showing that HYPOalert could positively impact lifestyle practices, self-managing, and help advance an understanding of the disease.
The importance of accurate and efficient positioning and tracking is widely understood. However, there isa pressing lack of progress in the standardisation of methods, as well as generalised framework of theirevaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensorsused for location estimation. The intent of this review is to take account of this taxonomy and to providea wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities,as well as popular fusion and integration techniques, discussing how their combinations can help invarious application settings. Firstly, we present the fundamental mechanics behind sensors employed bythe localisation community. Furthermore we outline the formal theory behind prominent fusion methodsand provide exhaustive implementation examples of each. Finally, we provide points for future discussionregarding localisation sensing, fusion and integration methods.
Recent trends have shown a migration of software from local machines to server-based services. These service-based networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity.
A first step towards emotional well-being is to monitor, understand and reflect upon one’s feelings and emotions. A number of personal emotion-tracking applications are available today. In this paper we describe an examination of these applications which indicates that many of the applications do not provide sufficient support for monitoring a full spectrum of emotional data or for analysing or using the data that is provided. To design applications that better support emotional well-being, the full capabilities of the Internet of Things should be utilized. The paper concludes with a description of how Internet of Things technologies can enable the development of systems that can more accurately capture emotional data and support personal learning in the area of emotional health.
The ubiquitous nature of mobile devices like smartphones and tablets make them ideal platforms for engaging users in Ecological Momentary Assessments (EMA). In EMA, participants are repeatedly assessed frequently (daily or multiple times per day) through a set of questionnaires. In this short paper, we present a secure EMA platform developed using Android mobile devices. The platform is flexible and can scale up to perform data mining tasks for sentiment analysis in patient rehabilitation settings.
Over the years Radial Basis Function (RBF) Kernel Machines have been used in Machine Learning tasks, but there are certain flaws that prevent their usage in some up-to-date applications (e.g., some Kernel Machines suffer from fast growth number of learning parameters whilst predicting data with large number of variations). Besides, Kernel Machines with single hidden layer have no mechanisms for features selection in multidimensional data space, and machine-learning task becomes intractable with enlargement of the data available for analysis. To address these issues, this paper investigates the usage of a framework for “deep learning” architecture composed of multilayered adaptive non-linear components – Multilayer RBF Kernel Machine. To be precise, three different approaches of features selection and dimensionality reduction to train RBF based on Multilayer Kernel Learning are explored, and comparisons between them in terms of accuracy, performance and computational complexity are made. As opposed to the “shallow learning” algorithm with usually single layer architecture, results show that the multilayered system produces better results with large and highly varied data. In particular, features selection and dimensionality reduction, as a class of the multilayer method, shows results that are more accurate. This paper proposes a novel scheme based on deep Multilayer RBF Kernel Machine learning for sleep apnea detection and quantification using statistical features of ECG signals. The results obtained show that the newly proposed approach provides significant accuracy improvements compared to state-of-the-art methods. Because of its noninvasive and low-cost nature, this algorithm has the potential for numerous applications in sleep medicine.
Obesity, overweight, allergy and additives related disease are common health problems worldwide nowadays. Many of these problems directly relate to food consumption and dietary habits. Hence keep tracking food intake and knowing the consumed food ingredients are essential for a healthy life. However, tracking daily consumption is tedious, and seldom be kept for long. This paper presented a mobile solution to leverage these troublesome and provide health advices on food consumption adaptively. Users can simply take photos of their meals as they usually do for dietary record. Our system then automatically recognizes the food type and provides useful statistics for analysis. We can know better the packaged food with barcode scanning and text recognition of food labels. All captured information provide basis for proper alert on allergen, suggestions to achieve health and balanced dietary. A preliminary study reveals that this is welcomed by most users care about their dietary.
In the industrial environments, it is common that robotic or remote interaction with both rigid objects and soft or deformable objects is required. However, it is usual in such an environment that only one mode of manipulation is used, and that little or no distinction is made between rigid or deformable objects. The ability to “feel” or touch an object easy a naturalistic way to determine what type of object is being manipulated. By feeling an object appropriate manipulation techniques can be applied. A novel Virtual Reality (VR) interface is presented that incorporates tactile feedback in order to “feel” objects being manipulated. Incorporation of an important extra “sense” into such a system allows far more nuanced and dexterous interaction to occur in manufacturing environments that may be “messy”, have imprecisely located objects or that have a range of different materials present.
In this article, we propose a system for augmented card playing with a projector and a camera to add playfulness and increase communication among players of a traditional card game. The functionalities were derived on the basis of a user survey session with actual players. Playing cards are recognized using a video camera on the basis of a template matching without any artificial marker with an accuracy of > 0.96. Players are also tracked to provide person-dependent services using a video camera from the direction of their hands appearing over a table. These functions are provided as an API; therefore, the user of our system, i.e., a developer, can easily augment playing card games. The Pelmanism game was augmented on top of the system to validate the concept of augmentation. The results showed the feasibility of the system’s performance in an actual environment and the potential of enhancing playfulness and communication among players. Received on 14 April 2016; accepted on 02 July 2016; published on 17 May 2017
Activity recognition approaches have been applied in home ambient systems to monitor the status and well-being of occupant especially for home care systems. With the advancement of embedded wireless sensing devices, various applications have been proposed to monitor userâ ĂŹ s activities and maintain a healthy lifestyle. In this paper, we propose and evaluate a Smart Medication Alert and Treatment Electronic Systems (SmartMATES) using a non-intrusive wearable activity recognition sensing system to monitor and alert an user for missing medication prescription. Two sensors are used to collect data from the accelerometer and radio transceiver. Based on the data collected, SmartMATES processes the data and generate a model for the various actions including taking medication. We have evaluated the SmartMATES on 9 participants. The results show that the SmartMATES can identify and prevent missing dosage in a less intrusive way than existing mobile application and traditional approaches.
