The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related to online learning in the form of tweets. Mining such tweets to develop a dataset can serve as a data resource for different applications and use-cases related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore, this work presents a large-scale open-access Twitter dataset of conversations about online learning from different parts of the world since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. The paper also briefly outlines some potential applications in the fields of Big Data, Data Mining, Natural Language Processing, and their related disciplines, with a specific focus on online learning during this Omicron wave that may be studied, explored, and investigated by using this dataset.

Capsule endoscopy is a promising diagnostic tool for the entire gastrointestinal tract. Since a patient swallows the capsules, their size must be sufficiently small. The principal built-in components are cameras, silver-oxide batteries, light emitting diodes, and an antenna for transmitting the video. For diagnosis and treatment, the precise localization of the capsules for specific video frames is required. Recently, static magnetic localization of these  apsules with an integrated permanent magnet showed promising results. However, in the state-of-the-art, relatively large magnets compared to the small capsules were used. Therefore, in this extended paper, the localization performance of a recently proposed optimized differential static magnetic localization method for different-sized discs and ring magnets was evaluated. The ring magnets were designed for integration with the two batteries of commercial capsules. The magnets were evaluated in static and dynamic scenarios to evaluate the performance of the method in a patient’s daily life. It was revealed that the mean position and orientation errors did not exceed 5 mm and 4°, respectively, for all applied magnets except for the 1.5 mm and 3 mm long disc magnets. Moreover, the results indicated that the ferromagnetic batteries of capsule endoscopes increase the localization performance when they are centered within a diametrical ring magnet. Overall, it was revealed that the localization performance of the optimized differential method is significantly better than the state-of-the-art even when the magnet volume is significantly reduced compared to previous work. Therefore, it was concluded that a 5 mm long disc magnet or a ring magnet are excellent candidates for integration into a commercial capsule for magnetic localization and yield the advantage of being passive magnetic sources.

We analyze the outage probability of integrated ground-air-space free-space optical (FSO) communication links for different atmospheric turbulence channel models. The performance of ground to high altitude platforms (HAPS), HAPS to geostationay Earth orbit (GEO) satellite, HAPS to HAPS and ground to GEO satellite is investigated for various link configurations (downlink, horizontal, and uplink) and parameters such as zenith angle, channel state, horizontal and vertical deviations, altitude, beam waist, receiver aperture diameter, wind speed, and visibility. The atmospheric attenuation (the effects of fog, cloud and volcanic activities), atmospheric turbulence, angle of arrival (AOA) fluctuations and pointing error are included in the considered model. Closed-form expressions for the probability density function (PDF) and cumulative distribution function (CDF) are obtained for Lognormal, Gamma and exponentiated Weibull distributed channel models. The benefit of using HAPS is shown by comparison of outage probabilities for direct ground to GEO satellite and HAPS assisted ground to GEO satellite links.

This paper presents a step-up DC-DC converter that uses a stepwise gate-drive technique to reduce the power FET gate-drive energy by 82%, allowing positive efficiency down to an input voltage of ±0.5 mV—the lowest input voltage ever achieved for a DC-DC converter as far as we know. Below ±0.5 mV the converter automatically hibernates, reducing quiescent power consumption to just 255 pW. The converter has an efficiency of 63% at ±1 mV and 84% at ±6 mV. The input impedance is programmable from 1 Ω to 600 Ω to achieve maximum power extraction. A novel delay line circuit controls the stepwise gate-drive timing, programmable input impedance, and hibernation behavior. Bipolar input voltage is supported by using a flyback converter topology with two secondary windings. A generated power good signal enables the load when the output voltage has charged above 2.7 V and disables when the output voltage has discharged below 2.5 V. The DC-DC converter was used in a thermoelectric energy harvesting system that effectively harvests energy from small indoor temperature fluctuations of less than 1°C. Also, an analytical model with unprecedented accuracy of the stepwise gate-drive energy is presented.

Sustainability and climate monitoring efforts create a need for long-term remote sensing of  large geographic areas.  However, environmental monitoring in remote areas of developing countries remains impeded by a lack of low-cost, scalable IoT~solutions.  Whereas IoT systems for remote sensing abound,  they mostly are either low-cost or suitable for large areas, but not both. In this paper, we present a low-cost low-power network solution for remote sensing of areas up to hundreds of square kilometers.  Taking advantage of LoRa technology, we develop a self-organizing mesh network that can be scaled to a hundred and more nodes. Scalability is achieved by developing methods that mitigate packet collisions during data collection.  We present a protocol, called CottonCandy, with which nodes self-organize in a spanning-tree network topology in a distributed fashion. A power profile on a  custom-built circuit board shows that CottonCandy nodes can run  thousands of duty cycles on 2~AA batteries, sufficient to operate for years in many applications. Using off-the-shelf components, the cost of a CottonCandy node is less than US-$ 15.  Evaluations by simulation show that CottonCandy networks with 100 nodes achieve a packet delivery ratio of >90%. Measurements of an outdoor deployment with 15~nodes corroborate the high packet delivery ratio in a real-life setting.

