The enhancement reached in the telecommunication field, especially in cellular communications, has led to the emergence of multiple multimedia services. The easy supported by the mobile networks together with their novelty has triggered their popularization among society, being considered already essential in the daily life. Likewise, the increasingly connectivity dependence of these services leads to have a huge impact of the network on the user’s perception, being their adequate provision one of the biggest concerns among telcos. Moreover, data encryption used for the security and integration of network information exchanged, together with the limited access to the application data, difficulties the assessment of the service’s end-to-end (E2E) performance. In this scope, this work presents a dataset based on multiple metrics namely KQIs, which provide the E2E conditions of different services. Particularly, the dataset considers video streaming and cloud gaming (CG) services. Furthermore, a detailed description of the testbed where the dataset has been generated is given, discussing the reasoning behind the choices made for its development.

Thermal feedback has been proven to enhance user experience in human-machine interactions. Yet state-of the-art thermal technology has focused on the single finger or palm in static contact, overlooking dynamic and multi-finger interactions. The underlying challenges include incompatible designs of conventional interfaces for providing salient thermal stimuli for such interactions and, thereby, a lack of knowledge on human thermal perception for relevant conditions. Here we present the ThermoSurf, a new thermal display technology that can deliver temperature patterns on a large interface suitable for dynamic and multi-finger interactions. We also investigate how user exploration affects the perception of the generated temperature distributions. Twenty-three human participants interacted with the device following three exploration conditions: static-single finger, dynamic-single finger, and static-multi finger. In these experiments, the individuals evaluated 15 temperature differences ranging from -7.5 °C to +1.5 °C with an initial temperature of 38 °C. Our results showed that human sensitivity against thermal stimuli is significantly greater for static-single finger contact compared to the other tested conditions. In addition, this interaction type resulted in higher thermal discrimination thresholds than the ones reported in the literature. Our findings offer new perspectives on providing salient and consistent thermal feedback for future tactile interfaces.

We present mean communication rate loss (MCRL) values due to the beam cusping phenomenon inherent to grid of beams based wireless systems, which are widely used/proposed in millimeter-wave and sub-THz bands. We consider array antenna elements with non-zero aperture and axisymmetric radiation pattern with a certain directivity, unlike the ideal isotropic antenna elements widely considered in theoretical papers on array processing and RF beamforming. The array antenna elements have a half power beam width of ninety degrees in this work which is typical of commonly used microstrip patch antenna elements. We perform Monte Carlo numerical experiments to obtain MCRL values for different spatial dimensions of multiple input multiple output (MIMO) systems, different angular fields-of view, and different beamforming codebook sizes. We show that increasing the beam grid density beyond a certain threshold does not help and therefore a certain cusping loss is unavoidable even for continuous beam steering with directive antenna elements. Further, we also show the quantitative impact on the MCRL values as the axisymmetric radiation pattern directivity of the modelled antenna element is increased.

We present a novel, power and hardware efficient, antenna system leveraging the eigenmodes of the over-the-air propagation matrix from an active multi-antenna feeder (AMAF) to a large reflective intelligent surface (RIS), both configured as standard linear arrays and placed in the near field of each other. We demonstrate the flexibility of the proposed architecture by showing that it is capable of generating radiation patterns for multiple applications such as very narrow beams with low side lobes for space-division multiple access communications, wide-angle beams for short range automotive sensing and sectorial beaconing, and monopulse-like patterns for radar angular tracking. A key parameter in our design is the AMAF-RIS distance which must be optimized and it is generally much less than the Rayleigh distance. For a given AMAF aperture, the optimal AMAF-RIS distance increases as a function of the RIS size. The AMAF-RIS loss is compensated almost exactly by the larger aperture gain of the RIS leading to almost constant RIS gain with increasing RIS sizes. This allows to choose different beam angular selectivities with the same center beam gain. Active RF amplification is done at the AMAF only, thus resulting in a much higher power efficiency and much lower hardware complexity than conventional phased arrays with same beamforming performance. Accepted for the IEEE ICC 2023, Rome

Hardware obfuscation is a design transformation technique that transforms a design to protect its confidentiality against untrusted parties. In particular, it aims at protecting proprietary hardware intellectual property (IP) blocks against reverse engineering (RE), piracy, and extraction of design secrets. A wide array of existing works on hardware obfuscation have demonstrated its security promises and theoretical robustness against diverse attacks. However, these techniques lack in (1) scalability to large commercial-scale designs, (2) ease of integration with existing electronic design automation (EDA) tool flow, (3) ability to protect against emergent attack modes, such as structural analysis-based attacks, and (4) ability to efficiently trade-off security with design overhead. The latter requires an effective metric to quantify robustness against RE attacks. In this paper, we introduce a practical state space obfuscation algorithm and associated automation tool, ProtectIP, that address the above shortcomings. The algorithmic steps have polynomial complexity and are scalable for large designs. We have developed a complete EDA tool flow that integrates ProtectIP. We show exponential resistance can be achieved against all known RE attacks while incurring modest area overhead (24% on average) with negligible impact (maximum 5% overhead) on critical-path delay. We quantify the level of achieved security using metrics and show that an intelligent attacker with partial knowledge of the obfuscation process has a low probability (maximum 0:33) of succeeding in RE attacks.

