Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Characterization and Management of GaN-on-SiC High Power Amplifier MMIC

Driven by the industry trends in radar applications, Gallium nitride (GaN) power amplifier (PA) has developed quite fast, targeting high performance needs, which in the meantime has posed critical challenge for device thermal management. Thermal design was started during the transistors and chip design. There are three stage transistors in the designed GaN PA chip. Thermal simulation correlated with IR camera experiment has been conducted for design, analysis and characterization of GaN-on-SiC Monolithic microwave integrated circuit (MMIC). Peak channel temperature and thermal resistance for DC and pulsed RF input operation have been investigated. The junction-to-case thermal resistance of the GaN chip under DC operation is 1.63°C/W, white thermal resistance is 1.05°C/W under pulsed operation. The validated model and results were used to guide the MMIC design, function test and reliability evaluation.
An Assurance Case Driven Development Paradigm for Autonomous Vehicles: An F1TENTH Racing Car Case Study

Autonomous driving has drawn great interest from both industry and academia. Due to some serious consequences such as loss of life caused by autonomous vehicles, assurance certification has been proposed in the automotive industry to ensure safe self-adaptive behaviors at run-time in autonomous cars. Central to assurance certification are assurance cases that provide compelling, comprehensive, and valid argument structures showing a system is safe in a given environment. However, many existing approaches generate assurance cases as a by-product of a system. In this paper, we will present a novel development paradigm that employs assurance cases to guide an autonomous vehicle to operate correctly and safely at run-time. Specifically, we consider an F1TENTH racing car as an example to illustrate how the assurance case driven paradigm can guide the vehicle to achieve safe and reliable self-adaptive behavior at run-time.
Simple and Comprehensive Strategy to Synthesize Huygens Metasurface Antenna and Verification

Huygens metasurface (HMS) provides a flexible way to tailor the phases of fields without incurring reflection. Recently, HMS has been used in antenna scenes to convert the incident field into directional radiation. However, most design processes of the HMS antennas are based on the generalized sheet transition condition, which is usually augmented with extra operations for ensuring passivity and losslessness. Here, we propose a simple and comprehensive strategy to design and optimize the HMS antenna. The strategy builds an equivalent model of the HMS antenna and deals with the designs from the perspective of phase optimization, which is simple and straightforward. A fast and efficient algorithm is presented to solve the optimization problem under the framework of alternating direction method of multipliers (ADMM). The phases obtained by this strategy are naturally compatible with the passive and lossless conditions and can be directly implemented by metasurfaces. Furthermore, the strategy imposes constraints on the phase difference of adjacent meta-atoms, thus reducing the performance deterioration caused by aperiodic boundary conditions. The feasibility and superiority of the strategy are verified by full-wave simulation results and experimental measurements of exemplary designs.
Research on Force Visual Perception of Bevel-Tip Needle Based on BP Neural Network

Needle insertion is a minimally invasive treatment technique. During the surgery, the insertion path needs to be planned in advance to manipulate the flexible needle to avoid nerves and organs. In order to predict the insertion trajectory, a force-visual perception prediction model based on the BP neural network is established. Through the force analysis of the flexible needle, the displacement L of the needle holder and the reaction force Fr, insertion force F and torque M on the needle holder are used as the force visual perception model. Input to predict the trajectory of the needle tip during insertion. Through experiments on three different types of flexible needles, data are collected to train the model. The lowest mean absolute error (MAE) of the model is 0.7490, the correlation coefficient R is between 0.99962 and 0.99996, and the accuracy is high. The force visual perception model provides a feasible prediction of the needle tip trajectory. The results show that the displacement of the needle tip in the X and Y directions predicted by the model is basically consistent with the experimental results, and the insertion trajectory can be predicted more accurately.
A Magnetic Switch Sensor Based Inductive Power Transfer System With Power Control and Efficiency Maximization for Vehicular Applications

In order to establish an efficient inductive power transfer (IPT) mechanism for electric vehicles (EVs) it is necessary that a system with effective power control and efficiency maximization is established. As the equivalent resistance of the on-board battery charger continuously fluctuates during operation, a battery charging algorithm based on an improvised continuous current (CC)–constant voltage (CV) is proposed. This article introduces the design of an integrated stationary IPT system to inductively transfer power from a transmitter pad positioned on the ground and the receiver pad embedded under the chassis of an EV. An innovative feature of the design is the implementation of a magnetic switch sensor that is incorporated into both the transmitting and receiving wireless charging circuitry to ensure optimum alignment for IPT. The power electronics design focuses on the implementation of an H-bridge converter incorporating series–series (SS) compensation topology to use an innovative control algorithm to prioritize battery charging operations. The system is validated through a simulation model in PSIM and a hardware-in-the-loop (HIL) simulation in Typhoon before hardware implementation and testing of the developed prototype. At a test resonant frequency of 23.74 kHz and a nominal air gap separation of 120 mm, the developed IPT system had an overall efficiency of 93.41%.
Motor Imagery based Brain Computer Interface Paradigm for Upper Limb Stroke Rehabilitation

