Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wake-Up Receiver With a Multi-Stage Self-Mixer and With Enhanced Sensitivity When Using an Interferer as Local Oscillator

An ultra-low power wake-up receiver with an energy-detection passive-RF architecture uses a multi-stage self-mixer that has a better conversion gain than the conventional envelope detector. The self-mixer, co-designed with the RF matching network, optimizes the sensitivity and minimizes the power consumption of the receiver. A wake-up receiver prototype in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS operates at 550 MHz, consumes 220 nW from 0.5 V, and achieves a sensitivity of −56.4 dBm at a 400-kb/s chip rate using an 11-bit wake-up code. When a large interferer is present, the receiver operates in an interferer-enhanced mode, leveraging the interferer as a local oscillator to improve the sensitivity; in the presence of a −43.5-dBm interferer, a −63.6-dBm sensitivity is achieved while consuming 1.1 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>.
Optical Imaging of Ventricular Action Potentials in a Torso Tank: A New Platform for Non-Invasive Electrocardiographic Imaging Validation

Background: Non-invasive electrocardiographic imaging (ECGI) is a promising tool to provide high-resolution panoramic imaging of cardiac electrical activity noninvasively from body surface potential measurements. Current experimental methods for ECGI validation are limited to comparison with unipolar electrograms and the relatively low spatial resolution of cardiac mapping arrays. We aim to develop a novel experimental set up combining a human shaped torso tank with high-resolution optical mapping allowing the validation of ECGI reconstructions. Methods: Langendorff-perfused pig hearts (n = 3) were suspended in a human torso-shaped tank, with the left anterior descending artery (LAD) cannulated on a separate perfusion. Electrical signals were recorded from an 108-electrode epicardial sock and 128 electrodes embedded in the tank surface. Simultaneously, optical mapping of the heart was performed through the anterior surface of the tank. Recordings were made in sinus rhythm and ventricular pacing (n = 55), with activation and repolarization heterogeneities induced by perfusion of hot and cold solutions as well as Sotalol through the LAD. Fluoroscopy provided 3D cardiac and electrode geometries in the tank that were transformed to the 2D optical mapping window using an optimization algorithm. Epicardial unipolar electrograms were reconstructed from torso potentials using ECGI and validated using optical activation and repolarization maps. Results: The transformation and alignment of the 3D geometries onto the 2D optical mapping window was good with an average correlation of 0.87 ± 0.10 and error of 7.7 ± 3.1 ms with activation derived from the sock. The difference in repolarization times were more substantial (error = 17.4 ± 3.7 ms) although the sock and optical repolarization patterns themselves were very similar (correlation = 0.83 ± 0.13). Validation of ECGI reconstructions revealed ECGI accurately captures the pattern of activation (correlation = 0.79 ± 0.11) and identified regions of late and/or early repolarization during different perfusions through LAD. ECGI also correctly demonstrated gradients in both activation and repolarization, although in some cases these were under or over-estimated or shifted slightly in space. Conclusion: A novel experimental setup has been developed, combining a human-shaped torso tank with optical mapping, which can be effectively used in the validation of ECGI techniques; including the reconstruction of activation and repolarization patterns and gradients.
Recommending Personalized Summaries of Teaching Materials

Teaching activities have nowadays been supported by a variety of electronic devices. Formative assessment tools allow teachers to evaluate the level of understanding of learners during frontal lessons and to tailor the next teaching activities accordingly. Despite plenty of teaching materials are available in the textual form, manually exploring these very large collections of documents can be extremely time-consuming. The analysis of learner-produced data (e.g., test outcomes) can be exploited to recommend short extracts of teaching documents based on the actual learner’s needs. This paper proposes a new methodology to recommend summaries of potentially large teaching documents. Summary recommendations are customized to student’s needs according to the results of comprehension tests performed at the end of frontal lectures. Specifically, students undergo multiple-choice tests through a mobile application. In parallel, a set of topic-specific summaries of the teaching documents is generated. They consist of the most significant sentences related to a specific topic. According to the results of the tests, summaries are personally recommended to students. We assessed the applicability of the proposed approach in real context, i.e., a B.S. university-level course. The results achieved in the experimental evaluation confirmed its usability.
A Fully Decentralized Adaptive Droop Optimization Strategy for Power Loss Minimization in Microgrids With PV-BESS