A document by Onel Alcaraz López . Click on the document to view its contents.

Building end-to-end speech synthesisers for Indian languages is challenging, given the lack of adequate clean training data and multiple grapheme representations across languages. This work explores the importance of training multilingual and multi-speaker text-to-speech (TTS) systems based on language families. Such a study is crucial not only from a low resource perspective but also to reduce the overall consumption of computing power. TTS systems are trained separately for Indo-Aryan and Dravidian language families, and their performance is compared to that of a combined Indo-Aryan+Dravidian voice. We also investigate the amount of training data required for a language in a multilingual setting. We want to see if we can do more with less data without compromising the quality and intelligibility of the synthesised speech. The voices are easily extendable to new languages with limited data. Same-family and cross-family synthesis and adaptation to new languages are analysed. The analyses show that language family-wise training of Indic systems is the way forward for the Indian subcontinent, where a large number of languages are spoken.

The data used our analysis is from an ID fan which is part of a coal powered plant that produces a target output of 117W. The fan is placed at the outlet of the boiler. The fan runs at an air flow rate of 5365 Am3/min , with a density of 0.85 kg/m3, diameter of 2440 mm , with a total pressure of 505 mmAq and a speed of 989 rpm. The fan impeller is driven by a 650 kW electric induction motor (6600 V 50 Hz 3 Phase) with a speed of 1000 rpm.

This paper proposes a FAst paRAllel and pipeliNE Q-learning accelerator (FARANE-Q) for a configurable Reinforcement Learning (RL) algorithm implemented in a System on Chip (SoC). The proposed work offers flexibility, configurability, and scalability while maintaining computation speed and accuracy to overcome the challenges of a dynamic environment and increasing complexity. The proposed method includes a Hardware/Software (HW/SW) design methodology for the SoC architecture to achieve flexibility. We also propose joint optimizations on the algorithm, architecture, and implementation to obtain optimum (high efficiency) performance, specifically in energy and area efficiency. Furthermore, we implemented the proposed design in a real-time Zynq Ultra96-V2 FPGA platform to evaluate the functionality with an actual use case of smart navigation. Experimental results confirm that the proposed accelerator FARANE-Q outperforms state-of-the-art works by achieving a throughput of up to 148.55 MSps. It corresponds to the energy efficiency of 1747.64 MSps/W per agent for 32-bit and 2424.33 MSps/W per agent for 16-bit FARANE-Q. Moreover, the proposed 16-bit FARANE-Q outperforms other related works by an improvement of at least 1.23× in energy efficiency. The designed system also maintains an error accuracy of less than 0.4% with optimized bit precision for more than eight fraction bits. The proposed FARANE-Q also offers a speed up of processing time up to 1795× compared to embedded SW computation executed on ARM Zynq processor and 280× of computation of full software executed on i7 processor. Hence, the proposed work has the potential to be used for smart navigation, robotic control, and predictive maintenance.

Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-totarget paths. We tested our trajctory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers (p < 0.001). The trajectory optimization method should be practically implemented to improve the FES-driven reaching performance.

This paper presents a tutorial on the Tracking, Telemetry, and Command (TT&C) for spacecraft and satellite missions. In particular, it provides a thorough summary of the design of the TT&C, starting from elementary system aspects and going down to the details of the on-board TT&C subsystem design, its units, and the physical layer. The paper is then complemented with a description of emerging TT&C techniques and technologies, the standardization framework, and with practical examples of actual spacecraft design of European space missions. The tutorial here presented is thought for professionals (also in other telecommunication engineering fields) that want to face the challenges and state-of-the-art of the TT&C, and know more about this fundamental function that allows us to daily control and monitor our spacecraft.

