Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Downsized LASDRA with 2-DoF Joint Locking Device

Aerial manipulation with a multi-rotor drone and manipulator systems or omni -directional drones has critical barriers such as limited flight time, insufficient payload, and inaccurate onboard sensing and control. To overcome these limitations, the Large-size Aerial Skeleton with Distributed Rotor Actuation (LASDRA) was proposed in previous research [1]. In this paper, we develop a downsized LASDRA with a novel joint locking device. The developed downsized LASDRA with joint locking device can work in narrower spaces with more DoF and generates a bigger operational force by joint locking. The joint locking device employs a capstan brake and latch mechanism to generate large locking torques with a small form factor. As an experimental result, the payload of a 3-link downsized LASDRA increased from 0.4 kg to 1.2 kg with joint locking, while the length of each link decreased from 1 m to 0.76 m.
Enhancement of Silane Coupling Agents on The Underfill Adhesion Undergoing Hydrothermal Aging

In this study, three kinds of underfill materials were used to investigate the enhancement of silane coupling agents on the adhesive strength of the underfill and the silicon substrate undergoing boiling water soaking. Adhesion properties between the underfill and the silicon were evaluated by shearing test. The fracture interfaces of the shearing tests are observed by SEM. The results show that the adhesive strength between the silicon and the underfill decreases significantly without silanes, and the bonding between the resin and the filler becomes loose by hydrothermal aging. Underfill containing aminosilane has higher water resistance, which can improve the adhesive strength of underfill, and can help to strengthen the bonding between underfill and filler after hydrothermal aging.
Assessment of Power Electronics Converters for HVDC series tapping station

This article assesses the power electronic converters for HVDC tapping stations. Firstly, it compares parallel and series tapping stations to show the potential benefits of the last ones. Then, an original series converter based on a Current Source Alternate Arm Converter (CSAAC) is compared to a common Voltage Source Modular Multilevel Converter (VSMMC). It shows that the former requires little industrial effort whereas the latter, less mature, outperforms its predecessors in many criteria.
Emulating artificial mechanoreceptor functionalities from SiO2-based memristor and PDMS stretchable sensor for artificial skin applications

The development of robust artificial sensory systems that will be able to detect, memorize and respond to external stimuli is attracting a considerable amount of interest over the last years towards emulating artificial skin properties. Skin is the largest sensory system of the human body that carries out complex procedures by employing a variety of biological receptors. However, it is quite difficult to acquire stretchable artificial skin functionalities by combining the present tactile sensors. Along these lines, we report here a sensory system that has the ability to detect various strain loads with excellent sensitivity and at the same time respond and store the applied stimuli. In order to attain this goal, we used a bipolar switching memory device made on poly-ethylene naphthalate (PEN) substrate and Pt nanoparticles (NPs)-based sensor on poly-dimethylsiloxane (PDMS). The memristor operates only under strain conditions and retains the induced resistance changes even when the applied stain gauge is removed with <20% resistance attenuation. The sensing element exhibits a colossal gauge factor (GF) of 109, which is utilized in order to produce sharp and distinct responses. Moreover, the memristive element exhibits also a quite large memory window (~105) which can be leveraged in order to assimilate various external stimuli. Additionally, paired-pulse facilitation (PPF) and long-term plasticity (LTP) effects are demonstrated indicating the potential perspective of our approach to performing neuromorphic computations with low power consumption.
SpecMCTS: Accelerating Monte Carlo Tree Search Using Speculative Tree Traversal

Monte Carlo Tree Search (MCTS) algorithms show outstanding strengths in decision-making problems such as the game of Go. However, MCTS requires significant computing loads to evaluate many nodes in the decision tree to make a good decision. Parallelizing MCTS node evaluations is challenging because MCTS is a sequential process that each round of tree traversal depends on the previous node evaluations. In this work, we present SpecMCTS, a new approach for accelerating MCTS by speculatively traversing the search tree. Many MCTS applications, such as AlphaGo Zero, use a deep neural network (DNN) model to evaluate the tree nodes during the search. SpecMCTS uses a pair of DNN models, the speculation model and the main model. The faster (but less accurate) speculation model accelerates the sequential tree search while the more accurate main model improves the decision quality. SpecMCTS accelerates MCTS for the game of Go by up to $2.09\times {}$ on the NVIDIA T4 GPU. This performance improvement can be translated into a better decision quality by performing a larger number of tree traversals within the time limit. For a fixed decision time, SpecMCTS shows stronger gameplay (higher win rate) than the original sequential MCTS and state-of-the-art MCTS parallelization approaches.
A 38.5-to-60.5 GHz LNA with Wideband Combiner Supporting Cartesian Beamforming Architecture