As the scale of small satellite network is not large and the transmission cost is high, it is necessary to optimize the routing problem. We apply the traditional time-expanded graph to model the data acquisition of small satellite network so that we can formulate the data acquisition into a multi-commodity concurrent flow optimization problem (MCFP) aiming at maximizing the throughput. We use an approximation method to accelerate the solution for MCFP and make global optimization of routing between satellite network nodes. After the quantitative comparison between our MCFP algorithm and general augmented path maximum flow algorithm and exploring the detail of the algorithm, we verify the approximation algorithm’s reasonable selection of routing optimization in small satellite network node communication.
Virtual channel (VC) flow control proves to be an alternative way to promote network performance, but uniform VC allocation in the network may be at the cost of chip area and power consumption. We propose a novel VC number allocation algorithm customizing the VCs in network based on the characteristic of the target application. Given the characteristic of target application and total VC number budget, the block probability for each port of nodes in the network can be obtained with an analytical model. Then VCs are added to the port with the highest block probability one by one. The simulation results indicate that the proposed algorithm reduces buffer consumption by 14.58
Ubiquitous in-network caching is one of key features of Information Centric Network, together with receiver-drive content retrieval paradigm, Information Centric Network is better support for content distribution, multicast, mobility, etc. Cache placement strategy is crucial to improving utilization of cache space and reducing the occupation of link bandwidth. Most of the literature about caching policies considers the overall cost and bandwidth, but ignores the limits of node cache capacity. This paper proposes a G-FMPH algorithm which takes into account both constrains on the link bandwidth and the cache capacity of nodes. Our algorithm aims at minimizing the overall cost of contents caching afterwards. The simulation results have proved that our proposed algorithm has a better performance.
Space-Ground Integrated Network (SGIN) is the future network, and the satellite-earth link channel is one critical part of the SGIN. This paper simulates the satellite-earth link channel of SGIN based on the simulation environment of OMNeT++. We set up the model of space-ground network and satellite-earth link channel. The satellite-earth link channel includes two main parts, one part is the free space channel that ranges from the satellites to the aerosphere and the other part is the channel that ranges from aerosphere to the ground terminals. According to the ITU Recommendations, we simulate the satellite-earth link channel of the SGIN, from the results of the simulation. We analyze the satellite-earth link channel attenuation, obtaining the packet delay and packet arrival rate of the SGIN as well.
In this paper, we introduce a telemedicine architecture for supporting emergency patient stabilization and patient transportation to a fully equipped health care center. In particular, we focus on the description of a set of mobile apps, designed for supporting data recording and transmission during patient transportation by ambulance. Some of the apps are interfaced to the monitoring devices in the ambulance, and automatically send all the recorded data to a server at the destination center. One additional app enables the travelling personnel to input and transmit further significant patient data, or comments. At the destination center, the specialist physician is allowed to inspect the data as soon as they are received, possibly providing immediate advice. The exploitation of the apps also allows to maintain the transportation data over time, for medico-legal purposes, or to perform a-posteriori analyses. Some first evaluation results are discussed in the paper.
With service types and requirements of broadband satellite internet continuously increasing, improving QoS (Quality of Service) of satellite internet has attracted extensive attention. To reduce the impact of self-similarity caused by various of service traffic sources converging on satellite communication system, this paper establishes a novel model from the perspective of self-similar traffic prediction. A method combinating wavelet transform and ARIMA (Autoregressive Integrated Moving Average) model to predict self-similar traffic of satellite internet is proposed. The optimal prediction model is presented. The number selection of prediction samples and the impact of prediction steps on the accuracy of the prediction system are discussed, and the parameters are addressed. Simulation results show ARIMA model with a combination of wavelet transform can achieve a better prediction than that of the traditional autoregressive model, not utilizing wavelet technology.
Nowadays, SRR (super resolution image reconstruction) technology is a very effective method in improving spatial resolution of images and obtaining high-definition images. The SRR approach is an image late processing method that does not require any improvement in the hardware of the imaging system. In the SRR reconstruction model, it is the key point of the research to choose a proper cost function to achieve good reconstruction effect. In this paper, based on a lot of research, Lorenzian norm is employed as the error term, Tikhonov regularization is employed as the regularization term in the reconstruction model, and iteration method is employed in the process of SRR. In this way, the outliers and image edge preserving problems in SRR reconstruction process can be effectively solved and a good reconstruction effect can be achieved. A low resolution MRI brain image sequence with motion blur and several noises are used to test the SRR reconstruction algorithm in this paper and the reconstruction results of SRR reconstruction algorithm based on L2 norm are also be used for comparison and analysis. Results from experiments show that the SRR algorithm in this paper has better practicability and effectiveness.
An increasing global population, the rise in number of chronic disease patients and the threat of global epidemics have made the way for technology as a potential answer to many of these problems. Health insurance can contribute to the resolution of some of these issues but insurers need to transition from simple “Payers” to “Players” in order to achieve that. They need to become points of reference on which the customer and the health care system can count on. This is possible and is strictly related to connected insurance and in particular to wearables and devices that are able to gather vital data from patients and share them with the care givers.