Motor Imagery (MI) based Brain Computer Interface (BCI) systems have shown potential to serve as a tool for neurorehabilitation for post stroke patients to complement the standard therapy. The aim of this study was to develop an MI based BCI system that could potentially be used in neurorehabilitation of hand motor function in stroke patients. Two co-adaptive, three-class MI based BCI systems for realtime processing were developed and compared using the publicly available data from the BCI Competition III Dataset V as well as our own data. The first algorithm utilizes the Filterbank Common Spatial Pattern (FBCSP) for feature extraction, and the other utilizes the Separable Common Spatio-Spectral Pattern (SCSSP) – both combined with a Multi-layer Perceptron (MLP) for classification. The proposed system proved successful when using the competition data showing an average accuracy of 64.71 % for the SCSSP compared to 60.48% for the FBCSP. This proved superior to a related study using the same feature extraction methods, but with other classification methods. The proposed system, however did show results around chance level for the 3-class MI experimental data that we have collected in our laboratory. Further studies needs to be conducted to improve the performance as well as to realize such a system to put in use.
Exergy evaluation of a trigeneration plant applied to industry sector: an Italian case study

Nowadays, technology development in the direction of more sustainable energy is imperative. Energy cogeneration and the use of renewable energy sources provide key solutions for reaching these objectives. In situations when seasonal changes in heat demand limit the operation of a full cogeneration system, combined cold-heat-and-power can be employed. To evaluate such energy systems, exergy analysis can be helpful because it can assess the variation in the quality of energy carriers. Among the sectors where exergy assessment is widely applied is the building sector, wherein renewable sources are also used. Some works adopted this kind of exergetic approach also to industries equipped with cogeneration plant. However, few works investigated the opportunity to carry on exergy analysis of a trigeneration system of an industry. Therefore, this paper can give a relevant contribution to this field. The case studies are two Italian industries that, given their physical proximity, want to be an energy community by sharing an internal combustion engine in a trigeneration arrangement. To evaluate the trigeneration systems proposed, exergy analysis was carried out using the Matlab tool. Results underlined that the trigeneration system developed is convenient from the exergy point of view.
Tropical low formation and intensification over land as seen in ECMWF analyses

Case studies of the formation and intensification over land of three tropical lows in northern Australia are described. The case studies are based on European Centre for Medium‐Range Weather Forecasts (ECMWF) analyses. The aim is to investigate the generality of recent results concerning the dynamics and thermodynamics of tropical lows. Consistent with these results, it is found that the processes of low formation and intensification are the same over land as over the ocean. An important element of the intensification process is the need for bursts of deep convection to persist near the circulation centre, which, in turn, requires that convective instability be maintained by surface moisture fluxes. The moist monsoonal environment locally surrounding the storm provides a shield against the adverse effects of dry‐air intrusion from the Australian continent.
Rainfall Prediction Using Deep Learning and Machine Learning Techniques

Predicting rainfall is a crucial component of contemporary weather forecasting, with consequences for agriculture, hydrology, water management, and disaster preparedness and response. The conventional method of predicting rainfall has depended on historical information and meteorological observations. Yet, more precise and timely predictions of rainfall patterns are now possible thanks to the development of sophisticated computer models, remote sensing technologies, machine learning, and deep learning algorithms. An overview of the methods for predicting rainfall is given in this work, including the analysis of data from satellite imaging, atmospheric conditions, ocean temperatures, and other climate variables. We also look at how machine learning and deep learning algorithms are evolving to build more sophisticated models that can evaluate vast quantities of data and predict rainfall patterns with greater accuracy.
Learning Self-Consistency for DeepFake Detection

Due to the space limit, we mainly reported the performance of our models in term of AUC in the main text. Here we provide more results in terms of AUC, AP, and EER for Table 4 and 5 in the main text. Note that these results are at video-level, computed by averaging the classification scores of all video frames. The experiments are conducted under the cross-dataset setting, in which we train our model only with real data from the raw version of FF++ [15] and the fake/positive samples are generated by I2G (more detailed are included in Sec. 4.2 in the main text).
A Fox–Milnor theorem for knots in a thickened surface