Current millimetre-wave (mmW) 5G NR standard supports multiple bands at 24.5/28/37/39/43/47GHz for communications. To cover several bands of the 5G NR and reaching lower end of unlicensed 60GHz band for 802.11ad, this work presents a wideband phased array front-end with LNA and two VGAs for scalar-only weighting function, and a wideband combining network of each signal weight in mmW domain for beamforming. In this work, two array elements are combined in two cascaded stages for extremely wideband operation. Combined load resonances are distributed and adjusted appropriately in each of the combining stages to achieve a flat response over the band of 38.5-60.5GHz. A single array path achieves rms gain of 8.5-12.5dB, noise figure of 6.2-8.1dB, and IP1dB of -33 to - 26dBm. The measurements show ≈ 6dB of array gain when the two phased array elements are combined in phase with +0.6dB to -0.4dB maximum gain error in the mmW VGAs. The prototype is implemented using 28nm CMOS.
Reliability Analysis of Thermal Interface Materials (TIMs) in large size FCBGA package

In large size package, high temperature affects the stability, reliability and service life of electronic components. The thermal interface material (TIM) plays an important role of effectively enhancing the heat transfer in Flip Chip Ball Grid Array (FCBGA) package; therefore reliability of TIM is indispensable especially when the package device needs to withstand wide high and low temperature periods. In this paper, we conduct a detailed analysis of TIM's reliability in large-size FCBGA package by simulating the stress and strain of TIM layer with the finite element software ABAQUS. The stress of TIM in the package is complex and can be affected by multiple factors such as device structure, material parameters, etc. It is necessary to check the model structure and material parameters to correctly understand the stress of TIM in FCBGA. The statistics method is adopted for analyzing the influence of various parameters.
Single-stage isolated and bidirectional AC-DC converter suitable for ancillary services supply

This work introduces a single-stage, bidirectional, isolated AC-DC converter with capability to supply ancillary services to grid. The converter is based on a simplification of the two-stage configuration where the power is transferred to grid by means of the low frequency voltage components of a voltage source inverter (VSI) meanwhile high frequency components are filtrated. In the proposed converter, high frequency components of the voltage are exploited for isolated power transfer to the DC by means of high Q resonant tanks and an additional phase-shift modulated active bridge. The proposed converter operates under similar principles to the two-stage configuration but with lower component count. This way, DC link capacitor is still present and designed to provide virtual inertia to the grid. The virtual inertia is achieved by an additional outer control loop that links frequency variations to the DC voltage control reference. Thus, the proposed converter is able to perform isolated and bidirectional AC-DC conversion and to provide the ancillary service of virtual inertia to the grid. The reliability of the system is demonstrated by means of simulation analysis of a 2.4 kW system.
2-D Optical-CDMA Modulation With Hard-Limiting for Automotive Time-of-Flight LiDAR

In this proof-of-principle paper, the application of 2-D optical code-division multiple-access (OCDMA) modulation to long-range automotive time-of-flight (ToF) light detection and ranging (LiDAR) is studied. The regulations and physical constraints that govern the design parameters are reviewed. Using 2-D carrier-hopping prime codes (CHPCs), the modulation model and a novel 2-D hard-limiting decoder are designed and validated with OptiSystem$^{\rm {TM}}$ simulations. Based on the design parameters, the 2-D CHPCs have six times as many distinct sequences (for sensor identification) as 1-D code sequences. Analytical and simulation studies show that the proposed 2-D OCDMA modulation model can eliminate the near-far (power) problem and support more LiDAR sensors with distinctive ToF tags, greater interference robustness for more simultaneous ToF measurements, and better performance than the 1-D counterparts. The simulation results show that the 2-D model can support four times as many simultaneous emitting sensors without false detections as the 1-D model. In summary, the 2-D OCDMA modulation has more benefits and is more cost efficient overall, even though it is more complex.
AI-Aided Channel Quality Assessment for Bluetooth Adaptive Frequency Hopping

In this work, we propose an artificial intelligence (AI) based channel quality assessment algorithm for Bluetooth adaptive frequency hopping (AFH) to avoid interference between heterogeneous systems coexist in 2.4GHz Industrial, Scientific, and Medical (ISM) band. The proposed network takes the received signal strength indicator (RSSI) of all Bluetooth channels as input and outputs the estimated channel quality, which is further used to update AFH channel map. A gated recurrent unit (GRU) is adopted to extract the temporal information of interference on each channel. A novel loss function combining classification loss and ranking loss is designed to improve the performance of the neural network. The complexity analysis shows that the network considered is lightweight and resource-friendly. Moreover, simulation experiments under different interference models show that the proposed method outperforms several existing channel selection schemes.
Machine Learning based Prediction of Wire Bonding Profile in 3D stacked integrated microelectronic packaging