The impressive growth of the demand for wireless services for 5G and beyond, particularly encouraged by the Internet of Things, Internet of Everything, etc., raised up the importance of the problems related to the drastic increase (among others) of spectrum efficiency of the systems. In this regard two opportunistic trends were recently discussed by practical professionals and academicians: non-orthogonal spectrum sharing (NOMA transmission) for user equipment (UE) at physical layer level and decentralized Blockchain-enabled Radio Access (B-RAN) paradigm at network layer label. Both of them promise a significant growth on the transmission rate and the spectrum efficiency with low latency, etc. not only for 5G networks, but further, towards 6G networks and so on. Moreover, it is a fact that the number of published works for B-RAN (some of them considered hereafter) is very impressive, however, the attention to its modeling aspects is rather limited. The following material is dedicated to present several theoretical aspects of the small-scale modeling of B-RAN based on two well-known principles: continuous time Markov stochastic processes with discrete set of states for B-RAN modeling and self-similar processes for its traffic modeling.

The digital transformation is characterized by the convergence of technologies — from the Internet of Things (IoT) to Edge-Fog-Cloud computing, Artificial Intelligence (AI), and Blockchain — in multiple dimensions blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus towards any reference models or best practices. This paper provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this paper by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition. With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties including vacuity and dissonance, for robust long-term hand grasp recognition. To avoid heuristically determining the optimal rejection threshold, we examine the performance of misclassification detection in the validation set. Extensive comparisons of accuracy under the non-rejection and rejection scheme are conducted when classifying 8 hand grasps (including rest) over 8 subjects across proposed models. The proposed ECNN is shown to improve the recognition performance, achieving an accuracy of 52.44% without the rejection option and 83% under the rejection scheme with multidimensional uncertainties, which significantly improves the current state-of-the-art (SoA) by 5.72% and 13.19%, respectively. Furthermore, its overall rejection-capable recognition accuracy remains stable with only a small accuracy degradation after the last data acquisition over 3 days. These results show the potential design of a reliable classifier that yields accurate and robust recognition performance.

Freezing of gait (FoG) is a widely observed movement disorder in Parkinson’s Disease patients (PD). Its prediction is crucial for effectively giving the cue to avoid FoG occurrence. However, present methods of prediction of FoG are inaccurate for large but practical prediction horizons (PH)s. Therefore, this work presents a comprehensive analysis of the electroencephalography (EEG) and inertial measurement units (IMU)s to predict FoG advance in time. An ensemble model consisting of two neural networks, EEGFoGNet and IMUFoGNet, was developed and tested at different PHs and ensemble weights. Moreover, the model is tested for two practical scenarios: clinical or research applications and personal uses. For clinical or research applications, stratified 5-Folds cross-validation was used. For personal uses, a transfer learning technique was used for learning user-specific FoG-related features. The model obtained the best accuracy of 92.1% at 1 second’s PH and the least accuracy of 86.2% at 5 seconds’ PH. The presented results are encouraging and show the proposed model’s clinical applicability. This study will also help practitioners in comparing the efficacy of different cueing methods.

In this paper, the diversity order (DO) and the diversity measure (DM) of multiple-input multiple-output (MIMO) antennas in generic single-user, multi-user and massive MIMO wireless communications are calculated for several practical case studies. These performance metrics are calculated from the antenna correlation coefficient (ACC) matrix. The DO is calculated from the rank of the ACC matrix, and the DM is obtained from the trace and Frobenius norm of the ACC matrix. Numerical results are provided. In particular, it is shown that though the DM is always less than the DO, it incorporates the effect of antenna correlation on the diversity gain in a better way.  (This paper has been submitted to  IEEE Antennas and Wireless Propagation Letters )

A better option for the solution of the corresponding system of equations is suggested.

Arrhythmias are conditions characterized by a faster, slower, or irregular heart rhythm. Some of them may be harmless and brief, but others can lead to a sudden cardiac arrest. Thus, procedures that safely restore the normal heartbeat are a matter of interest. Experimental evidence shows that some arrhythmia may have an origin in chaotic dynamics; therefore, this paper describes a multivariable feedback control method to regulate heart rhythm-like dynamics in a master-slave framework. Since there is no consensus on the equations describing the cardiac dynamics, we use a phenomenological model that generates a variety of electrocardiogram-like signals and represent it in a Takagi-Sugeno fuzzy form. We provide control design conditions to achieve the tracking control for a nominal and an uncertain slave system. The results suggest that our closed-loop control scheme can get the heart rhythm dynamics from a non-desirable situation back to a normal behavior given by the reference system. Our approach reveals a proof of concept to suppress pathological behaviors in the heart dynamics that could lead to implementation in cardiac devices.

