Miracle-dz/UniFAR-FTUs huggingface dataset:
AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation

Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis.
Sizing and Control of a Multilevel Converter for Photovoltaic Generation with Independent Power Extraction Capabilities per Module

This work presents the sizing and the control scheme of a modular multilevel converter for grid-connected photovoltaic generation that is capable of extracting different powers from each one of the sub-inverters, and simultaneously manage reactive power injection to the grid. With the division of the entire PV array into several equal independent subarrays and then connecting each to a module of the multilevel converter, the disadvantage of partial shading and its consequent total loss of power are removed. Conversely, the PV subarrays can deliver different powers, due to transient cloud shading, differences in ambient temperature, and even due to unequal degradation of the PV panels in the long term. The approach used to accomplish a different power extraction capability in the converter modules is based on variating the modulation indexes. The principle of operation is exposed, and then a methodology to size the dc-link voltages, number of modules, and components of the converter, based on the total power and voltage of the system, is proposed. Based on this, a control scheme is also proposed accordingly.
Mood Classification with Lyrics and ConvNets

The paper presents research outcomes of classifying music into moods and provides an end-to-end, open source pipeline for mood classification using lyrics. It explores techniques that classify music using audio features and lyrics using various natural language processing methods and machine learning. The paper performs a comparative study across different classification models and mood frameworks. The linguistic aspects of lyrics are explored and are used as features for classification methods to understand what model classifies mood in the most adequate manner. The results show how lyrics are a valuable information source for classification of music. Term-frequency/inverse-document frequency and word embeddings are explored to connect words to mood classes. Various machine learning and deep learning classifiers are tested across different arrangements of the mood labels. The paper demonstrates that models which learn from lyrics using current methods of natural language processing using deep learning demonstrate higher levels of accuracy. Our final model achieves an accuracy of 71%.
On-Chip Dual-Band Millimeter-Wave Power Divider Using GaAs-Based IPD Process

A new on-chip dual-band Wilkinson power divider (WPD), developed on a GaAs-based integrated passive device (IPD) process, is designed at 28/60 GHz for millimeter-wave (mmW) applications. Composite right/left-handed lines in microstrip form are employed to achieve the dual-band property. Thanks to the low-loss feature of the GaAs-based IPD process, the proposed mmW WPD ensures comparable performance when compared to its counterparts at microwave frequencies. Besides, the proposed design, one of the few on-chip dual-band WPDs designed in mmW bands, yields better performance in terms of input matching and power loss when compared with the previous work.
Fault-Localization Protocol for Wireless Sensor Networks

Maintaining network connectivity for reliable acquisition of data is crucial when disseminating the sensed phenomena in wireless sensor networks. The use of Global Positioning System (GPS) is considered as the most reliable method when detecting the location of a particular node in the network. However, localisation techniques using GPS are extremely expensive when the sensor field is massively dense. Many researchers have designed alternative methods such as triangulation where beacon nodes are used for creating a reference point from the nearest neighbor for locating the nodes of interest. Such technique mostly relies on various estimates where chances of error are extremely high if there are problems with the beacon node that is used for creating a reference point. In this paper, we present a novel Green’s Theorem (GT) based Fault-Localisation Protocol (GT-FLP) to identify the faulty nodes and their location in the network. The proposed technique, GT-FLP, uses partitioning and clustering methods for the dense sensor networks. Each cluster has a cluster-head for disseminating data-sets to the sink node. It is also assumed that the network is partitioned such that the neighboring edges of the next cluster are closer than their sensing radius. The sensor nodes also store their Received Signal Strength Indicator (RSSI) values and disseminate information about their battery levels and energy consumption rates. We performed the experiment to investigate, (i) how to efficiently identify faulty nodes and pinpoint their exact location, (ii) dynamically partitioning the network for reliability and adaptability, and (iii) provide mathematical formulation of the localisation problem in the context of wireless sensor networks.
On the importance of TCP splitting proxies for future 5G mmWave communications

5G mmWave technology promises capacities 10 to 100 times that of 4G. However, mmWave links are very sensitive to having direct line of sight between sender and receiver, with dramatically fluctuating capacities due to transient blocking and shadow fading, which can substantially degrade TCP performance. TCP-splitting proxies (SPEPs) are often used to improve TCP performance over wireless links. We investigate current operator SPEPs (used in 4G LTE networks) under mmWave-like dynamics using the MONROE1 testbed. We introduce a mmWave emulation model for an urban canyon scenario, and use it to evaluate how current operational SPEPs impact the performance. Our results from experiments including four operators and three countries, indicate that SPEPs will be even more important for 5G mmWave than they are in LTE. Current 4G SPEPs provide significant, though not optimal, benefits under mmWave-like conditions, allowing them to be utilized as the network transitions to 5G technology.
FitSDN: Flexible Integrated Transactional SDN

