Surround-Screen Mobile based Projection: Design and Implementation of Mobile Cave Virtual Reality

Virtual Reality (VR) is the employment of computer devices to simulate real or imaginary environments. Conventional user interfaces stresses the usage of screen displays to interact with the developed environment. The prospect of VR enables the user to be virtually inside the environment. CAVE is one kind of room-shaped VR technology that displays the environment on its walls. We observe some common limitations in the existing CAVE technology, such as fixed space requirements and the difficulty in moving it. In the current work, we present a novel mobile-CAVE system that uses light-weighted materials and portable powered devices. It solves the limitation of re-using the allocated space by packing it and the ability to move it easily. We have assessed our technology by performing usability analysis, power consumption, and mobility experimental study. The profound experiments demonstrated the efficiency of the proposed technology in comparison with former CAVE system. OAPAScopu

Reference layer artefact subtraction (RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI

Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG–fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods

Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: implications for motion artefact correction

Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject’s head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity. Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals. Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data

Targeting SARS-CoV2 Spike Protein Receptor Binding Domain by Therapeutic Antibodies

The Authors As the number of people infected with the newly identified 2019 novel coronavirus (SARS-CoV2) is continuously increasing every day, development of potential therapeutic platforms is vital. Based on the comparatively high similarity of receptor-binding domain (RBD) in SARS-CoV2 and SARS-CoV, it seems crucial to assay the cross-reactivity of anti-SARS-CoV monoclonal antibodies (mAbs) with SARS-CoV2 spike (S)-protein. Indeed, developing mAbs targeting SARS-CoV2 S-protein RBD could show novel applications for rapid and sensitive development of potential epitope-specific vaccines (ESV). Herein, we present an overview on the discovery of new CoV followed by some explanation on the SARS-CoV2 S-protein RBD site. Furthermore, we surveyed the novel therapeutic mAbs for targeting S-protein RBD such as S230, 80R, F26G18, F26G19, CR3014, CR3022, M396, and S230.15. Afterwards, the mechanism of interaction of RBD and different mAbs were explained and it was suggested that one of the SARS-CoV-specific human mAbs, namely CR3022, could show the highest binding affinity with SARS-CoV2 S-protein RBD. Finally, some ongoing challenges and future prospects for rapid and sensitive advancement of therapeutic mAbs targeting S-protein RBD were discussed. In conclusion, it may be proposed that this review may pave the way for recognition of RBD and different mAbs to develop potential therapeutic ESV

Wearable RealTime Heart Attack Detection and Warning System to Reduce Car Accidents in Qatar

