The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.

Tracking beats of singing voices without the presence of musical accompaniment can find many applications in music production, automatic song arrangement, and social media interaction. Its main challenge is the lack of strong rhythmic and harmonic patterns that are important for music rhythmic analysis in general. Even for human listeners, this can be a challenging task. As a result, existing music beat tracking systems fail to deliver satisfactory performance on singing voices. In this paper, we propose singing beat tracking as a novel task, and propose the first approach to solving this task. Our approach leverages semantic information of singing voices by employing pre-trained self-supervised WavLM and DistilHuBERT speech representations as the front-end and uses a self-attention encoder layer to predict beats. To train and test the system, we obtain separated singing voices and their beat annotations using source separation and beat tracking on complete songs, followed by manual corrections. Experiments on the 741 separated vocal tracks of the GTZAN dataset show that the proposed system outperforms several state-of-the-art music beat tracking methods by a large margin in terms of beat tracking accuracy. Ablation studies also confirm the advantages of pre-trained self-supervised speech representations over generic spectral features.

Objective: Auditory and visual cues have been shown to be efficacious in laboratory-based freezing of gait (FoG) mitigation in Parkinson’s disease (PD). However, the real-life applications of these cues are restricted due to inconvenience to the users and lower social acceptance. Recently developed closed loop vibrotactile cues based on temporal gait events have overcome the shortcomings of auditory and visual cueing. However, spatial gait parameter driven vibrotactile cueing has not been explored yet. Previous studies have suggested spatial gait parameter driven cueing to be more effective than temporal cueing based on FoG pathophysiology. Therefore, we developed and validated a novel cueing scheme in which foot to ground angle at heel strike (FGA_HS) is estimated using indigenous instrumented shoes to drive vibrotactile cueing. Methods: Eight PD freezers underwent a 6-meter timed walk test including reverse counting and hurdles to induce FoG. PD freezers underwent the protocol in the off-medication state with and without cue, and after medication without cue. Indigenous instrumented shoes were used for objective gait assessment. Results: Freezing severity, quantified by the ratio of time spent freezing to the total walking time, reduced with the cue in the off-medication state. The reduced freezing severity was accompanied by increased antero-posterior stance center of pressure excursion (CoP_AP) and FGA_HS.  Conclusion: The novel spatial gait parameter-based cue was effective in mitigating FoG through improvement in associated gait parameters. Significance: With a future comprehensive validation on a larger number of participants, the novel cueing method has significant potential for practical use and commercialization.

Electroencephalography (EEG) signals are used for the diagnosis of neurological disorders and detection of seizures. Early detection of seizures not only can save the patient’s life but also can improve the quality of life. The traditional methods for seizure detection are time-consuming. To combat these problems and further improve the accuracy of seizure detection, the techniques of deep machine learning have been applied to EEG signals. This work develops an EEG-based seizure detection program using deep machine learning to detect seizures using EEG signals. The model is developed using a data set of EEG recordings from 79 human subjects. To this end, a literature review has been conducted to review the latest research related to deep learning algorithms applied to EEG signals for seizure detection. The EEG data has been pre-processed, filtered, and segmented before being fed to the machine learning model. Three models were developed, two models are based on feed-forward neural networks and the third model is based on long-short term memory (LSTM) network. For the feed-forward neural network with 10 hidden neurons, the obtained accuracy was too low (66.1%). For the feed-forward neural network with 100 hidden neurons, the accuracy was slightly improved to 74.3%. The LSTM model showed an accuracy of 87.7%. The model correctly classified 96.4% of normal patients as having no seizures and correctly classified 71.6% of seizure patients as having seizures. The limitations on increasing the accuracy of the models are discussed and possible solutions are suggested. If channel-specific seizure annotations are provided by medical experts, the accuracy of the model is expected to increase significantly.

A unified theory of fields (UTF) or radiations is explored in this article. The UTF unveils that an arbitrary static field (either scalar or vectorial, called an ontological field) in an inertial system (static system) will merge as two vector fields in an inertial system moving relative to the static system, which satisfy Maxwell-like equations. Therefore, the Maxwell equations are not only valid for describing the EM fields but also for any physical fields for which an ontological field exists. The UTF is based on the theory of special relativity and the Helmholtz theorem. In order to find how the vector field changes in different inertial systems, one only needs to examine how the curl and divergence of the vector field transform. As applications, the Maxwell equations for EM field and the Maxwell-like equations for the gravitational field are derived from the UTF, and the latter are also derived from the Einstein field equations in the theory of general relativity. Some universal laws of nature are shown to be derivable from the UTF.

Broadcast of localized TV content enables tailored content delivery catering to the requirements of regional user base. 5G multicast-broadcast service (MBS) requires a spectrally efficient broadcast solution that enables the change of content from one local service area (LSA) to another. A frequency reuse factor of unity between two adjacent LSAs causes their boundary region to become saturated with co-channel interference (CCI). Increasing the reuse factor will reduce the CCI at the cost of degrading the spectral efficiency. This letter addresses the frequency and transmit power planning which manages the CCI at the LSA boundary to achieve a satisfactory trade-off between spectral efficiency and broadcast coverage.

We report an avalanche-mode light-emitting transistor (AMLET) in silicon (Si), based on a lateral bipolar junction, which emits light near 760 nm optical wavelength with a record low bandwidth of 38 nm. The AMLET, designed in a CMOS-compatible silicon-on-insulator (SOI) photonics platform, is optically confined within a 0.21 μm thick SOI layer, which forms a Fabry-Perot (FP) resonator perpendicular to the Si surface. Light is emitted from the reverse biased emitter-base junction via phonon-assisted hot carrier recombination and, additionally, minority carriers are injected via the forward-biased Base-Collector junction. The combination of injection from collector terminal through a narrow base and FP optical resonance, yields a high optical power efficiency of 4.3×10−6 at VBC = 0.8 V and VEB = 10 V. Our work opens new possibilities in spectralengineering of Si light-emitters, which could boost performance of all-Si optical interconnects and sensors.

This work presents a novel perfect reconstruction filter bank decomposition (PRFBD) for nonlinear and nonstationary time series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach wholly based on the Fourier representation, is shown to be a special case of the proposed PRFBD. The adaptive Fourier–Gauss decomposition (FGD) proposed in this work is a variation of the FDM, which is based on the FR and Gaussian filtering. Similarly, we also consider Butterworth filtering to develop adaptive Fourier–Butterworth decomposition (FBD). The proposed theory can decompose any signal (time series, image, or other data) into a set of the desired number of Fourier intrinsic band functions (FIBFs), which follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation is also provided, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters. We have performed an extensive analysis of both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches.

In this research, we collected 70,000 COVID–19 vaccine-related geotagged Twitter posts from nine African countries. The duration is from December 2020 to February 2022. We used VADER to classify the tweets into three sentiment classes (positive, negative, and neutral). The outputs were validated using machine learning classification algorithms, including, Naive Bayes, Logistic Regression, Support Vector Machines, Decision Tree, and K-Nearest Neighbour. We identified hotspots by clustering the sentiment of these tweets using the point-based location technique. These hotspots were visualised on the map using ArcGIS Online. On the map, we used green to represent positive sentiment dominance, red to represent negative sentiment dominance, and grey to represent neutral sentiment dominance. The outcome of this research shows that social media data can be used to complement existing data in identifying hotspots during future outbreaks, especially in the areas where there is little or no available data. It can also be used to inform health policy in managing vaccine hesitancy.

In this paper, the impact of the non-linearity of the propagation medium on the extended $\eta$-$\mu$ fading distribution is studied. In particular, the extended $\alpha$-$\eta$-$\mu$ fading model in which the parameter $\alpha$ represents the non-linearity of the propagation environment is presented via providing the exact and asymptotic of the statistical properties, namely, the probability density function (PDF), cumulative distribution function (CDF), and generalized-moment generating function (G-MGF).

An elliptically shaped electromagnetic vibration energy harvester is presented for two- and one-dimensional motions with the easy tuning of the resonance frequency. The harvester consists of a free-to-move ring-shaped permanent magnet with radial magnetization, a set of cube magnets distributed elliptically in a fixed holder, and two coil windings located above and below the harvester. When the device is exposed to vibrations, the free-to-move ring magnet moves until the magnetic restoring force from the fixed cube magnets pushes it back while inducing an electromagnetic force on the fixed coils. The performance of the device is characterized using an XY-shaker, whose frequency is swept from 1 Hz to 10 Hz with motion amplitudes of 2 mm and 4 mm on both axes simultaneously. Unlike 1-D harvesters, the resulting output power shows two resonant frequencies at 4.5 Hz and 7 Hz, at which the harvester can generate around 1.5 mW. The device also displays a nonlinear hardening resonator behaviour. Finally, 1D vibration experimental results show that the output power of the prototype depends on its angular position with respect to the motion direction of the vibration source, reaching a maximum at an angular position of 45 degree.