In this paper, a fully decentralized adaptive droop optimization strategy for inverters is proposed for minimizing power loss during power transmission in islanded microgrids integrated with solar photovoltaic systems. From a hierarchical point of view, the proposed control architecture of the distributed generators encompasses both the primary and secondary levels in a decentralized plug-and-play manner. The primary level, realized by a droop controller, is in charge of the fast response in load sharing among all the generation units in the microgrid. The secondary level control, with a larger operation time scale, has the objective of minimizing the power loss in the system, which is achieved by the proposed adaptive droop functions. The advantage of the proposed droop optimization strategy is that the power generation units are fully decentralized by using only local measurements. The adaptability of the droop functions is achieved by adopting a perturbation and observation (P&O) method. Particularly, when a small perturbation in the offset of the $P\text{--}f$ droop functions is introduced, the resultant effects on frequency and generated power are examined in order to select the offsets with the minimum power generation, indicating that the power loss is minimum. The P&O process is performed iteratively by every participating generation unit with a fixed perturbation over a constant time step. Eventually, the system converges to a steady state with minimum power loss. The process within the secondary controller continues indefinitely, and any change in loads or grid configuration will bring the system to a new steady state with minimum power loss.
Diet Induced Obesity Alters Intestinal Cytoplasmic Lipid Droplet Morphology and Proteome in the Postprandial Response to Dietary Fat

Dietary fat absorption by the small intestine is an efficient, multistep process that regulates the uptake and delivery of essential nutrients and energy. Fatty acids taken up by enterocytes, the absorptive cells of the small intestine, are resynthesized into triacylglycerol (TAG) and either secreted in chylomicrons or temporarily stored in cytoplasmic lipid droplets (CLDs). Proteins that associate with CLDs are thought to regulate the dynamics of TAG storage and mobilization. It is currently unclear what effect diet induced obesity (DIO) has on the balance between dietary fat storage and secretion. Specifically, there is limited knowledge of how DIO affects the level and diversity of proteins that associate with CLDs and regulate CLD dynamics. In the current study, we characterize CLDs from lean and DIO mice through histological and proteomic analyses. We demonstrate that DIO mice have larger intestinal CLDs compared to lean mice in response to dietary fat. Additionally, we identified 375 proteins in the CLD fraction isolated from enterocytes of lean and DIO mice. We identified a subgroup of lipid related proteins that are either increased or unique to the DIO CLD proteome. These proteins are involved in steroid synthesis, TAG synthesis, and lipolysis. This analysis expands our knowledge of the effect of DIO on the process of dietary fat absorption in the small intestine (D’Aquila, 2016).
Simulating The Thermoelectric Behaviour of CNT Based Harvester

Thermoelectric harvesters, where electrical energy is produced from temperature difference, show interest among various researchers during the previous decade. Carbon nanotubes (CNT), as a semiconductor, have been utilized as a thermoelectric paste due to its appropriate Seebeck coefficient. However, optimized paste wasn’t highlighted in literature due to lack in an efficient numerical model. Herein, we propose a novel numerical simulation model, based on Comsol Multiphysics, to simulate the thermoelectric behavior of a CNT based harvester. Simulation data is validated by experimentally prepared and characterized paste. The comparison showed an acceptable agreement.
Spatial Multi-Criteria Decision Analysis for Rehabilitation Priority Ranking: A Collaborative Application to Heritage Workforce Housing Sites

ABSTRACT This work presents a methodology to rank heritage sites regarding rehabilitation, considering both the characteristics of building sites and of the urban environment in the surrounding area. The objective is to aid the decision process of building rehabilitation by ranking the sites according to their potential for re-emergence in the affordable housing rental market. The developed methodology is based on a combination of multi-criteria decision analysis (MCDA) and spatial analysis of geographical data, in order to construct an index, the “rehabilitation potential”, which is understandable by rehabilitation technicians and land managers and is applicable to support a list of priorities of building rehabilitation interventions. The methodology was applied to a case study consisting of a set of 33 heritage sites of the workforce housing typology in Lisbon. These were built in the early industrial age in Portugal and are owned by the city municipality. The application of MCDA was a collaborative process that brought together the expertise of the academy and of the public administration. The results included a sensitivity analysis and gave form to a recommendation of five sites, selected from the total workforce housing set, to be rehabilitated in the near future.
HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?

Functional connectivity analysis by detecting neuronal co-activation in the brain can be efficiently done using Resting State Functional Magnetic Resonance Imaging (rs-fMRI) analysis. Most of the existing research in this area employ correlation-based group averaging strategies of spatial smoothing and temporal normalization of fMRI scans, whose reliability of results heavily depends on the voxel resolution of fMRI scan as well as scanning duration. Scanning period from 5 to 11 minutes has been chosen by most of the studies while estimating the connectivity of brain networks. In this study, we have demonstrated that Dictionary Learning (DL) based rs-fMRI analysis, due to its inherent sparsity constraint-based matrix factorization property, can identify all the functional networks efficiently even with a limited number of scans. Preliminary results demonstrate that a scan of 1 minute is sufficient for DL to produce all the networks with better accuracy, computation, and reliability than existing methods.
Efficient Integration of Cognitive Radio in a Multiuser MIMO Channel Environment