A knot in a thickened surface [Formula: see text] is a smooth embedding [Formula: see text], where [Formula: see text] is a closed, connected, orientable surface. There is a bijective correspondence between knots in [Formula: see text] and knots in [Formula: see text], so one can view the study of knots in thickened surfaces as an extension of classical knot theory. An immediate question is if other classical definitions, concepts, and results extend or generalize to the study of knots in a thickened surface. One such famous result is the Fox–Milnor Theorem, which relates the Alexander polynomials of concordant knots. We prove a Fox–Milnor Theorem for concordant knots in a thickened surface by using Milnor torsion.
Fonctions $k$-lipschitziennes sur un anneau local et polynômes à valeurs entières

© Bulletin de la S. M. F. , 1973, tous droits réservés. L’accès aux archives de la revue « Bulletin de la S. M. F. » (http: //smf.emath.fr/Publications/Bulletin/Presentation.html) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/ conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="detti-ieq1-3236447.gif"/></alternatives></inline-formula>Bench: An Open-Source Factory of Benchmark Microservice Applications

<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="detti-ieq2-3236447.gif"/></alternatives></inline-formula>Bench is an open-source tool for benchmarking cloud/edge computing platforms that run microservice applications. The tool creates dummy microservice applications that can be customized and executed on a Kubernetes cluster. <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="detti-ieq3-3236447.gif"/></alternatives></inline-formula>Bench allows users to control fundamental properties of the microservice applications it creates, such as service mesh topology, microservices’ behaviors using a portfolio of stress functions (e.g., for CPU, memory, I/O, network) or implementing new ones, microservice-to-microservice API (HTTP or gRPC), etc. Application performance can be evaluated by stochastic or trace-driven workloads. μBench is aimed at researchers and cloud platform developers who lack real microservice applications to benchmark their findings (e.g., new resource control mechanisms, artificial intelligence-driven orchestration, etc.) or wish to thoroughly evaluate their proposals versus a broad set of heterogeneous applications that μBench can create. In addition to the description of μBench, in this article, we show one possible use of it. We compared advantages and disadvantages of microservice architectures versus monolithic ones, and analyzed the performance impact of key architectural choices, such as service mesh topology and the use of replication. For this analysis, we generated several microservice applications with different properties, and two of them are derived from a real cloud dataset.
Chaos, Solitons and Fractals

The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the ﬁrst notiﬁcation until nowadays. Developing eﬃcient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the pub- lic health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR)
The Full-IMCs Interconnects through Transient Liquid Phase Bonding of Ga/Cu System for Advanced Electronic Packaging

To meet the needs for further miniaturization and multi-functionalization of microelectronics, three-dimensional (3D) integrated circuits and advanced interconnects have become the most promising packaging technology in the semiconductor industry. 3D integrated circuit (ICs) packaging is based on 2D processes and stacks of ICs vertically to achieve the dual role of space compression and improving data transmission efficiency. As a relatively new bonding method, the low temperature transient liquid phase bonding (TLPB) technique shows a great potential for multiple bonding on the same substrate. This study uses gallium (Ga) metal as the interconnect material to achieve the Cu-to-Cu TLPB process. The aim is to investigate the Ga/Cu IMCs grains growth kinetics, the microstructure of IMCs in these joints and their mechanical properties bonded at 200°C. The results show that the Ga solder presents excellent bondability on Cu substrate, forming cube-like CuGa2 grains which can grow stably at 200°C, and this anisotropy of IMC grains growth is beneficial to the microstructure and strength of the joint. These results provide strong evidence for the feasibility of such a novel manufacturing route for 3D IC packaging and heterogeneous integration.
Imaging with Coherent Synchrotron Radiation: X-ray Imaging and Coherence Beamline (I13) at Diamond Light Source

Synchrotron radiation newS, Vol. 30, No. 5, 2017 19 Introduction Synchrotron radiation provides a number of outstanding features that are highly valuable for X-ray imaging and tomography. One of them is the partial coherence of the radiation, which is explored at the X-ray Imaging and Coherence beamline (I13) at Diamond Light Source. The variety of methods employed at the beamline offer imaging with the highest quality. We discuss the benefi t of imaging with coherent radiation and describe methods and applications available at Diamond’s I13 beamline.
On Higher-performance Sparse Tensor Transposition