Extremely popular with the research community, the SDN has indeed matured and mostly resolved the original performance doubts. The next frontier is to make it popular with the end users, i.e. the administrators and the developers, without sacrificing the performance. Recent distributed SDN proposals still come with a number of design decisions that appear limiting with respect to the ease of administration, application development, and performance. For instance, opting for strong consistency in the controller design results in a complex network administration and can severely impact the performance; choosing the eventual consistency delegates the real problems to application developers: while this design makes the administration lighter, to get the expected behaviour and performance, it requires developers to have profound distributed systems understanding. We argue that this is a bad tradeoff. Consequently, we design, implement and evaluate a novel Flexible Integrated Transactional SDN (FitSDN) that achieves the ease of development and administration, as well as good performances. In a nutshell, FitSDN eliminates the replication of critical network state in the controller instances and taps switch flow tables under a transactional semantics instead. FitSDN enables simple and intuitive APIs that permit SDN application developers to focus on the actual application logic. Comparing FitSDN to a popular distributed controller, FitSDN achieves around 4 times faster responses to network events.
Synchronous Reluctance Motors With an Axially Laminated Anisotropic Rotor as an Alternative in High-Speed Applications

The performance capabilities of an axially laminated anisotropic rotor (ALA) in a high-speed synchronous reluctance motor (SynRM) were studied. A 12 kW ALASynRM was designed as an alternative to a high-speed induction motor (IM) with a solid rotor. The electromagnetic design was implemented taking into account possible issues related to the new manufacturing methods, which require thicker rotor layers than in a typical ALA. The ALASynRM shows a higher efficiency than the corresponding IM with a smooth or slitted solid rotor equipped with copper end rings. To verify the design method, a prototype IM with a smooth solid rotor was built and tested. In the analysis, it was found that, similar to IMs, in an ALASynRM a considerable part of losses takes place in the rotor despite the absence of slip-related losses in the SynRM. The distribution of eddy current losses in the ALA rotor is significantly uneven. The torque ripple in the ALASynRM is considerably larger than the corresponding ripple in IMs.
Analysis of Power, Temperature, and Performance on Mobile Application Processor

Recent mobile devices have multiple cores and high operating frequencies. As a result, their performance has increased, along with their power consumption and temperature, which have become problems to solve. To understand how to operate a CPU efficiently while solving these problems, we study the relationship between power consumption, temperature, and performance as functions of the number of operating cores and operating frequency. We use CPU power consumption that we measured, use steady-state temperature of the CPU calculated by Therminator and use DMIPS that is an index of CPU performance measured by Dhrystone. The experimental results show that the performance increases and temperature decreases as the number of operating cores increases for the same power consumption. In addition, for the same performance, power consumption and temperature decrease as the number of operating cores increases. Consequentially, the quad-core shows a 75.54% performance improvement and 36.04% reductions in temperature compared to the single-core for the same power consumption. In addition, when at the same performance, the quad-core has decreased power consumption and temperature compared to the single-core, 49.42% and 53.94%, respectively. Therefore, operating by increasing the number of cores in the multi-core application processor will effectively increase the performance and lower the power consumption and temperature.
Reliability Modeling and Mitigation for Embedded Memories

CMOS technology scaling has faced over the past recent decades significant variability and reliability challenges both from the manufacturing and operational point of view. It is well recognised that Bias Temperature Instability (BTI) is one of the most (if not the most) aging mechanisms for CMOS technology. The impact of such mechanism has been heavily studied for memory cell array of SRAMS, but not enough for peripheral circuit and its overall impact on the memory functionality. This paper quantifies the impact of BTI on the write path and read path of an SRAM while considering different supply voltages, temperatures, workloads and technology nodes. The results show that the BTI impact is marginal for the write circuitry, irrespective of the workload and technology. In contrast, the impact is much higher (~3× more) for the read path, where the sense amplifier (SA) is the most sensitive part. Therefore, a mitigation scheme for the SA is proposed and evaluated. The results show that the SA offset voltage specification can be reduced significantly (~3.5×),
Autonomous and Reversible Adhesion Using Elastomeric Suction Cups for In-Vivo Medical Treatments