The design of energy and spectrally efficient multiuser (MU) MIMO networks is a very challenging problem with many tradeoffs to consider. In this paper we address the problem of designing energy efficient (EE) Primary Multiuser (P-MU) MIMO networks when Cognitive Radio (CR) Secondary users (SU) are being incorporated. We investigate what are the proper channel conditions for integration of the CR transceivers in a P-MU MIMO network. We aim to minimize the total consumed power with the maximum possible spectral efficiency. This paper also addresses the application of CR combined with automatic RF signal classifier (RFSC). It argues that the two applications can be complementary and the latter can support another mode of operation for CR. An SU can operate in presence of an active primary user (PU) without interfering, unlike the standard approach whereby SUs should not be activated when active PUs are sensed. OFDM is the signaling strategy of choice for the P-MU to do spectrum sharing and control interference.
CONCEPT AND EVALUATION OF MOBILE CELL CONNECTIVITY OVER A SATELLITE BACKHAUL FOR FUTURE 5G NETWORKS

A terrestrial network deployment with mobile cell connectivity using a bandwidth limited satellite backhaul is being investigated in terms of the integration of state-of-the-art 4G commercial off-the-shelf network components. A testbed is analyzed for providing end-to-end quality of service and flexible resource allocation on the satellite link using a novel multi-carrier modem technology with dynamic bandwidth allocation. To minimize network traffic delay and enable backhaul independent internal cell communication, virtual nodes are exploited to distribute core network components to the edge of the cell co-located with the radio base stations.
On-Policy Dataset Synthesis for Learning Robot Grasping Policies Using Fully Convolutional Deep Networks

Rapid and reliable robot grasping for a diverse set of objects has applications from warehouse automation to home de-cluttering. One promising approach is to learn deep policies from synthetic training datasets of point clouds, grasps, and rewards sampled using analytic models with stochastic noise models for domain randomization. In this letter, we explore how the distribution of synthetic training examples affects the rate and reliability of the learned robot policy. We propose a synthetic data sampling distribution that combines grasps sampled from the policy action set with guiding samples from a robust grasping supervisor that has full state knowledge. We use this to train a robot policy based on a fully convolutional network architecture that evaluates millions of grasp candidates in 4-DOF (3-D position and planar orientation). Physical robot experiments suggest that a policy based on fully convolutional grasp quality CNNs (FC-GQ-CNNs) can plan grasps in 0.625 s, considering 5000x more grasps than our prior policy based on iterative grasp sampling and evaluation. This computational efficiency improves rate and reliability, achieving 296 mean picks per hour (MPPH) compared to 250 MPPH for iterative policies. Sensitivity experiments explore the effect of supervisor guidance level and granularity of the policy action space. Code, datasets, videos, and supplementary material can be found at http://berkeleyautomation.github. io/fcgqcnn.
Simulating the Effect of Permanent Magnet Synchronous Machine on Dipole Antenna Radiation in Industrial Site

The use of wireless sensor network (WSN) in industrial site has been increasing significantly. WSN transmits and collects the data through electromagnetic waves. The characteristic of electromagnetic wave is affected by the environment where electromagnetic wave is propagating. The electromagnetic propagation in industrial site encounters various challenges, caused not only by the object around it but also by the noise disturbances coming from the electrical machine. These disturbances can degrade the performance of WSN. Therefore, simulating the performance of WSN prior to the implementation is suggested, so that errors during implementation can be avoided. This paper presents a simulation to determine the effect of disturbances to radiation of electromagnetic wave from operating the WSN. In this research, WSN was modelled by dipole antenna with 2.4 GHz frequency, while the electrical machine was modelled as permanent magnet synchronous machine (PMSM). Both models were simulated in simple 3D industrial site consist of some object. The results show that the characteristic of radiation from dipole antennae, e.g., gain, S-parameter, and directivity was affected by electrical machine.
Trust Evaluation in Cross-Cloud Federation