Sparse tensor algebra has been an important tool in scientific research. However, there is limited study on sparse tensor transposition on its performance. To the best of our knowledge, sparse tensor transposition has been a critical performance bottleneck in sparse tensor algebra computations. In this work, we characterize the performance of sparse tensor transposition in a chain of distinct sparse tensor algebra operations and study sparse tensor transposition with, without sorting, and with various sort algorithms. We find that sparse tensor transposition with count sort performs best among other options. However, with the best performant count sort variant, sparse tensor transposition still becomes the performance bottleneck in tensor computations with mixed tensor operation types, such as Sparse-Tensor-Times-Vector (SpTTV). To resolve sparse tensor transpose as a bottleneck, we further investigate possible sparse tensor algorithm designs and propose an efficient new sparse tensor algorithm by integrating bucket sort and count sort. The evaluation results show that our algorithm is faster than the state-of-the-art (SOTA) approach, with 1.2x speedup on average and up to 2.3x speedup with multithreading. There is also potential to further improve the performance of sparse tensor transpose by selectively enabling the SOTA approach or ours on particular dimensions of coordinates with respect to the coordinate distribution
On the Impact of Packet Reordering in MPT-GRE Multipath Networks

Several factors, such as network delays, congestion, differing path lengths, and varied processing durations at intermediary nodes, can cause out-of-order arrival of the packets, negatively affecting network performance, decreasing throughput, and increasing latency. When packets arrive out of order, the receiver may need to wait for missing packets before assembling a complete message, resulting in additional delays. Furthermore, some applications, like music and video streaming, require continuous and ordered data flow. Any out-of-order arrival of packets can result in jitter and decreased quality. To mitigate the impact of this issue in MPT-GRE multipath networks, MPT contains a packet reordering mechanism. In this paper, we designed a network topology that generated delays and limited the transmission speed on one of the paths. We investigated how the out-of-order arrival of packets influences the throughput aggregation capability of the MPT library with and without enabling the packet reordering feature of MPT. Our network throughput measurements show that by enabling MPT's packet reordering mechanism, MPT can efficiently aggregate the throughput of both symmetric and asymmetric channels.
A comparison study between three different Kernel estimators for the Hazard rate function

Kernel estimation is one of the most important data analytical tool, if we consider the non parametric approach in the estimation of the probability density function. In parallel, its used to estimate the hazard rate function, which is one of the most important ways for representing the life time distribution in the survival analysis. As the support of the hazard rate function is in the non negative part of the real line , its will be under the boundary effect near zero when the estimation is done using symmetric kernels such as the Gaussian kernel. Two kernel estimators for the hazard rate function were proposed using asymmetric kernels are the Reciprocal Inverse Gaussian and Inverse Gaussian kernel estimators to avoid the high bias near zero. In this paper, we propose a theoretical comparison between those estimators by looking at their asymptotic bias, variance and the mean squared error. Also, a comparison of the practical performance of the two estimators based on simulated and real data will be present.
An Optimized 1.47 GHz Rectifier for Wirelessly Powering Ultra-Low Power Implantable Medical Devices

This paper studies modifications to the PMOS array in a differential threshold voltage cancellation rectifier to increase efficiency. It successfully designs a new high-efficiency rectifier circuit operating at 1.47 GHz and achieving voltage conversion efficiency and power conversion efficiency of 89.4054% and 53.3475%, respectively. By using 90nm technology, the novel rectifier is able to convert an AC signal to a DC signal in an efficient and consistent manner for an ultra-low power implantable medical device. Furthermore, due to its on-chip integrability, the rectifier circuit allows for easy integration into the receiver chip of implantable medical devices, allowing for greater flexibility in device design and placement.
Fuse-based protection system in a DC-microgrid for fast-charging applications: sizing and implications

DC microgrids are more and more used for specific application where loads to be supplied are in DC. This shift requires to perform ad hoc modelling regarding electrical aspects, particularly in terms of protection system. This is a critical subject in DC distribution, especially if the microgrid is intended to supply high-demanding load (for instance electric vehicle in fast-charging) at a high voltage level. The case study presented in this paper is a DC-microgrid - the eStation developed by Free2move eSolutions - for fast-charging applications. The proposed paper deals with the sizing process and the implications of a fuse-based protection system in a DC microgrid whose Battery Energy Storage System (BESS) is directly connected to the DC bus, without a converter interfacing them.
Bottom Side Cooling for Glass Interposer with Chip Embedding using Double-sided Release Process for 6G Wireless Applications

This paper presents a die-embedded glass interposer process using a new double-sided release method. This new method is introduced for CTE balancing and minimum warpage process for 6G wireless applications. In the glass interposer, through cavities are prepared in the glass substrate. Thermal test chip (TTC) is embedded into the through cavity and connected via redistribution layers (RDL) in the topside. A copper heat spreader is attached to the exposed TTC backside using thermal-interface material (TIM) for thermal management. The double-sided release method using two different carriers during the heat spreader attachment is the key to balancing the CTE and minimum warpage. The embedded TTC power density is tested to see the cooling capacity ranging up to ~1W/mm2 with various heat transfer coefficients ranging between 28.8~261.3 W/m2K. This work provides a solution for CTE mismatch during die embedding glass interposer process and provide minimum warpage.