Remotely controllable and reversible adhesion is highly desirable for surgical operations: it can provide the possibility of non-invasive surgery, flexibility in fixing a patch and surgical manipulation via sticking. In our previous work, we developed a remotely controllable, ingestible, and deployable pill for use as a patch in the human stomach. In this study, we focus on magnetically facilitated reversible adhesion and develop a suction-based adhesive mechanism as a solution for non-invasive and autonomous adhesion of patches. We present the design, model, and fabrication of a magnet-embedded elastomeric suction cup. The suction cup can be localised, navigated, and activated or deactivated in an autonomous way; all realised magnetically with a pre-programmed fashion. The use of the adhesion mechanism is demonstrated for anchoring and carrying, for patching an internal organ surface and for an object removal, respectively.
A THz Imaging System Using Linear Sparse Periodic Array

This paper presents our comprehensive study on a THz imaging system using linear sparse periodic array (SPA) at 220 GHz. A 8 Tx and 8 Rx SPA is designed and simulated, considering the issues of theoretical resolution, practical field of view (FOV) and ghost images and suggesting the guidelines on designing such a THz SPA imaging system. In order to verify the proposed THz SPA imaging system experimentally, we have investigated a scheme that the Tx and Rx elements are located on two scanning tracks separated by a distance and devised a simplified experimental set-up using only 1 Tx and 1 Rx channels. The further simulation that takes account of effects resulting from track separation and beam properties correlates to the final measurement quite well. This experimental approach provides a low-cost solution to a practical implementation of THz SPA imaging system.
Magnetic and Mechanical 3-D Modelling of a 15 T Large Aperture Dipole Magnet

In 2018, two structures have been proposed with the goal of replacing the magnet EDIPO that stopped operating in 2016. These two solutions were designed to generate a field of 15 T on an aperture of 100 × 150 mm2 at 4.2 K by using Nb3Sn superconducting Rutherford cable. After the boarding review, which took place in December 2018, a solution consisting of 6-double-layers coils was retained, based on the 2-D modelling results. We now extend the study, by considering the 3-D models, with the goal of concluding the magnet conceptual design. The target is to obtain a homogeneous field, with 1% of homogeneity, over a length of 1000 mm, which allows the testing of superconducting cables for both fusion and high-energy physics. The double-layers coils, of the block-type magnet, have flared ends to allow the aperture to cross the magnet length. The 6-coils solution differs from previous similar projects as the FRESCA2 and HD2 magnets, where 4-coils were used.
900V GaN FETs in a 300kHz 2kW LLC Converter for High Input Voltage Applications

900V GaN FETs are here. Their superior reliability allows them to handle 1000V transient voltage and 900V DC link with reduced lifetime. This paper is to demonstrate 900V 50m Ω GaN FETs in a 300 kHz, 2kW LLC converter bridging high voltage systems (500V to 900V), and low voltage systems (45V to 50V). As 900V GaN FETs can handle higher voltages, it becomes less necessary to design 3-level LLC converters with 600/650V devices. A 2-level half bridge using 900V 50m Ω GaN FETs enables high frequency LLC converters for a wide-range of high-input-voltages. And, the GaN FETs are compatible with classic gate drive circuits for Si MOSFETs. A prototype has been evaluated with input voltages ranging from 500V to 900V and output power up to 2kW.
Robust Nonlinear Optimal Control for Voltage-Frequency and Active-Reactive Power Regulation in Microgrids

This paper presents a novel nonlinear optimal and robust control strategy, for the dual operation mode for a microgrid. During the grid-connected operation mode, it uses PQ control mode, in order to inject or absorb active or reactive power to the microgrid AC common bus, while on the other hand after an isolation event, the microgrid uses the VF control mode in order to ensure the voltage and frequency regulation to the local load. The main contribution of this paper is to present an alternative control scheme, instead of the classical control methods based on nested linear controllers, with dedicated control loops for the current and voltage regulation. The advantages of the proposed control strategy are that the control algorithm is able to achieve the control objectives and at the same minimizes a given performance index, expressed in terms of the control expenditure and the tracking reference error. It is worth mentioning that the microgrid under study is modeled using the dq-frame and therefore output power of the system becomes a nonlinear function of the state, therefore it is necessary to apply nonlinear optimal and robust controllers in order to reject the parametric uncertainties of the system and deal with the microgrld nonlinear behavior.
Advanced Burn-In - An Optimized Product Stress and Test Flow for Automotive Microcontrollers

