Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Hybrid Beamforming With Low-Resolution PSs for Wideband mmWave MIMO-OFDM Systems

Analog/digital hybrid beamforming is considered as a key enabling multiple antenna technology for implementing millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications since it can reduce the number of costly and power-hungry radio frequency (RF) chains while still providing for spatial multiplexing. In this paper, we introduce a novel hybrid beamforming architecture with dynamic antenna subarrays and hardware-efficient low-resolution phase shifters (PSs) for a wideband mmWave MIMO orthogonal frequency division multiplexing (MIMO-OFDM) system. By dynamically connecting each RF chain to a non-overlapping antenna subarray via a switch network and PSs, multiple-antenna diversity can be exploited to mitigate the performance loss due to the employment of practical low-resolution PSs. For this dynamic hybrid beamforming architecture, we jointly design the hybrid precoder and combiner to maximize the average spectral efficiency of the mmWave MIMO-OFDM system. In particular, the spectral efficiency maximization problem is first converted to a mean square error (MSE) minimization problem. Then, an efficient iterative hybrid beamformer algorithm is developed based on classical block coordination descent (BCD) methods. An analysis of the convergence and complexity of the proposed algorithm is also provided. Extensive simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm with dynamic subarrays and low-resolution PSs.
A Dynamic-Biased Resistor-Based CMOS Temperature Sensor With a Duty-Cycle-Modulated Output

This brief presents a resistor-based CMOS temperature sensor with a duty-cycle-modulated output. A dynamic-biased resistive analog front-end (AFE) is proposed to generate voltages with positive and negative temperature dependencies. The voltages are then converted into a digital-friendly duty-cycle-modulated output, which can be proceeded by digital systems directly. Fabricated in a standard 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, this sensor occupies a silicon area of 0.025 mm<sup>2</sup> and can operate with a supply voltage as low as 0.8 V. It has a measured inaccuracy of ±0.85 °C (<inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula>) from −40 °C to 85 °C after a two-point calibration. Measured at room temperature, it shows a resolution of 0.226 °C in a 0.25-ms conversion time while dissipating 12.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>.
A Novel Approach for Atrial Fibrillation Signal Identification Based on Temporal Attention Mechanism

Atrial fibrillation (AF) is a common heart rhythm which occurs when the upper chambers of the heart beat irregularly. With the rapid development of the deep learning algorithm, the Convolutional Neural Networks (CNN) is widely investigated for the ECG classification task. However, for AF detection, the performance of CNN is greatly limited due to the lack of consideration for temporal characteristic of the ECG signal. In order to improve the discriminative ability of CNN, we introduce the attention mechanism to help the network concentrate on the informative parts and obtain the temporal features of the signals. Inspired by this idea, we propose a temporal attention block (TA-block) and a temporal attention convolutional neural network (TACNN) for the AF detection tasks. The TA-block can adaptively learn the temporal features of the signal and generate the attention weights to enhance informative features. With a stack architecture of TA-blocks, the TA-CNN obtains better performance as a result of paying more attention to the informative parts of the signal. We validate our approach on the single lead ECG classification dataset of The PhysioNet Computing in Cardiology Challenge 2017. The experimental results indicate that the proposed framework outperform state-of-the-arts classification networks.Clinical Relevance—The proposed algorithm can be potentially applied to the portable cardiovascular monitoring devices reducing the danger of AF
Epiphytic Bryophyte Communities of Prunus lusitanica Iberian Forests: Biogeographic Islands Shaped by Regional Climates

Abstract Epiphytic communities of Iberian forests remain partly unknown and most studies have focused on the dominant oak forests. We provide a comprehensive analysis and interpretation of the epiphytic bryophyte communities of forests dominated by the Tertiary relict evergreen cherry Prunus lusitanica. This type of forest, scattered in the western and northern half of the Iberian Peninsula, harbours a noticeable richness of epiphytic bryophytes, including an outstanding number of liverwort species. Their floristic composition varies markedly across the Peninsula yet is driven by the main climate patterns prevailing in the area. Multivariate analyses (TWINSPAN, CCA) render two main groups of epiphytic communities with their respective indicator species. Both groups share a high proportion of non-Mediterranean species, a circumstance that is most remarkable in the forests that fall within the Mediterranean Region, which could be considered as ecological refuges or biogeographic islands.
Novel Multilevel Inverter Based Standalone PV System Using Reduced Number of Components

