11,511 research outputs found

    Two-step detection of water sound events for the diagnostic and monitoring of dementia

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    International audienceA significant aging of world population is foreseen for the next decades. Thus, developing technologies to empower the independency and assist the elderly are becoming of great interest. In this framework, the IMMED project investigates tele-monitoring technologies to support doctors in the diagnostic and follow-up of dementia illnesses such as Alzheimer. Specifically, water sounds are very useful to track and identify abnormal behaviors form everyday activities (e.g. hygiene, household, cooking, etc.). In this work, we propose a double-stage system to detect this type of sound events. In the first stage, the audio stream is segmented with a simple but effective algorithm based on the Spectral Cover feature. The second stage improves the system precision by classifing the segmented streams into water/non-water sound events using Gammatone Cepstral Coefficients and Support Vector Machines. Experimental results reveal the potential of the combined system, yielding a F-measure higher than 80%

    Review of risk from potential emerging contaminants in UK groundwater

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    This paper provides a review of the types of emerging organic groundwater contaminants (EGCs) which are beginning to be found in the UK. EGCs are compounds being found in groundwater that were previously not detectable or known to be significant and can come from agricultural, urban and rural point sources. EGCs include nanomaterials, pesticides, pharmaceuticals, industrial compounds, personal care products, fragrances, water treatment by-products, flame retardants and surfactants, as well as caffeine and nicotine. Many are relatively small polar molecules which may not be effectively removed by drinking water treatment. Data from the UK Environment Agency’s groundwater screening programme for organic pollutants found within the 30 most frequently detected compounds a number of EGCs such as pesticide metabolites, caffeine and DEET. Specific determinands frequently detected include pesticides metabolites, pharmaceuticals including carbamazepine and triclosan, nicotine, food additives and alkyl phosphates. This paper discusses the routes by which these compounds enter groundwater, their toxicity and potential risks to drinking water and the environment. It identifies challenges that need to be met to minimise risk to drinking water and ecosystems

    IoT in smart communities, technologies and applications.

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    Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    Rationally engineered nanosensors: A novel strategy for the detection of heavy metal ions in the environment

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    Heavy metal ions (HMIs) have been mainly originated from natural and anthropogenic agents. It has become one of biggest societal issues due to their recognised accumulative and toxic effects in the environment as well as biological media. Key measures are required to reduce the risks posed by toxic metal pollutants existing in the environment. The increased research activities of HMIs detection, and use of technologies based on electrochemical detection that combine with engineered nanomaterials, is a key promising and innovative strategy that can potentially confine heavy metal poisoning. Deep understanding of the characteristics of the physicochemical properties of nanomaterials is highly required. It is also important to interpret the parameters at the nano-bio interface level that merely affect cross-interactions between nanomaterials and HMIs. Therefore, the authors outlined the state-of-the-art techniques that used engineeringly developed nanomaterials to detect HMIs in the environment. The possible novel applications of extensive and relatively low-cost HMIs monitoring and detection are discussed on the basis of these strengths. Finally, it is concluded by providing gist on acquaintance with facts in the present-day scenario along with highlighting areas to explore the strategies to overcome the current limitations for practical applications is useful in further generations of nano-world

    ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs

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    A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure

    Healing power of Malaysian seaweeds

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    Seaweeds are macroalgae, that do not possess true roots, stems or leaves. However, some of the larger species possess attachment organs or holdfasts that have the appearance of roots, and there may also be a stem-like portion called a stipe, which flattens out into broad leaf-like portion or lamina.Seaweeds are classified into three divisions, Rhodophyta (red algae), Chlorophyta (green algae) and Phaeophyta (brown algae). The red algae are characterized by having red pigments called phycobilins, which mask the color of chlorophyll though some may show different colors. The group is essentially marine; only a few of the approximately 4,000 species live in fresh water. The green algae are largely unicellular and non-marine. They are typically bright green since chlorophyll is not masked by other pigments. The colors of brown algae vary from olive green to dark brown, due to a preponderance of yellow pigments, particularly fucoxanthin, over chlorophyll. Human consumption of brown seaweed (66.5%), red seaweed (33%) and green seaweed (5%) is high in Asian countries, mainly Japan, China and Korea. Seaweeds have been used since ancient times as food, fodder, fertiliser and medicinal drugs. Tropical seaweed, rich in dietary fibres and bioactive phenolic compounds, for example Eucheuma cottonii and Sargassum polycystum are used in food and medicine due to its anti-diabetic, anti-hypertensive, cardiovascular protective and anti-oxidative tissue protective properties
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