Simulation for the fault current limiting operation of REBCO CORC® superconducting cables with different core materials

A new class of vulnerabilities, called trojan source, has been recently discovered by Boucher and Anderson. They allow attackers to inject specific Unicode control characters in the source code of a software project, such that what text editors visualize differs from what compilers interpret. In the absence of specific protections, trojan source vulnerabilities can be exploited to launch effective supply chain attacks that appear invisible to developers. This work describes the state of the art of known trojan source attacks, illustrates two new attack variants involving configuration files and Java code, and describes practical preventive measures. The first variant affects Java platforms and may enable the execution of instructions injected within comments. Whereas the second variant exploits BiDi and homoglyph Unicode symbols within source code and configuration files. We also give several proofs of concept for many popular development platforms and provide a detection tool that can be easily integrated into the build process.

An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.

Owing to increasing connected medical devices and rapid development of deep learning technologies, the Internet of Medical Things (IoMT) has attracted great attention for healthcare monitoring with intelligent sensors. Radar serves as a non-contact healthcare device, continuously measuring human’s vital signs and behavior to provide daily and comprehensive long-term record on health status. However, radar sensor data collected from various users and families usually involve sensitive personal information, while traditional deep learning technologies using IoMT radar data may present high privacy leakage risks. This paper proposes FedRadar, a novel federated multi-task transfer learning framework for radar-based heartbeat rate and activity monitoring in IoMT to solve these challenges. It deals with the decentralized structure, training personalized multi-task models collaboratively, combining the shared relevance knowledge of human’s physiological information while keeping personal radar data locally for privacy protection. First, the multi-task neural network is built on the spatial-temporal radar data to capture the potential relationship and shared representations between human’s vital sign and activity. Furthermore, the federated learning with knowledge transfer scheme is designed to achieve personalized local models by transferring coarse relevant features and keeping fine-grained individual information. Extensive experiments demonstrate the effectiveness and robustness of FedRadar with 2.8% and 2.5% superior than local training model respectively on the accuracy of heartbeat rate estimation and activity classification in realistic constructed radar datasets. In addition, FedRadar is extensible and suitable to continuously monitor multiple health indicators with privacy protection in IoMT. The FedRadar codes and constructed radar datasets are available on https://github.com/bupt-uwb/FedRadar.

While research in passive flexible circuits for Wireless Power Transfer (WPT) such as coils and resonators continues to advance, limitations in their power handling and low efficiency have hindered the realization of efficient all-printed high-power wearable WPT receivers. Here, we propose a screen-printed textile-based 6.78 MHz resonant inductive WPT system using planar inductors with concealed metal-insulator-metal (MIM) tuning capacitors. A printed voltage doubler rectifier based on Silicon Carbide (SiC) diodes is designed and integrated with the coils, showing a power conversion efficiency of 80-90% for inputs between 2 and 40 W. Compared to prior wearable WPT receivers, it offers an order of magnitude improvement in power handling along with higher efficiency (approaching 60%), while using all-printed passives and a compact rectifier. The coils exhibit a simulated Specific Absorption Rate (SAR) under 0.4 W/kg for 25 W received power, and under 21◦C increase in the coils’ temperature for a 15 W DC output. We finally demonstrate a wirelessly-powered textilebased carbon-silver Joule heater, capable of reaching up to 60◦C at 2 cm separation from the transmitter, as a potential wearable application which can only be wireless-powered using the proposed system.

This paper presents a time-domain formulation of a meshless method based on the generalized sheet transition conditions (GSTCs). This formulation is applicable for modeling a kind of dispersive metasurface and a time-varying metasurface. This is the first time a meshless method is developed for analysis of the metasurfaces using GSTCs. The significance of time-domain meshless methods originates from their capabilities for multi-scale and conformal modeling in addition to analysis of problems in a broad frequency-range. Here, for simplicity, the meshless method formulation is presented for analysis of 1-D problems consisting of metasurfaces which are monoisotropic. The extension of this method to 2-D and 3-D problems and bianisotropic metasurfaces is straightforward. The efficiency of the proposed formulations is verified by numerical examples.

The power-synchronization control has been proven effective to enhance the stability of voltage-source converters (VSCs) in weak grids. This method can be realized with the dc-link voltage control for VSCs with regulated dc-link dynamics, which leads to the dc-link voltage-synchronization control. This paper analyzes the small-signal stability of dc-link voltage-synchronized VSCs, considering the effect of constant power load/source at the dc link. It is found that low-frequency oscillations may arise with dc-link constant-power dynamics. To dampen the oscillation, an active damping control method based on the active power feedforward (PFF) control is proposed. A notch filter tuned at the synchronous frequency is embedded in the PFF loop to avoid the adverse effect on synchronous oscillation. Design guideline for the PFF control parameters is formulated. Experimental tests of VSC operating in the rectifier and the inverter modes are performed. The results validate the theoretical analysis and the effectiveness of the control approach.