People with transtibial limb loss suffer from reduced mobility. Intelligent ankle-foot prostheses have the potential to improve quality of life in people with limb loss, but there are scientific, clinical, and commercial barriers that prevent widespread impact. Further research tools and experiments are needed to expand our understanding of how to design and control intelligent prosthetic limbs. We designed and built a robotic ankle-foot prosthesis with off-board actuation and control to serve as a platform for biomechanical lower limb loss research. Our prosthesis fits inside of a shoe during walking, and attaches to standard clinical prosthesis componentry, including carbon fiber prosthetic footplates and pyramid adapters. Our novel mechanical architecture implements a custom torsion spring in parallel with the ankle joint to allow for dorsiflexion and plantarflexion torque control with a single off-board actuator. Benchtop tests show that our prosthesis has peak plantarflexion torques greater than 175 Nm and a torque control bandwidth of 6.1 Hz. Walking experiments with two participants with lower limb loss indicate that the prosthesis can achieve low torque tracking errors and push-off power greater than the biological ankle during walking. This device will enable future experiments on amputee gait biomechanics, human-robot interaction, and prosthesis control.

Conceptual design of a micro-nuclear reactor for Canadian Arctic applications.

Terahertz communications has been foreseen as a key enabler to the sixth generation  (6G) of wireless communications systems. However, the design of spectrally-efficient waveforms and modulation schemes is an ongoing challenge in this regime. In this paper, we propose and experimentally demonstrate the transmission of M-ary carrierless amplitude and phase (CAP) modulated signals using a 253 GHz photo-mixing-based terahertz wireless communications system combined with the optical transmission over a 10-km standard single mode fiber (SSMF). Experimental results show that the CAP modulation technique has a capability to support the high-speed transmission of terahertz signals over a wide range of data rates from 4 Gbit/s to 96 Gbit/s based solely on optical intensity modulation at the transmitter side and terahertz envelope detection at the receiver side. Consequently, M-ary CAP can potentially be adopted as a low-complexity waveform and modulation contender to simplify the transceivers architectures for the sixth generation (6G) terahertz communications systems, without sacrificing the high throughput targeted by these systems.

The introduction of direction finding in the Bluetooth standard enabled the use of antenna arrays for locating Bluetooth devices, using carrier phase measurements to estimate the direction from the array to a moving device. In this work, this feature is utilized for outdoor localization. We show how using repeated measurements from all array elements, instead of only the initial single-element reference samples as often suggested, can contribute to an improved estimate of the signal’s unknown carrier frequency offset, thereby improving the direction estimation performance. To run the direction-of-arrival estimation in real-time with high angular resolution on an embedded computer we propose a pseudo-spectrum peak search strategy that combines a coarse search, where the resolution is decided based on the array’s main lobe width, with a local nonlinear optimization for estimate refinement. We consider practical aspects relating to the phase sampling configuration and demonstrate direction estimation at up to 700m range with insignificant packet loss within 500m, and without significant loss of angular precision even when the received signal is near the receiver’s signal strength sensitivity threshold. In an open outdoor environment, using a square antenna array with 12 elements, the azimuthal performance is found to be very consistent with range, with noise standard deviation typically around 1°. While the elevation is significantly affected by multipath at lower elevation angles, with visible disagreement between frequency channels, it is shown to be consistent with simulations of ground reflection multipath.

The majority of inverter-based resources (IBRs) currently operate as grid-following inverters (GFLIs). These inverters exhibit certain stability issues when integrated in low-strength areas of the grid. To enhance the grid strength in a GFLI-dominant area, virtual synchronous generators (VSGs) are installed. If the VSG and the GFLI are investigated as a paralleled system, despite the benefits brought by the VSG, in some cases, the transient angle instability process, caused by a voltage sag, is accelerated. Therefore, this paper studies the transient angle stability of a paralleled VSG-GFLI system via investigating the voltage at a common bus between the two IBRs. Subsequently, a stable region of the voltage angles of the two IBRs is determined. Based on this region, the stability margin of the paralleled system can be determined and used to quantitatively evaluate the system stability. Moreover, a control loop is proposed to improve the transient stability of the paralleled system. The proposed controller can adaptively reduce the VSG power set-point to avoid a complete failure of the VSG even when no stable equilibrium point exists during a voltage sag. The stability investigation and performance evaluation of the proposed method are conducted in PSCAD/EMTDC and experimentally validated.

An uplink internet-of-things (IoT) scenario is considered, wherein, multiple IoT devices (IoTDs) report their timely status updates to a central node (CN). We analytically compare the performance of orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) scheduling schemes for the expected system age of information and throughput.