Research effort to develop new multilevel inverters (MLIs) for the photovoltaic (PV) system is emergent. The conventional MLI consists of a large number of switches and diodes. Due to this, the power losses increase and the overall system efficiency decreases. In this work, two novel MLI structures (DS1 and DS2) are introduced. The DS1 and DS2 structures are interfaced with PV system through a single-switch single-inductor single-input multiple-output dc-dc converter. The output capacitances of the dc-dc converter are self-balanced without employing any closed-loop control. Both the DS1 and DS2 structures consist of an equal number of components for generating a seven-level output. However, DS1 does not possess reactive power handling ability unlike the DS2. Both the structures are compared with the state-of-art MLIs in terms of the number of switches, number of diodes, and reactive power handling ability. After analyzing the structural functioning of both the MLIs, selective harmonic elimination (SHE) modulation technique is discussed that is further used to control the MLI. The structure DS2 is simulated with PV system and experimentally verified considering a similar rating system.
Application of EEMD-HHT Method on EEG Analysis for Speech Evoked Emotion Recognition

Electroencephalograph (EEG) is widely used to study human brain activities. However, the interpretation of EEG signals is still a challenging computational task. Emerging evidence has shown that the non-stationary traits of EEG signals hinder the way of informative interpretation. Compared to the classical Welch frequency analysis method (short-time Fourier transform), Hilbert Huang Transform(HHT) is more suitable for non-linear and non-stationary signals. This paper proposes a band energy extraction method based on EEMD-HHT for time-frequency analysis of EEG signals. We evaluate the method on an EEG database obtained through the emotional cognitive experiment. The auditory stimulus in this paper are selected from CHEAVD2 which is a speech emotion database of the Chinese Academy of Sciences. The correlation coefficients between the predict and target values reach 0.51 and 0.43 for arousal and valence dimension, respectively. This method shows great potentials in applications of computational neuroscience and cognition of art creation.
300-GHz-Band 120-Gb/s Wireless Front-End Based on InP-HEMT PAs and Mixers

We developed a 300-GHz-band 120-Gb/s wireless transceiver front-ends (TRX) using our in-house InP-based high-electron-mobility-transistor (InP-HEMT) technology for beyond-5G. The TRX is composed of the RF power amplifiers (PAs), mixers, and local oscillation (LO) PAs which are all packaged in individual waveguide (WG) modules by using a ridge coupler for low-loss WG-to-IC transition. RF PAs are designed using the low-impedance inter-stage-matching technique to reduce the inter-stage matching loss of the amplifier stages, and the back-side DC line (BDCL) technique is used to simplify the layout and to improve the gain of the PAs. The fabricated RF PAs show a high output 1-dB compression point of more than 6 dBm from 278 to 302 GHz. The mixers are used for both up- and down-conversion in the transmitter and receiver. These mixers are designed to have high conversion gain (CG) over the wideband even after packaging by enhancing the isolation between the RF and IF ports. The measured CG of mixer module is −15 dB, and the 3-dB IF-bandwidth is 32 GHz. The LO PAs are also designed using the BDCL technique so that they can supply the required LO power to the mixers. The TRX with these InP building blocks enables the data transmission of a 120 Gb/s 16QAM signal over a link distance of 9.8 m.
Controller Hardware in the Loop Testing of an IEC 61850 GOOSE Based Control for Seamless Transition of a Microgrid Between Island and Grid Connected Modes

This paper demonstrates development of a controller hardware in the loop test system for validating microgrid control schemes. The investigation also proposes and analyzes the performance of an IEC 61850 Generic ObjectOriented Substation Event Protocol (GOOSE) based control strategy for managing the transition of a microgrid test model from grid-connected mode to island mode. The test model contains a single inverter-interfaced distributed generation (DG) unit. The inverter controls allow the DG to regulate its output during grid connected operation and regulate the microgrid voltage and frequency when separated from the area electric power system. The test model, inverter and plant controllers are simulated using RTDS Technologies’ real-time digital simulator. Digital protection relays are used in the hardware loop to provide I/O to the simulated controllers via GOOSE messaging. The results of the investigation illustrate the benefits of IEC 61850 standard for integration of controller hardware in a microgrid control scheme as well as the suitability of GOOSE messaging for seamless transitioning between microgrid operating modes.
Decision Making over Multiple Criteria to Assess News Credibility in Microblogging Sites

