A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

[EN] Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved

A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building

Due to the increased awareness of issues ranging from green initiatives, sustainability, and occupant well-being, buildings are becoming smarter, but with smart requirements come increasing complexity and monitoring, ultimately carried out by humans. Building heating ventilation and air-conditioning (HVAC) units are one of the major units that consume large percentages of a building’s energy, for example through their involvement in space heating and cooling, the greatest energy consumption in buildings. By monitoring such components effectively, the entire energy demand in buildings can be substantially decreased. Due to the complex nature of building management systems (BMS), many simultaneous anomalous behaviour warnings are not manageable in a timely manner; thus, many energy related problems are left unmanaged, which causes unnecessary energy wastage and deteriorates equipment’s lifespan. This study proposes a machine learning based multi-level automatic fault detection system (MLe-AFD) focusing on remote HVAC fan coil unit (FCU) behaviour analysis. The proposed method employs sequential two-stage clustering to identify the abnormal behaviour of FCU. The model’s performance is validated by implementing well-known statistical measures and further cross-validated via expert building engineering knowledge. The method was experimented on a commercial building based in central London, U.K., as a case study and allows remotely identifying three types of FCU faults appropriately and informing building management staff proactively when they occur; this way, the energy expenditure can be further optimized

Signature Inspired Home Environments Monitoring System Using IR-UWB Technology

Home monitoring and remote care systems aim to ultimately provide independent living care scenarios through non-intrusive, privacy-protecting means. Their main aim is to provide care through appreciating normal habits, remotely recognizing changes and acting upon those changes either through informing the person themselves, care providers, family members, medical practitioners, or emergency services, depending on need. Care giving can be required at any age, encompassing young to the globally growing aging population. A non-wearable and unobtrusive architecture has been developed and tested here to provide a fruitful health and wellbeing-monitoring framework without interfering in a user’s regular daily habits and maintaining privacy. This work focuses on tracking locations in an unobtrusive way, recognizing daily activities, which are part of maintaining a healthy/regular lifestyle. This study shows an intelligent and locally based edge care system (ECS) solution to identify the location of an occupant’s movement from daily activities using impulse radio-ultra wide band (IR-UWB) radar. A new method is proposed calculating the azimuth angle of a movement from the received pulse and employing radar principles to determine the range of that movement. Moreover, short-term fourier transform (STFT) has been performed to determine the frequency distribution of the occupant’s action. Therefore, STFT, azimuth angle, and range calculation together provide the information to understand how occupants engage with their environment. An experiment has been carried out for an occupant at different times of the day during daily household activities and recorded with time and room position. Subsequently, these time-frequency outcomes, along with the range and azimuth information, have been employed to train a support vector machine (SVM) learning algorithm for recognizing indoor locations when the person is moving around the house, where little or no movement indicates the occurrence of abnormalities. The implemented framework is connected with a cloud server architecture, which enables to act against any abnormality remotely. The proposed methodology shows very promising results through statistical validation and achieved over 90% testing accuracy in a real-time scenario

Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model

Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1–9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist’s conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

Introduction The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.Methods and analysis The study will recruit 15 inpatients (>60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system.Ethics and dissemination Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI

Amplitude analysis of Bs0→KS0K±π∓B^{0}_{s} \rightarrow K^{0}_{\textrm{S}} K^{\pm}\pi^{\mp} decays