Wire bonding (WB) have been widely used in 3D stacked integrated microelectronic packaging because of its high reliability and low-cost. In 3D stacked packaging, the required size for microelectronic devices was much smaller than before, which led the shape of wire profile becoming more critical as it directly affects the total thickness of the chip package. However, there is no facile means to predict the wire profile. In this study, machine learning (ML) was proposed to predict the wire profile of wire bonding under various conditions. After collecting abundant relevant information using finite element simulation experiments, the support vector regression (SVR) was selected as the predictor while support vector machine (SVM) was used as the classifier to the train the ML model, thereby, the wire bonding profile that can meet the requirements of microelectronic industry can be obtained. This study can provide useful insights for developing wire bonding processing techniques.
A Novel Hybrid Aerial-Ground Manipulator for Pipeline Inspection tasks

Autonomous inspection of industrial pipelines in Oil & Gas refiners represents an emerging research and application field. In this context, we present a novel hybrid aerial ground mobile platform for inspection tasks. The goal of this platform is to assist human operators to perform non-destructive tests to assess the quality status of pipelines carrying fluids. Thanks to its design, the proposed platform is able to navigate crowded environments to reach and land over industrial pipes and use its motion capabilities to inspect the pipe surface. In this work, we introduce the mechanical design of the hybrid platform discussing its main components, demonstrating its flying and inspection capabilities.
Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection

With the emergence of increasingly growing network threats, network security becomes a major issue which causes huge existing and potential losses, such as WannaCry. Various methods had been adopted to maintain network security, among which Intrusion Detection System (IDS) is one of the most essential parts of cybersecurity to defense against sophisticated and ever-growing network attacks. A number of researchers have studied comprehensive datasets and effective approaches to build IDS. Machine learning models are also applied in IDS and obtained considerable results in building better network security system. In this paper, we proposed a two-stage IDS based on machine learning models RF and SVM optimized with Feature Selection algorithm CFS. We also conducted experiments on NSL-KDD benchmark datasets to evaluate the performance of the two-stage IDS, comparing against RF and SVM models respectively. The results demonstrated that our proposed two-stage IDS outperformed RF and SVM with an increase from 4.31% to 14.62% in Precision and a reduction of 93.84% in time than SVM.
Viscoelastic Characterization and Simulation of Thermal Interface Materials

The thermal interface material (TIM) located above the chip in a package is a key factor in determining the heat dissipation of a chip. The deformation or warpage of the package significantly affect the performance of the TIM, which induces stress that causes failures such as cracking and interface delamination. Therefore, a detailed analysis of the stress state of the TIM in the package is essential for the thermal management of the chip. Most polymer materials used in package are viscoelastic, showing obvious correlation with temperature and time. However, in most finite element analysis (FEA) the polymer materials are usually treated as linear elastic, which may lead to the deviation of simulation from the actual situation. In this paper, we use inverse analysis method to obtain the viscoelastic constitutive parameters of TIM based on the Prony series derived from the experimental stress relaxation test. The viscoelastic constitutive of the TIM is applied to FEA for studying the cooling process in a flip-chip package. We find that the viscoelastic TIM may absorb more stress than the elastic material does, leading to less deformation from the bottom side of package transferring to the upper lid due to stress relaxation. In addition, the stress and strain distribution in the plane of viscoelastic TIM also shows characteristics different from that derived using linear properties. This work will help to improve the understanding of the actual mechanical behavior of TIM during the deformation of a package.
Using Extra Weight in Machine Learning Algorithms for Clickbait Detection of Indonesia Online News Headlines

Nowadays, large numbers of Indonesian people access news using online platforms. The large population makes them the target of clickbait news for publishers to gain ad revenue from clickbait. For languages such as Indonesian, there is still a lack of analysis of clickbait tasks [1]. This paper proposes machine learning with extra weight to detect clickbait from Indonesia Online News Headlines. We used 400 out of the 15,000 documents of local online news headlines randomly. The small dataset is used for better understanding and faster calculation. In pre-processing, documents of labeled clickbait are compared with documents of labeled non-clickbait. The result shows few characters which only appear in the specific label. These few characters become extra weight for the TF-IDF weighting term. 10-Fold cross-validation is used for data validation. In the classification process, we use four different algorithms (SVM, K-NN, NB, and Decision Tree). Finally, we conclude that using extra weight gives a maximum of 6% better performance results rather than without extra weight.
A Systematic Mapping Study on Edge Computing Approaches for Maritime Applications

Background: The edge computing paradigm allows to reduce latency and response time of applications by bringing computations and data storage closer to the locations where they are needed. Edge computing is used in different kinds of Internet of Things (IoT) applications. Maritime represents an important application domain for IoT applications and edge computing solutions. Modern vessels employ many different types of sensors, which produce a massive amount of data. Edge computing allows to perform computations and data analyses on-board a vessel or at the edge of the network. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on edge computing approaches for maritime applications. Method: A Systematic Mapping Study (SMS) of the existing edge computing approaches for maritime applications. Results: A taxonomy of 17 papers on edge computing approaches for maritime applications. Conclusion: The results of the study show that there is a small number of existing edge computing approaches for maritime applications. Most of the existing approaches focus mainly on monitoring and communication functions in vessels. Moreover, several research gaps exist with respect to the types of edge computing approaches, the purposes of using edge computing on vessels, and the data analysis techniques used for edge computing on vessels.
Design and Manufacture of the Wing Folding Mechanism for a Bioinspired Ornithopter