Introduction Fatal car accidents have become an alarming issue all over the globe. A sudden medical condition such as a heart attack causes medical symptoms that lead a driver to lose consciousness while driving and consequently leads to a crash. Many studies have demonstrated the high correlation between the driver's sudden medical conditions and involving in a car crash [1][2]. Therefore, to reduce car crashes from the driver's sudden illness from heart-attack as well as save the driver's life in a timely manner, in this work, we discuss the development of a portable wearable system that can continuously monitor the driver for any early symptoms of heart attack and inform him before losing conciuous to stop the car as well as inform medical caregivers to save life. Background Myocardial infarction (MI) is the medical term for the medical condition commonly known as a heart attack, a serious medical emergency in which the blood supply to the heart is suddenly blocked, usually by a blood clot, leading to damage heart muscle [3]. A complete blockage of a coronary artery is a 'STEMI' heart attack (ST-elevation MI), whereas a partial blockage would be a 'NSTEMI' heart attack (a non-ST-elevationMI) [4]. The average, resting heart rhythm has a QRS-complex following a P-wave and followed by a T-wave, as illustrated in Figure 1(a). A STEMI heart attack will cause an elevation in the ST-complex (Figure 1(b)), whereas a NSTEMI heart attack would not signify ST elevation, but nonetheless can cause ST-segment depression or T-wave inversion (Figure 1(c)), which can be detected immediately by a real-time device to save the driver's life. Method The prototype system consists of two subsystems (Figure 2) that communicate wirelessly using Bluetooth low energy (BLE) technology: wearable sensor subsystem, and an intelligent heart attack detection and warning subsystem. Wearable Subsystem: The wearable chest-belt sub-system includes dry electrodes (reference and two electrodes for differential acquisition), analogue front end (AFE), power management module, and RFDuino microcontroller with BLE. This subsystem acquires the ECG signals from human body continuously and sends these raw measurements wirelessly using BLE technology to the intelligent subsystem. Reusable and smaller dimension dry electrodes (Cognionics, Inc) were embedded in a chest belt to be worn by a car driver. AD82832 AFE is an integrated signal conditioning block to extract, amplify (60 dB gain), and filter (0.48-41 Hz) ECG signal in the presence of noisy conditions. Lithium Polymer (LiPo) battery of 3.7 V (1000 mAH) with the Microchip MCP73831 charge controllers, and Texas instruments' TPS61200 voltage regulators to supply 3 V to the wearable system. The miniaturized ARM Cortex M0 RFDuino microcontroller digitizes the signal at 500 Hz sampling rate and transmits the acquired signal through built-in BLE to decision making subsystem. Intelligent Decision-making Subsystem: This subsystem will receive the ECG signals from the wearable subsystem continuously. It is capable of processing, analyzing the received ECG signals, and making the right decision using support vector machine (SVM) algorithm to classify the normal and abnormal ECG signal to detect heart attack symptoms. This subsystem was built around the single board computer, Raspberry Pi 3 (RPi3) along with SIM 908 GSM and GPS module for location information and alerting service. Multi-threaded python code was written for RPi3 to automatically acquire, buffer, baseline correction and digital smoothing and analyse the ECG data. SVM algorithm was implemented in RPi 3 and used for real-time abnormality detection using the trained model and classification was done using LIBSVM, an open source library [5]. 4-fold cross-validation was used to evaluate classification accuracy. SIM908 GSM+GPS shield attached on the RPi3 to provide car location (latitude, longitude) and to connect to the mobile network for generating an automatic call to medical emergency. This subsystem is designed to take power from the car battery using Cigarette Lighter Socket, which powers the system only when the car's engine is ON. To develop the intelligent program for decision-making subsystem, public MIT-BIH ST change database [6] was used to train a SVM model for normal, ST-elevated, and T-inverted ECG-beats with the time domain (TD), frequency domain (FD) and extended time-frequency domain (TFD) features extracted. The TD features mean, variance, skewness, kurtosis, and coefficient of variation and the FD features spectral flux, spectral entropy and spectral flatness were calculated to spot abnormalities in the ECG-beats. Three time-frequency (TF) distributions were also used in this study: Wigner-Ville Distribution (WVD), Spectrogram (SPEC), and Extended Modified B-Distribution (EMBD). Result and Discussion Recorded ECG Traces: It was clearly revealed from Fig. 5 that the ECG signal transmitted using the prototyped system is in clinical grade. Training SVM: Five hundred traces from each patient and total 2500 traces from MIT-BIH database having either normal or abnormal heart rhythm were segmented and averaged for each case (Figure 6 (A, B, & C)). The power spectral of the signal in Figure 6 (D, E & F) shows that the power spectral density peaks appear at different frequencies for normal and abnormal ECG signals. This reflects that the FD feature can help in classifying the ECG signals. However, TD, FD, and TFD features provide an insight on the signal while compensating for the noise or motion artefacts. Classification using SVM: Table 1 below summarizes the accuracy of the prototyped device. EMBD produces higher accuracy in classification of ECG signal. Conclusion This work shows the possibility to detect driver's heart attack reliably using the developed prototype system. SVM machine learning algorithm that was trained with a sufficiently high number of training data can classify STEMI or NSTEMI with approximately 97.4% and 96.3% accuracy respectively when the extended TF features (with EMBD distribution) were used for training and classification. The maximum current drawn by the wearable chest-belt subsystem during continuous acquisition is 9.3 mA, which ensures the life span of a 1000 mAh LiPo battery is 75 hours, once it is fully charged and therefore it can be expected that the device can run longer without requiring recharging daily.qscienc