Cross-Cloud Federation (CCF) is beneficial for heterogeneous Cloud Service Providers (CSPs) for leasing additional resources from each other. Despite the benefits of on-demand scalability and enhanced service footprints for better service quality, the adoption of CCF is however mainly hindered due to the lack of a comprehensive trust model. The basic aim of such a model should be to address the security and performance concerns of a home CSP on its foreign peers before placing its users’ data and applications in their premises. A transitivity of users’ trust on home CSP and home CSP's trust on its foreign CSPs marks the uniqueness of trust paradigm in CCF. Addressing the concerns of cloud-to-cloud trust paradigm is inevitable to achieve users’ trust in a federation. Various trust models have been proposed in literature for conventional and multi-cloud computing environments. They focus on user requirements but none on federation perspective. Their applicability to CCF for addressing the concerns of cloud-to-cloud trust paradigm requires further consideration. For this reason, we have first outlined the general characteristics of CCF as being dynamic, multi-level and heterogeneous. Afterwards, cloud-to-cloud trust paradigm is proposed based on a set of unique principles identified as (i) trust bi-directionality, (ii) trust composition, (iii) delegation control, and (iv) Resource awareness. An insightful review of Trust Management Systems (TMS) proposed in literature reveals their shortcomings in addressing the requirements of cloud-to-cloud trust paradigm. To overcome these shortcomings, we suggest that some challenges can be merely addressed by aligning the existing methods to the nature of CCF. The remaining challenges require entirely new mechanisms to be introduced. A demonstration of this concept is presented in the form of a requirement matrix suggesting how the characteristics and properties of both CCF and the TMS are influenced by each other. This requirement matrix reveals the potential avenues of research for a TMS aimed specifically for CCF.
Comparison Accuracy W-NN and WD-SVM Method In Predicted Wind Power Model on Wind Farm Pandansimo

Wind power is one of the environmentally friendly renewable energy systems. The biggest obstacle is the operation of wind energy at intermittent wind speeds. Unstable wind speed conditions tend to be very influential in the production of electricity produced. For this reason, this study aims to create and compare wind power prediction models using the WD-NN and WD-SVM methods in wind fields that have very high intermittent levels such as Pandansimo wind farms.
Convolutional Neural Network Trained by Joint Loss for Hyperspectral Image Classification

In this letter, it is proposed the hyperspectral image classification method based on the convolutional neural network, which is trained jointly by the reconstruction and discriminative loss functions. In the network, small convolutional kernels are cascaded with the pooling operator to perform feature abstraction, and a decoding channel composed of the deconvolutional and unpooling operators is established. The unsupervised reconstruction, performed by the decoding channel, not only introduces priors to the network training but also is made use to enhance the discriminability of the abstracted features by the control gate. By the experiments, it is shown that the proposed method performs better than the state-of-the-art neural network-based classification methods.
Initialization of Active Contour for Dermoscopic Image Segmentation: A Comparative Study

Segmentation is an important step in medical image processing and analysis. Active contour (AC) is the one of the most effective methods for image segmentation. In this paper, the model of active contour without edge is used to segment the lesions in dermoscopic images, where it is more effective than the traditional snake. However, its performance depends on the initial curve. This motivates the current work to apply different shape of mask as an initial contour of Chan-Vese segmentation method to study the segmentation performance on the dermoscopic image. Evaluation metrics are calculated to measure the segmentation performance. The experimental results establish that the circular initial mask provids the best performance, where it is approximately has the same shape of the skin lesion regions. In addition, this initial circular mask achieves the best segmentation performance if it has 49% size from the whole dermoscopic image size.
The Whale Optimization Algorithm based controller for PMSG wind energy generation system

The direct drive permanent magnet synchronous generator (PMSG) have many advantages consequently, it ends up a standout amongst the most appealing variable speed wind generation types. This paper proposes a novel application of a Whale Optimization Algorithm (WOA) aims at finding the optimal parameters of the conventional Proportional-Integral (PI) controllers for the permanent magnet synchronous generator based wind energy conversion system (WECS). The Whale Optimization Algorithm (WOA) is applied to the classical PI-controller on machine side converter system to find the optimal gains of the PMSG, to such an extent that a maximum power point tracking (MPPT) under normal condition, a step change of wind speed and the optimal dynamic performance of PMSG under grid fault can be easily achieved. Three case studies in this paper, dynamic responses of the system under normal operation, a step change of wind and a three-phase symmetrical fault with using crowbar protection and without crowbar protection. The optimal values that result from the WOA algorithm are compared with the simplex method.
A 5.3-ps, 8-b Time to Digital Converter Using a New Gain-Reconfigurable Time Amplifier

Time amplifiers (TAs) are the key building blocks of the two-step time-to-digital converters. High resolution TAs suffer from inaccuracy the gain due to employing meta-stability behavior of the SR latches. In the proposed method, two offset NAND gates are placed in parallel with the NAND gates of the conventional SR latch to get a linear re-configurable gain. Gain of the TA is controlled only by the driving strength of the NAND gates. To confirm the effectiveness of the proposed method, an 8-bit two step time to digital converter (TDC) was designed and laid-out in 0.18- ${\mu }\text{m}$ CMOS technology. Using a supply voltage of 1.2 V, the proposed TDC consumes 1.1 mW at 30 MS/s throughput.