This paper presents a folding mechanism for ornithopter's wings. The mechanism has been implemented using rods and joints to replicate wing performance of animal flight. In this sense, bio-inspiration has been the baseline of the design but hard requirements as lightweight and integration with the current platform have also been considered. The final specifications of volume ratio folded/unfolded of 1/3 and additional mass < 100 g/wing with respect to the current structure, make this concept quite promising. Moreover, unlike most of the existent creations, it is intended to allow control of the folding while perching. Bench experiments demonstrate its performance and compatibility with the prototype platform.
Reduced switch count Asymmetric 13-Level Voltage Source Inverter

A novel asymmetrical 13-Level inverter topology is proposed for high-power AC motor drives requiring a medium voltage inverter. The proposed topology, which is composed of two T-Type inverters connected through additional power switches, can generate a high fundamental frequency output voltage using a low switching frequency, thus achieving high efficiency and high-power quality, but with complexity and costs comparable or better than those of equivalent basic multilevel inverter topologies.
Interplay between Free Surface and Solid Interface Nucleation on Two-Step Crystallization of Poly(ethylene terephthalate) Thin Films Studied by Fast Scanning Calorimetry

Under confinement, unique features of crystallization arise, among which the slowing down and accelerating crystallization on cooling are both reported. The controversy can be resolved by performing isothermal crystallization experiments, taking into account temperature dependence of crystal nucleation and growth. The isothermal experiments of thin films were performed for the first time in this work in the wide range of thicknesses and wide range of temperatures, where it crystallizes. Fast scanning calorimetry (FSC, Flash DSC 1) was used to reach fast cooling and heating, avoiding crystallization and unwanted reorganization. Thin films of poly(ethylene terephthalate) (PET) were studied from 23 nm thickness with free surface and covered by a PS layer. Two-step crystallization was observed by FSC in films thinner than 60 nm at temperatures of crystallization below 140 °C. Evidence of strong free-surface-induced precrystallization was confirmed by atomic force microscopy (AFM). It was observed that highly .
Evaluation of energy flow data in emobility applications to create efficiency maps for simulation setup and system optimisation

As part of a research project at Fulda University of Applied Sciences (Germany), the energy efficiency of battery-electric commercial vehicles in real-world operation is being analysed. For this purpose, measurement data was collected from the driving operation of a battery-electric truck. The paper explains how measured data can be used to optimize the simulation of battery-electric commercial vehicles. The focus is on the generation and application of efficiency maps in order to more realistically represent the efficiencies of electrical components within the vehicle depending on relevant influencing parameters. The generated efficiency maps can also be used to estimate the energy flows of the subcomponents and their effect on the overall energy demand and on the range of the electric vehicle.
Multi-Parameter Identification by Exploiting Module Parasitics With Only One Sensor

In this study, a low cost current sensing approach based on module parasitics (inductances and resistances) is examined. It was found that the best results are achieved by using an active low pass filter as sensor element for the typical inductive-resistive measurement sections. This procedure, however, bears the drawback that the resulting sensor signal is highly dependent on other influencing parameters, especially module temperature and load current transient in the to-be-measured current pulse. To compensate these effects, this paper examines the origin of the sensor signal in detail and shows interesting results: It could be found that the information for current and the influencing parameters is embedded in the sensor signal waveform. This circumstance can be exploited by special sensor low pass time constants and an adapted sampling scheme of this signal. The samples can be processed to analyse the influencing parameters as well as the module current. In the following, considerations are given for different processing approaches including the application of an artificial neural network (ANN). This method is also tested in a close to reality simulative situation to proof the concept. It could be shown that the mathematical designed approach works and gives the first opportunity for a module parasitic current sensing approach without disturbance by temperature.
Advancements in indirect Time of Flight image sensors in front side illuminated CMOS

We will present major advances in Time of Flight (ToF) image sensors for consumer applications. Several innovative elements integrated into a 130nm front side illumination CMOS node reveal superior sensor performance so far only attributed to much more complex backside illumination technologies. The gate controlled pixels comprising deep trenches, buried mirrors and integrated prisms, reveal high quantum efficiency and modulation transfer function (MTF) close to the physical pixel size. In-pixel common mode suppression prevents saturation even under backlight conditions. The comprehensive System on Chip integrates high speed ADCs, a flexible phase shifter, as well as current monitoring for laser safety.