Design and parametric analysis of a wide-angle polarization-insensitive metamaterial absorber with a star shape resonator for optical wavelength applications

Optical wavelengths considered as the key source of electromagnetic waves from the sun, and metamaterial absorber (MMA) enables various applications for this region like real invisible cloaks, color imaging, magnetic resonance imaging, light trapping, plasmonic sensor, light detector, and thermal imaging applications. Contemplated those applications, a new wide-angle, polarization-insensitive MMA is presented in this study. The absorber was formatted with three layers that consisted of a sandwiched metal-dielectric-metal structure. This formation of metamaterial absorber showed a good impedance match with plasmonic resonance characteristics. The structure was simulated using the FIT and validated with the FEM. A variety of parametric studies were performed with the design to gain best physical dimension. The mechanism of absorption also explained immensely by various significant analysis. The design had average 96.77% absorption from wavelengths of 389.34 nm to 697.19 nm and a near-perfect absorption of 99.99% at a wavelength of 545.73 nm for TEM mode. For an ultra-wide bandwidth of 102 nm, the design exhibited above 99% absorbance. The proposed is wide-angle independent up to 60° for both TE and TM mode, which is useful for solar energy harvesting, solar cell, and solar thermophotovoltaics (STPV). This MMA can be used for an optical sensor or as a light detector. Moreover, this proposed design can be employed in some applications mentioned above.This research is funded by Universiti Kebangsaan Malaysia , Malaysia under research grant code: GUP-2019-011 . The project was also funded by the Deanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabia under grant no. KEP-24-135-38 . The authors, therefore, acknowledge with thanks DSR technical and financial support.Scopu

Cross coupled interlinked split ring resonator based epsilon negative metamaterial with high effective medium ratio for multiband satellite and radar communications

In this paper, a cross coupled interlinked split ring resonator based (CCI-SRR) based metamaterial has been presented. Epsilon negative (ENG) with a highly effective medium ratio (EMR) is attained in this metamaterial. The metamaterial unit cell consists of one square shaped split ring resonator and two rectangular rings. The rectangular rings reside within the outer square split ring. Two internal rings are coupled together by using a cross-shaped metal segment. These inner rings are also interlinked to the outer ring by using metal strips. Coupling causes to increase the electrical length and modifies the inductance of the unit cell. Multiple resonances covering C, X and Ku-band are achieved due to the interconnection of rings. The symmetric nature of the CCI-SRR unit cell exhibits unique quality to minimize noise and harmonics effect. The unit cell is designed on FR4 substrate with a thickness of 1.6 mm. The overall dimension of the unit cell is 0.124λ × 0.124λ, where λ is the wavelength calculated at a lower resonance frequency of 4.15 GHz. Three resonances are obtained for |S21| at frequencies of 4.15 GHz, 10.38 GHz and 14.93 GHz performing numerical simulation in CST microwave studio. Permittivity, permeability, refractive index and impedance are explored by using the Newton-Ross-Weir (NRW) method. ENG performance is observed in frequencies ranging from 3.95 to 5.65 GHz, 9.57–11.46 GHz, 13.68–16 GHz. Near-zero refractive index is attained within the frequency ranges, 4.16–5.75 GHz, 10.16–11.58 GHz, 14.46–16 GHz. An LC equivalent circuit is designed, and component values are achieved by Advanced Design Software (ADS) justifying |S21| with CST result. The study of electromagnetic wave interaction between the unit cell and the double positive medium reveals that the unit cell exhibits evanescent wave properties. The compact nature of the unit cell is confirmed by calculating EMR with a value of 8.03. The electromagnetic coupling effect is examined for 2 × 2 array in various orientations. The |S21| performance of 2 × 2, 4 × 4 and 8 × 8 is matched with the unit cell. Due to symmetric patterns, near-zero refractive index, negative permittivity and high EMR, the proposed unit cell can be used to enhance the performance of microwave devices used for C, X and Ku-bands, especially Satellite and Radar communications.The project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant no. KEP-24-135-38. The authors, therefore, acknowledge with thanks DSR technical and financial support. Also, it was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors. The author also like to thanks Universiti Kebangsaan Malaysia research grant code: KK-2020-005.Scopu

