Search

The standard CISPR 16-1-1 defines the measuring receiver using a black-box approach and sets requirements for its accuracy and spectral properties. Traditionally, such test receivers were developed using a superheterodyne architecture. Recently, time-domain electromagnetic emission measurement systems have been built employing direct sampling instruments, mainly oscilloscopes, and relying on specific signal processing to emulate the performance of compliant instruments. In these cases, the short-time Fourier transform is used for spectral estimation, but the corresponding electromagnetic compatibility standards lack details for its correct use with respect to parameters such as windowing function, overlapping factor, and frequency interpolation. Moreover, it is unclear which combination of spectral estimation parameters is best fit for this purpose. Obtaining reliable, consistent and low uncertainty spectral estimates of electromagnetic emissions measured in time-domain needs appropriate configuration and tuning of the signal processing algorithms. This paper investigates the error in the calculated spectrum for various reference signals: multitone, chirp pulses and rectangular pulses. The analysis is carried out for each CISPR band from A to D, that is, between 9 kHz and 1 GHz. After $489.6\times 10^{3}$ iterations, distributed in 1700 different digital implementations of the CISPR 16-1-1 measuring receiver, the simulations outcomes point to certain sets of parameters that showed satisfactory performance overall, being the Nutall, Kaiser, and Parzen windows with more than 75% of overlapping and using interpolation factor higher than 5, generally suitable. Calibration results are used to experimentally verify that a valid set of parameters is adequate to fulfil CISPR 16-1-1 requirements.

Performing in-situ radiated emissions measurements, that is, in locations different from a standard test site, can be a challenging task because of the high electromagnetic noise levels in the ambient. A harsh electromagnetic environment characterizes such sites, and it usually results in difficulties when discerning between emissions from the equipment under test (EUT) and electromagnetic fields generated by surrounding devices. Moreover, communication signals from broadcasting services are generally significantly higher than the standard emission limits, making it even harder to determine compliance. In this article, we present different techniques leveraging the advantages of time-domain measurement systems to provide effective and practical solutions to mitigate ambient noise’s effect on radiated electromagnetic interference measurements. First, the test method used is described, and pragmatic considerations are given to ensure reliable and repeatable measurements. Multichannel time-domain measurement systems are introduced as the fundamental tool for the proposed strategies. Subsequently, different study cases are evaluated with real test examples, highlighting several criteria intended to reduce the impact of ambient noise on the actual emissions measures produced by the EUT. Finally, a real application of those strategies for measuring a photovoltaic system is described. Overall, the methods employed and the main advantages of using full-time-domain FFT-based receivers are reviewed. In addition, the possibility of incorporating this article’s outcomes into forthcoming electromagnetic standards about in-situ radiated emission measurements is also debated.

Objective: The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel. Methods: First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters’ tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters. Main results: The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection. Significance: Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios.

In this paper, we present a basic theory of the duality of linear codes over three of the non-unital rings of order four; namely $I$ , $E$ , and $H$ as denoted in (Fine, 1993). A new notion of duality is introduced in the case of the non-commutative ring $E$ . The notion of self-dual codes with respect to this duality coincides with that of quasi self-dual codes over $E$ as introduced in (Alahmadi et al, 2022). We characterize self-dual codes and LCD codes over the three rings, and investigate the properties of their corresponding additive codes over $\mathop {\mathrm {\mathbb {F}}} _{4}$ . We study the connection between the dual of any linear code over these rings and the dual of its associated binary codes. A MacWilliams formula is established for linear codes over $E$ .

LoRa is a popular communication technology in the Internet of Things (IoT) domain, providing low-power and long-range communications. Most LoRa IoT applications use the LoRaWAN architecture, which builds a star topology between LoRa end nodes and the gateway they connect to. However, LoRa can also be used for the communication between end nodes themselves, forming a mesh network topology. In this paper, we present a library that allows to integrate LoRa end nodes into a LoRa mesh network, in which a routing protocol is used. Thus, an IoT application running on these nodes can use the library to send and receive data packets to and from other nodes in the LoRa mesh network. The designed routing protocol is proactive, and maintains the routing table at each node updated by sending routing messages between neighboring nodes. The implemented library has been tested on embedded boards featuring an ESP32 microcontroller and a LoRa single-channel radio. By using our LoRa mesh library, nodes do not need to connect to a LoRaWAN gateway, but among themselves. This opens the possibility for new, distributed applications solely built upon tiny IoT nodes.

Electroencephalography (EEG) is a non-invasive technology used for the human brain-computer interface. One of its important applications is the evaluation of the mental state of an individual, such as workload estimation. In previous works, common spatial pattern feature extraction methods have been proposed for the EEG-based workload detection. Recently, several novel methods were introduced to detect EEG pattern workloads. However, it is still unknown which one of these methods is the one that offers the best performance for the workload EEG pattern feature detection. In this article, four methods were used to extract workload EEG features: (a) common spatial pattern feature extraction; (b) temporally constrained sparse group spatial pattern feature extraction; (c) EEGnet; and (d) the new proposed shallow convolutional neural network for workload estimation (WLnet). The classification accuracy of these four methods was compared. Experimental results demonstrate that the proposed WLnet achieved the best detection accuracy in both stress and non-stress conditions. We believe that the proposed methods may be relevant to real-life applications of mental workload estimation.

One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance. The universal solution of a fixed bitrate ladder is inadequate in ensuring a high quality of user experience without re-buffering or introducing annoying compression artifacts. However, a content-tailored solution, based on extensively encoding across all resolutions and over a wide quality range is highly expensive in terms of computational, financial, and energy costs. Inspired by this, we propose an approach that exploits machine learning to predict a content-optimized bitrate ladder for on-demand video services. The method extracts spatio-temporal features from the uncompressed content, trains machine-learning models to predict the Pareto front parameters and, based on that, builds the ladder within a defined bitrate range. The method has the benefit of significantly reducing the number of encodes required per sequence. The presented results, based on 100 HEVC-encoded sequences, demonstrate a reduction in the number of encodes required when compared to an exhaustive search and an interpolation-based method, by 89.06% and 61.46%, respectively, at the cost of an average Bjøntegaard Delta Rate difference of 1.78% compared to the exhaustive approach. Finally, a hybrid method is introduced that selects either the proposed or the interpolation-based method depending on the sequence features. This results in an overall 83.83% reduction of required encodings at the cost of an average Bjøntegaard Delta Rate difference of 1.26%.

Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classify various types of data successfully. However, the deep learning approach requires massive data to train its neural networks, and the amount of data impacts greatly on the quality of the classifiers. This paper proposes a novel approach that combines deep learning and data augmentation for EEG classification. We applied the empirical mode decomposition on the EEG frames and mixed their intrinsic mode functions to create new artificial EEG frames, followed by transforming all EEG data into tensors as inputs of the neural network by complex Morlet wavelets. We proposed two neural networks-convolutional neural network and wavelet neural network-to train the weights and classify two classes of motor imagery signals. The wavelet neural network is a new type of neural network using wavelets to replace the convolutional layers. The experimental results show that the artificial EEG frames substantially improve the training of neural networks, and both two networks yield relatively higher classification accuracies compared to prevailing approaches. Meanwhile, we also verified the performance of our new proposed wavelet neural network model in the classification of steady-state visual evoked potentials.

With the number of nonhuman devices expected to significantly overtake human users of long-term evolution networks, it is no surprise that First Responders in mission critical (MC) operations will need to interact with an increasing number of unmanned devices, “bots”or drones. In this paper, we propose the MC “bot”concept as an entity capable of gathering environmental/situational information and triggering certain automated actions without the need of human intervention. We prove that in certain circumstances these bots can help quickly resolve emergency situations and complement traditional centralized coordination from dispatch control rooms. We explain how such “bots”relate to and expand the 3GPP MC Communications architecture framework, considering different architectural approaches and complexity levels. Importantly, because First Responders must remain focused, hands free, and context aware most of the time, we cover specifically the case where man-machine interaction is based on voice communication without having to use hands or look at a screen. It is hence of particular interest to convert “bot”interactions into audio information exchanged over push-to-talk communication services, be it through the cellular network or leveraging the 3GPP device-to-device capability. This paper is complemented with theoretical use cases as well as description and multimedia material of a prototype implementation of a concept emulator.

Metamaterials are artificial structures that have recently enabled the realization of novel electromagnetic components with engineered and even unnatural functionalities. Existing metamaterials are specifically designed for a single application working under preset conditions (e.g., electromagnetic cloaking for a fixed angle of incidence) and cannot be reused. Software-defined metamaterials (SDMs) are a much sought-after paradigm shift, exhibiting electromagnetic properties that can be reconfigured at runtime using a set of software primitives. To enable this new technology, SDMs require the integration of a network of controllers within the structure of the metamaterial, where each controller interacts locally and communicates globally to obtain the programmed behavior. The design approach for such controllers and the interconnection network, however, remains unclear due to the unique combination of constraints and requirements of the scenario. To bridge this gap, this paper aims to provide a context analysis from the computation and communication perspectives. Then, analogies are drawn between the SDM scenario and other applications both at the micro and nano scales, identifying possible candidates for the implementation of the controllers and the intra-SDM network. Finally, the main challenges of SDMs related to computing and communications are outlined.