Locating Web content useful to specific user needs and tasks concerns nowadays, in many circumstances, to assess the credibility of the content itself. With the diffusion of social media and the possibility for everyone to become a content generator, the problem of assessing information credibility has become a major research issue, in particular in microblogging sites, where fake news, hoaxes and other kinds of misinformation are diffused almost without any traditional form of trusted intermediation. In this paper, we propose an approach based on multiple criteria associated with news, on which the use of aggregation operators guided by linguistic quantifiers allow the modeling of the decision maker behavior into the news credibility assessment process. The operation and the evaluation of the approach are illustrated by considering the Twitter microblogging platform.
Recent Advances of Resource Allocation in Network Function Virtualization

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. Due to the numerous advantages such as flexibility, efficiency, scalability, short deployment cycles, and service upgrade, NFV has been widely recognized as the next-generation network service provisioning paradigm. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order through which data flows traverse, and it is also known as the Service Function Chaining (SFC). In this article, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the generalized problems by considering various QoS parameters (e.g., cost, delay, reliability, and energy) and different scenarios (e.g., edge cloud, online provisioning, and distributed provisioning). Finally, we conclude our article with a short discussion on the state-of-the-art and emerging topics in the related fields, and highlight areas where we expect high potential for future research.
Localization of Life Safety Vests in an Aircraft Using Backscattering RFID Communication

In an aircraft, a variety of equipment are installed for the safety of the passengers, including seat belts, life vests, oxygen masks and cylinders, which require traceability for an efficient maintenance schedule. Furnishing them with RFID tags can enable the maintenance personnel to obtain accurate service history by just querying the tags. Overwater air travel mandates that the life vests should be present under each passenger seat before the aircraft is airborne. A mere count of life vests is insufficient as locating the seat that has a missing vest is required - a time consuming and non-trivial activity. We focus on 2D tracking of the life vests using backscattering RFID communication. Our study is limited to single aisle aircraft, although, it can be extended seamlessly to twin aisle wide body aircraft. A Divide-and-Conquer approach has been implemented, using COTS components, for locating the tags based on the phase measured from the signals backscattered by the tags. Extensive experiments are conducted by emulating the environment in an aircraft mockup fuselage. We experimented with two different setups, viz., Standard Mobile Platform and Novel Handheld setups. Average percentage errors of 4.7% and 4.6% have been achieved with the Handheld setup in the x and y directions, respectively.
Active Content Popularity Learning and Caching Optimization With Hit Ratio Guarantees

Edge caching is an effective solution to reduce delivery latency and network congestion by bringing contents close to end-users. A deep understanding of content popularity and the principles underlying the content request sequence are required to effectively utilize the cache. Most existing works design caching policies based on global content requests with very limited consideration of individual content requests which reflect personal preferences. To enable the optimal caching strategy, in this article, we propose an Active learning (AL) approach to learn the content popularities and design an accurate content request prediction model. We model the content requests from user terminals as a demand matrix and then employ AL-based query-by-committee (QBC) matrix completion to predict future missing requests. The main principle of QBC is to query the most informative missing entries of the demand matrix. Based on the prediction provided by the QBC, we propose an adaptive optimization caching framework to learn popularities as fast as possible while guaranteeing an operational cache hit ratio requirement. The proposed framework is model-free, thus does not require any statistical knowledge about the underlying traffic demands. We consider both the fixed and time-varying nature of content popularities. The effectiveness of the proposed learning caching policies over the existing methods is demonstrated in terms of root mean square error, cache hit ratio, and cache size on a simulated dataset.
Partial Ternary Full Adder versus Complete Ternary Full Adder

This paper explores whether or not a complete ternary full adder, whose input variables can independently be ‘0’, ‘1’, or ‘2’, is indispensable in the arithmetic blocks of adder, subtractor, and multiplier. Our investigations show that none of these arithmetic units require a complete ternary full adder. Instead, they can be designed by means of partial ternary full adder, whose input carry never becomes ‘2’. Therefore, ternary adders can be realized with fewer transistors and higher performance. Afterwards, three well-known complete ternary full adders are changed into partial versions. Simulation results show considerable improvements for the partial ternary full adders over the complete versions.
Study of Performance of Knitted Conductive Sleeves as Wearable Textile Strain Sensors for Joint Motion Tracking