A mutual coupled concentric crossed-Line split ring resonator (CCSRR) based epsilon negative (ENG) metamaterial for Tri-band microwave applications

A metamaterial design and its analysis based on an epsilon negative concentric crossed-line split ring resonator (CCSRR) have been presented in this paper. The CCSRR unit cell structure is the amendment of the typical concentric split ring resonator (CSRR). The inserted crossed line increases the electrical length of the presented CCSRR unit cell. The dimension of the proposed CCSRR unit cell is 10 x 10 x 1.575 mm3 and it is printed on the Rogers RT 5880 substrate material. The transmission frequency ranges from 6.33 GHz to 6.65 GHz, 10.42 GHz to 10.73 GHz, and 13.21 GHz to 13.42 GHz which covers the frequency bands of C, X, and Ku-band of microwave applications. A complete analysis of scattering parameters, effective medium parameters, mutual coupling effect as well as the unit cell characteristics with electromagnetic analysis have been performed in this study. The proposed CCSRR unit cell structure exhibits epsilon negative characteristics in the frequency ranges of 6.53 GHz to 6.96 GHz, 10.63 GHz to 10.91 GHz, and 13.37 GHz to 13.40 GHz. Experimental validation has also been performed by measuring the scattering parameters of the proposed CCSRR unit cell and its array structure. Furthermore, the capacitive coupling among the concentric split ring resonators within the 1 x 2 and 2 x 2 array structures have been studied which is based on the near field split gaps that lead to the fundamental inductive-capacitive resonances. Besides, the effective medium ratio 4.5 implies the effectiveness and compactness of the proposed CCSRR unit cell structure. The compactness, effective medium parameters, and effective medium ratio make the proposed CCSRR metamaterial appropriate for the microwave applications. 2020 The Author(s)This research is funded by Universiti Kebangsaan Malaysia Research grant GUP-2020-074 . The project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant no. KEP-24-135-38. The authors, therefore, acknowledge with thanks to DSR technical and financial support.Scopu

Photo-Voltaic (PV) Monitoring System, Performance Analysis and Power Prediction Models in Doha, Qatar

This study aims developing customized novel data acquisition for photovoltaic systems under extreme climates by utilizing off-the-shelf components and enhanced with data analytics for performance evaluation and prediction. Microcontrollers and sensors are used to measure meteorological and electrical parameters. Customized signal conditioning, which can withstand high-temperature along with microcontrollers’ development boards enhanced with appropriate interfacing shields and wireless data transmission to iCloud IoT platforms, is developed. In addition, an automatically controllable in-house electronic load of the PV system was developed to measure the maximum power possible from the system. LabVIEW™ program was used to allow ubiquitous access and processing of the recorded data over the used IoT. Furthermore, machine learning algorithms are utilized to predict the PV output power by utilizing data collected over a two-year span. The result of this study is the commissioning of original hardware for PV study under extreme climates. This study also shows how the use of specific ML algorithms such as Artificial Neural Network (ANN) can successfully provide accurate predictions with low root-mean-squared error (RMSE) between the predicted and actual power. The results support reliable integration of PV systems into smart-grids for efficient energy planning and management, especially for arid and semi-arid regions

Machine Learning in Wearable Biomedical Systems

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems