A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Wearable sensor technologies attract increasing attention for continuous monitoring of human health. Much effort is devoted to exploit well‐designed materials to realize superior abilities, such as high sensitivity, stability, and responsiveness. However, it hardly meets huge demands for practically wearable applications simply focusing on the development of sensing materials in isolation. Comprehensive consideration is given from upstream materials to endure market, including materials design, sensor assembly, signal analysis, theoretical foundation, and final system performance, such as sensitivity, stability, responsiveness, cost, comfortability, and durability. Herein, a systematic design is presented that combines a conductive fiber fabrication based on surface nanotechnology, device assembly process optimization, signal acquisition and analysis, and theoretical simulation, through a new multidisciplinary strategy integrating material science, textile technology, electromagnetics, and electronic engineering. The as‐constructed magnetic inductance sensing system shows approximately six‐times inductance change with regard to joint bending motions during rehabilitation exercises. This integrated design strategy offers a new concept, namely, a whole sensing system design, for wearable technologies in real‐time health monitoring applications.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.201900037

Whole System Design of a Wearable Magnetic Induction Sensor for Physical Rehabilitation

Structural and magnetic characterization of Al microalloying nanocrystalline FeSiBNbCu alloys

Technology has revolutionised all aspects of human life at all consecutive intervals
and Fourth Industrial Revolution is no different. Daily transport and
energy industries not only shape the future of a country’s economy, but also
make the economy highly yielding due to recent advances. Electric vehicles
(EV) have been rapidly invading the market share during recent years. The
advancements in EV and enhanced market share demand EV charging, being
more reliant on either conventional plug-in charging or wireless charging.
Given the limitations within battery related apparatus such as escalating
battery costs, higher weight and lower power density, wireless power transfer
(WPT) is a novel state of the art technology in energising. WPT has remarkable
characteristics such as enhanced flexibility, mobility, convenience
and safety, indicating potential benefits, if it is adopted for EV with similar
efficiency; for example, it can eliminate the use of charging cables.
Despite the fact that the wireless charges for EV, have undergone significant
development phase during the last decade, many design limitations are
yet to be addressed. Although the technology has been commercially outgrown,
key limitations such as limited efficiency over distance, limited driving
range, vulnerability to misalignments, or positional offsets are yet to be
researched. Moreover, although high system efficiency can be attained, the
distance variations between the transmitter and receiver and the misalignments
will impact the system efficiency. This thesis addresses the aforementioned
limitations and design challenges of the magnetic resonance WPT system,
and proposes a novel transmitter and receiver circuit and coil designs,
to minimise the impact of distance variations and coil misplacement, reduce
the size and improve charging performance.
This thesis focusses on inductive wireless power transfer (IWPT) which
is also referred to as magnetic resonance and reviews and contrasts other
WPT mechanisms. Additionally, it presents a detailed mathematical analysis
of inductive wireless power circuit model to obtain accurate modelling parameters.
Two and four loop strongly coupled magnetic resonance (SCMR)
wireless power systems have been mathematically analysed and their performance
has been evaluated. A novel combined, conformal strongly coupled
magnetic resonance system (CSCMR) has been combined with SCMR, in order
to minimise the dimensions of the receiver and compensate the coupling
factor due to distance variations between the transmitter and receiver. In the
second phase, additional inductors were added to the existing loosely coupled
system to obtain higher efficiencies over higher distances. The size of the system has significantly reduced due to the additional smaller transmitter
and receiver inductor which were added to the existing system to achieve
better performance. The validity of each design has been discussed via a set
of simulations, and their measurements have been obtained via prototypes.
Finally, a smart WPT charging system, consisting of six transmitter loops
and a sensor network array, for an autonomous parking space was developed.
The proposed method reduces the energy required for determining a
car’s location, eventually increasing the performance of the charger.

Inductive wireless power transmission for automotive applications

Objective This paper aims to present how physiological signals can be processed based on wavelet decomposition to calculate multiple physiological parameters in real-time on an embedded platform. Approach An ECG and PPG are decomposed to the appropriate scale based on a quadratic spline wavelet base in order to obtain high and narrow pulse peaks at the location of the mutation points. Based on the decomposed waveforms, feature points are positioned to calculate physiological parameters in real-time, including heart rate, pulse rate, blood oxygen, and blood pressure. The proposed algorithm has been implemented on a Texas Instruments' CC2640R2F. Main results The misdetection rate of feature point location based on the square wavelet decomposition waveform is only 0.57% in the acquired ECG and 0.23% in the acquired PPG. Heart rate and pulse rate are both highly correlated with the reference, both having correlation coefficients of 0.99. The pulse rate and heart rate are 3.85% (51/1326) and 2.94% (39/1326) outside the 95% consistency limit, respectively. The systolic and diastolic blood pressures are significantly associated with standard equipment measurements, with correlation coefficients of 0.87 and 0.83. The systolic and diastolic blood pressures were 5.88% (21/357) and 5.32% (19/357) outside the 95% consistency limit, respectively. Significance The real-time calculation of multiple physiological parameters based on wavelet decomposition on an embedded platform presented here shows outstanding accuracy.

https://iopscience.iop.org/article/10.1088/1361-6579/ab6f52/pdf

Wavelet-based real-time calculation of multiple physiological parameters on an embedded platform.

Wireless power charging powers the storage device without any cords which makes the device more compact, water proof, easy to use and provide even more comfort. The efficiency of power transfer depends on many factors, few of them are quality factor of the inductor coil, coupling coefficient, distance between receiver and transmitter coil, impact of core used for the coil. This paper discusses about wireless power transfer by inductive coupling technique, design considerations of the coil used for power transfer and tuning of LC tank circuit to attain resonant frequency between transmitter and receiver.

Wireless charging for low power applications using Qi standard

Continuous measurement of heart rate is necessary for monitoring the patients with heart ailments. A wearable which continuously measures heart rate of an individual by a method called reflectance-based photoplethysmography (PPG) computes the heart rate of an individual according to the volumetric changes in blood flowing through the body is developed. In order to make the device more compact as well as with IP67 and IP68 standard, wireless charging technique is employed because it helps to get rid of wires while charging. Following the Qi standard for designing wireless power receiver circuits makes the device interoperable and work with greater efficiency with reduced losses. Impedance matching and designing the circuit to operate under resonance condition increases coupling efficiency in case of inductive coupling.

Power optimisation for wearable heart rate measurement device with wireless charging

Sno2: Investigation of Optical, Structural, and Electrical Properties of Transparent Conductive Oxide Thin Films Prepared by Nebulized Spray Pyrolysis for Photovoltaic Applications

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity.

Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Indium-free MgSnO_3 transparent conductive oxide layer: investigation on structural, optical and electrical properties and photovoltaic performance analysis

G Kiruthiga

Papers

Wireless charging for low power applications using Qi standard

Power optimisation for wearable heart rate measurement device with wireless charging

Sno2: Investigation of Optical, Structural, and Electrical Properties of Transparent Conductive Oxide Thin Films Prepared by Nebulized Spray Pyrolysis for Photovoltaic Applications

Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Indium-free MgSnO_3 transparent conductive oxide layer: investigation on structural, optical and electrical properties and photovoltaic performance analysis