Textile-based strain sensors combine wearability with strain sensing functionality by using only the tensile and electrical properties of the threads they are made of. In this study, two conductive sleeves were manufactured for the elbow and three for the knee using a Santoni circular machine with different combinations of elastomeric and non-elastomeric yarns. Linearity, repeatability and sensitivity of the sleeves resistance with strain were compared during 5 repetitive trials, each of them consisting of 4 sequences of 50 joint flexion-extension cycles. All knitted conductive sleeves registered motion over 1000 cycles, proving their suitability for joint motion tracking. In addition, sleeves whose inner layer was made only with nylon exhibited the highest sensitivity and predictability of changes (i.e. a linear trend of the non-elastic deformation). On the other hand, sleeves whose inner layer was made with lycra and polyester or lycra and nylon showed a more balanced performance in terms of linearity, sensitivity and repeatability either for low or high number of cycles. Based on requirements, knitted conductive sleeves show a potential for application in rehabilitation both in healthcare and sports.
Insights from interval-valued ratings of consumer products—a DECSYS appraisal

The capture and analysis of interval-valued data has seen increased interest over recent years. This offers a direct means to capture and reason about uncertainty in data, whether obtained from sensors or from people. Open-source software (DECSYS [1]) was recently released to facilitate the efficient capture of interval-valued survey responses. Potential real-world applications are broad ranging, and this paper documents an initial test-case of the software and its underpinning methodology, in a marketing-centric application. It provides an illustration of the insights offered by interval-valued responses, in this case relating to consumer preferences. We apply two approaches to describe and draw insights from the data: inferential statistics and descriptive visualisation methods. Statistical results indicate that overall purchase intention was well-described by four factors: value, healthiness, taste and brand. The capture of uncertainty information, afforded by intervals, also permitted identification of six factors that contribute to purchase intention uncertainty— relating to taste, ethics and visual appearance. Visualisations of interval-valued responses, using the IAA [2]–[5], also highlighted factors with high degrees of uncertainty—in particular, product ethics. This information could prove valuable for retailers in determining how to focus future marketing campaigns. It may prove equally valuable for market regulators, by informing where to improve product labelling information. More generally, the case study provides an overview of capturing and analysing intervals, highlighting some of the challenges, but also the unique potential to gain additional insights not available using conventional, ‘crisp’, approaches.
Characterizing Multiple Patterns of Motor Intent Using Spatial-Temporal Information for Intuitively Active Motor Training in Stroke Survivors

Upper extremity motor function loss severely affects stroke survivors during daily life activities. Different rehabilitation robotic systems have been developed to allow stroke survivors regain their motor function. Meanwhile, most of the robots only operate in a passive mode and restrict the users to navigate predefined trajectories which may not align with their motion intent, thus limiting motor recovery. One way to resolve this issue would be to utilize a decoded movement intent to trigger intuitively active motor training for patients. In this direction, this study proposed and investigated the use of spatial-temporal neuromuscular descriptor (STD) for optimal decoding of multiple patterns of movement intents in patient to provide inputs for active motor training in the rehabilitation robotic systems. The STD performance was validated using High-Density surface electromyogram recordings from five stroke survivors who performed 21 limb movements. Experimental results show that the STD achieved a significant reduction in limb movement classification error (13.36%) even in the presence of the inevitable White Gaussian Noise compared to other methods (p<0.05). The STD also showed obvious class separability for individual movement. Findings from this study suggest that the STD may provide potential inputs for intuitively active motor training in stroke rehabilitation robotic systems.Clinical Relevance— This study showed that spatial-temporal neuromuscular information could aid adequate decoding of movement intents upon which intuitively active motor training could be achieved in stroke rehabilitation robotic systems.
Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets

In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative computed tomography (CT) images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we propose a region segmentation method using U-net for the lung, which is an organ that deforms substantially during surgery. Because the accuracy of the results for smoker lungs is lower than that for non-smoker lungs, we improved the accuracy by translating the texture of the lung surface using a CycleGAN.