International audienceThe first untagged decay-time-integrated amplitude analysis of B$_{s}^{0}$  → K$_{S}^{0}$ K$^{±}$π$^{∓}$ decays is performed using a sample corresponding to 3.0 fb$^{−1}$ of pp collision data recorded with the LHCb detector during 2011 and 2012. The data are described with an amplitude model that contains contributions from the intermediate resonances K$^{*}$(892)$^{0,+}$, K$_{2}^{*}$ (1430)$^{0,+}$ and K$_{0}^{*}$ (1430)$^{0,+}$, and their charge conjugates. Measurements of the branching fractions of the decay modes B$_{s}^{0}$ → K$^{*}$(892)$^{±}$K$^{∓}$ and ${B}_s^0\to \overset{\left(\hbox{---} \right)}{K^{*}}{(892)}^0\overset{\left(\hbox{---} \right)}{K^0}$ are in agreement with, and more precise than, previous results. The decays B$_{s}^{0}$  → K$_{0}^{*}$ (1430)$^{±}$K$^{∓}$ and ${B}_s^0\to \overset{\left(\hbox{---} \right)}{K_0^{*}}{(1430)}^0\overset{\left(\hbox{---} \right)}{K^0}$ are observed for the first time, each with significance over 10 standard deviations

Measurement of ψ\psi(2SS) production cross-sections in proton-proton collisions at s\sqrt{s} = 7 and 13 TeV

International audienceThe cross-sections of $\psi(2S)$ meson production in proton-proton collisions at $\sqrt{s}=13~\mathrm{TeV}$ are measured with a data sample collected by the LHCb detector corresponding to an integrated luminosity of $275~p\mathrm{b}^{-1}$. The production cross-sections for prompt $\psi(2S)$ mesons and those for $\psi(2S)$ mesons from $b$-hadron decays ($\psi{(2S)}\mathrm{-from-}b$) are determined as functions of the transverse momentum, $p_{\mathrm{T}}$, and the rapidity, $y$, of the $\psi(2S)$ meson in the kinematic range $2<p_{\mathrm{T}}<20~\mathrm{GeV}/c$ and $2.0<y<4.5$. The production cross-sections integrated over this kinematic region are \begin{equation*} \begin{split} \sigma(\mbox{prompt }\psi(2S),13~\mathrm{TeV}) &= {1.430 \pm 0.005(\mathrm{stat}) \pm 0.099 (\mathrm{syst})\mu\mathrm{b}},\\ \sigma(\psi(2S)\mathrm{-from-}b,13~\mathrm{TeV})&={0.426 \pm 0.002(\mathrm{stat}) \pm0.030 (\mathrm{syst})\mu\mathrm{b}}. \end{split} \end{equation*} A new measurement of $\psi(2S)$ production cross-sections in $pp$ collisions at $\sqrt{s}=7~\mathrm{TeV}$ is also performed using data collected in 2011, corresponding to an integrated luminosity of $614~{p\mathrm{b}^{-1}}$.The integrated production cross-sections in the kinematic range $3.5<p_{\mathrm{T}}<14~\mathrm{GeV}/c$ and $2.0<y<4.5$ are \begin{equation*} \begin{split} \sigma(\mbox{prompt }\psi(2S),7~\mathrm{TeV}) &={0.471 \pm0.001 (\mathrm{stat}) \pm 0.025 (\mathrm{syst})\mu\mathrm{b}},\\ \sigma(\psi(2S)\mathrm{-from-}b,7~\mathrm{TeV}) &={0.126\pm0.001 (\mathrm{stat}) \pm0.008 (\mathrm{syst})\mu\mathrm{b}}. \end{split} \end{equation*} All results show reasonable agreement with theoretical calculations

Observation of Several Sources of CPCP Violation in B+→π+π+π−B^+ \to \pi^+ \pi^+ \pi^- Decays

International audienceObservations are reported of different sources of CP violation from an amplitude analysis of B+→π+π+π- decays, based on a data sample corresponding to an integrated luminosity of 3  fb-1 of pp collisions recorded with the LHCb detector. A large CP asymmetry is observed in the decay amplitude involving the tensor f2(1270) resonance, and in addition significant CP violation is found in the π+π-S wave at low invariant mass. The presence of CP violation related to interference between the π+π-S wave and the P wave B+→ρ(770)0π+ amplitude is also established; this causes large local asymmetries but cancels when integrated over the phase space of the decay. The results provide both qualitative and quantitative new insights into CP -violation effects in hadronic B decays