According to different partitioning styles, there exist different impedance-based stability analysis methods for complex systems such as voltage source converter (VSC)-based high-voltage direct current (HVDC) systems. Thus, the determination of the partitioning method is of importance in impedance-based stability analysis. However, how to partition a complex system is not paid much attention in the past. In this paper, the partitioning problems are revisited and a set of practical partitioning principles are proposed. Based on the principles, the VSC-HVDC system is partitioned into five parts, satisfying simplicity and integrity. In addition, the methods to form the equivalent circuits of the sub-systems are given and the equivalent circuits of the sub-systems are built, which is helpful to obtain the input-output models of the sub-systems. Furthermore, a stability criterion by directly applying argument principle is used instead of the generalized Nyquist criterion (GNC), which could avoid the calculation of eigenvalue transfer functions, especially when a large number of sub-systems are involved. Finally, the correctness of the impedance-based stability analysis method is verified by control hardware-in-loop (CHIL) experimental results.

Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited from the proprietary interpretation of the dataset. Addressing comparability and reproducibility issues, we introduce Payload-Byte, an open-source tool for extracting and labeling network packets in this work. Payload-Byte utilizes metadata information and labels raw traffic captures of modern intrusion detection datasets in a generalized manner. Moreover, we transformed the labeled data into a byte-wise feature vector that can be utilized for training machine learning models. The whole cycle of processing and labeling is explicitly stated in this work. Furthermore, source code and processed data are made publicly available so that it may act as a standardized baseline for future research work. Lastly, we present a brief comparative analysis of machine learning models trained on packet-based and flow-based data. UNSW-NB15 and CIC-IDS2017.

It is not always reasonable to set a fixed weight value to an attribute (or a variable) because this cannot accurately maintain the similarity between users and leads to decrease the performance of collaborative filtering recommendation algorithms. In this paper, a piecewise weighting method is proposed with the hyper-class representation for improving the collaborative filtering recommendation. Specifically, a kernel function is first applied to map the original data into the kernel space, and to learn the weight of each attribute. And then, the hyper-class representation of the data is constructed to learn the weight of the segmented attribute value (hyper-class) for each attribute, so as to construct a piecewise weighting function. In addition, the piecewise weighting function is applied to calculate the similarity between users for a collaborative filtering recommendation. Finally, experiments are conducted to examine the collaborative filtering recommendation algorithm, and show that the proposed algorithm with piecewise weighting functions is more efficient than the compared algorithm with fixed weight values, in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Precision.

Digital signal processing has become central to many fields, from coherent optical telecommunications where it is used to compensate signal impairments, to video image processing. Image processing in particular is important for observational astronomy, medical diagnosis, autonomous driving, big data and particularly artificial intelligence. Digital signal processing is mainly performed electronically, but new applications, particularly those involving real time video image processing, are creating unprecedented demand for ultrahigh performance, including bandwidth and reduced energy consumption. Here, we demonstrate a photonic digital signal processor operating at 18 Terabits/s and use it to process multiple simultaneous video signals in real-time. The system processes 400,000 video signals concurrently, performing 34 functions simultaneously that are key to object edge detection, edge enhancement and motion blur. As compared with spatial-light devices used for image processing, our system is not only ultra-high speed but highly reconfigurable and programable, able to perform many different functions without any change to the physical hardware. Our approach, based on an integrated Kerr soliton crystal microcomb, opens up new avenues for ultrafast robotic vision and machine learning.

The time-reversal method has been applied to the source location due to its spatiotemporal focusing properties. Much work on the topic is to locate the impulse or wideband sources. However, most sources in practical situations are narrowband. Therefore, it is desirable to develop a single-frequency time-reversal method to reconstruct narrowband-source locations. Unlike the conventional time-reversal method, the single-frequency time-reversal method we propose in this paper extracts the field signals at a single frequency at the time-reversal-mirror locations and reinjects them into the solution domain for the backward simulations. The preliminary experimental results with an ergodic cavity demonstrate the effectiveness of our proposed method and move one crucial step forward for the practical uses of the time-reversal method. They lay the foundations for further extensions of time-reversal theory and applications.

The used dataset is available publically at the UCI repository for the experiments. In this work Dynamic mixture of gases, the dataset is used for the classification, where this time series dataset is converted to 2D GAF representation and then classify by using the ALexNet classifier. Further, the 1D time series dataset is classified by using deep learning architecture named GasNEt0CNN.

Reference Architectures (RAs) play an important role in the integration of 6G terrestrial and satellite networks. In this paper, we present the essence of the reference architectural roadmap as per the Edition-3 document of the Satellite Working Group of IEEE Future Networks Initiative (FNI).