Burn-In (BI) stress to screen out early life failures caused by latent defects has become a quality requirement for automotive microcontrollers. However, as feature size continues to scale down, performing BI stress on packaged parts has started to run into challenges including increased risks of thermal runaway and overstress, together with continuously increased cost and cycle time. A more effective and efficient product stress and test flow to achieve better quality with less cost, namely, Advanced Burn-In (ABI), is presented in this paper. With enhanced wafer level stress and test, as well as enhanced Advanced Outlier Limit (AOL) algorithms, ABI enables us to eliminate package BI on majority of parts on each wafer while improving quality at the same time. It has been successfully implemented on automotive products at NXP with superb quality at PPB (defective Parts-Per-Billion) level.
Design of Novel High-Frequency 2-D Magnetic Properties Tester for Nanocrystalline Alloy

To broaden the applications of promising nanocrystalline magnetic materials in electrical engineering at high frequency, the materials must be characterized well at a wide range of frequencies and fields. This paper proposes a novel two-dimensional (2-D) magnetic properties tester up to 20 kHz. The finite element method is used to optimize the tester design by calculating the magnetic field distributions in the specimen and yokes. Considering the dimension and physical properties of the specimen, a particular B-H sensing structure is designed to measure the flux density and field intensity in the specimen. Furthermore, a frequency-domain feedback control system is integrated into the measurement system in order to control the waveform of flux density. Moreover, the paper discusses the factors which impact on the experimental results. This paper demonstrates that the novel tester is reliable to study the magnetic properties of materials. At last, the 2-D rotational magnetic properties of nanocrystalline alloy are preliminary measured.
Knowledge Transfer in Board-Level Functional Fault Identification using Domain Adaptation

High integration densities and design complexity make board-level functional fault identification extremely difficult. Machine-learning techniques can identify functional faults with high accuracy, but they require a large volume of data to achieve high prediction accuracy. This drawback limits the effectiveness of traditional machine-learning algorithms for training a model in the early stage of manufacturing, when only a limited amount of fail data and repair records are available. We propose a board-level diagnosis workflow that utilizes domain adaptation to transfer the knowledge learned from a mature board to a new board in the ramp-up phase. First, a metric is designed to evaluate the similarity between products, and based on the calculated value of the similarity, either a homogeneous or a heterogeneous domain adaptation algorithm is selected. Second, these domain adaptation algorithms utilize information from both the mature and the new boards with carefully designed domain-alignment rules and train a functional fault identification classifier. Three complex boards in volume production and one new board in the ramp-up phase are used to validate the proposed domain-adaptation approach in terms of the diagnosis accuracy.
A Flip-Chip Capable Low-Side and High-Side SOI Gate Driver with Variable Drive Strength for GaN Power FETs

A variable drive strength gate driver for gallium nitride (GaN) power FETs intended for a 6.6 kW EV charger is presented. The driver incorporates low-side and high-side channels in a single integrated circuit that is flip-chip capable. The gate driver has been designed in a 0.18 μm silicon-on-insulator (SOI) process featuring deep trench isolation, 1.8 V and 5 V CMOS devices, and DMOS devices rated up to 200 V. The on-chip protection circuitry includes an undervoltage lockout (UVLO) in both channels, desaturation detection, and a VDS sensing circuit for the low-side power FET. The novel VDS sensing implementation enables the gate driver to have a dynamically controlled source current, programmable between 0.15 A and 2.4 A, to mitigate excessive ringing. Both low and high-side channels have a sink current of 5 A. The gate driver IC also includes active Miller clamping, a low-side to high-side level shifter, and a power supply for each channel.
Aerial Geo-Localisation for MAVs using PoseNet

The Global Position System (GPS) has become an essential sensor for drones. Autonomous flight in outdoor areas is possible thanks to the use of GPS that enables the drone to obtain its position in latitude and longitude coordinates. However, GPS may become unreliable when the drone flies in environments where the signal may get occluded. Malicious attacks may also compromise the GPS signal, aiming at blocking the signal or replacing it with spurious data. Motivated by these scenarios, we present preliminary results of a methodology aimed at estimating the GPS position of a drone using Convolutional Neural Networks (CNN) and a learning-based strategy. For the latter, we have adopted the PoseNet CNN architecture, originally proposed to address the relocalisation or kidnapping camera problem for facing forward cameras. First we trained PoseNet with a set of aerial images captured with an on-board camera, providing X, Y and Z coordinates as labels, which are obtained from converting GPS coordinates into metres for X and Y, and using the altimeter for Z. Then we perform validation flights where the vehicle follows a different trajectory to that used for collecting the training datasets. Even when the terrain includes bushes and repetitive texture, the CNN returns predictions with an error around the 2.5 metres and a processing speed of 15 milliseconds on average. We argue that a system such as this could be used as an emergency option to return the drone to home in the event of GPS failure. To our knowledge, this is the first time PoseNet is tested to address the problem of geo-localisation of aerial images.