Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Gold Wire Sweep in Cantilever Chip-Stacked Package during Molding Process

With the continuous improvement on the function of electronics, the IC integration density is gradually increased. Meanwhile, the stacked chip packaging technology appeared to meet the requirements of chip integration. Among them, the cantilever stacked chip structure not only increases the package density, but also satisfies the need of vertically integrated chips with the same size. However, the gold wire sweep induced during the transfer molding process is still one of the critical issues for the reliability of the cantilever stack structure. Therefore, it is of great significance to explore the wire offset in the process of transfer molding to enhance the reliability of stacked chip packaging. In this paper, a mold-flow analysis model of cantilevered chip-stacked structure is setup. The viscoelastic properties were measured by using a DMA analyzer. For studying the deviation of wire loop in transfer molding, the process parameters including the molding temperature, filling time and melt temperature are analyzed by finite element simulation. Firstly, the effect caused by different positions of the gate on the wire offset also is studied. Secondly, the influence of the above process parameters on the position deviation of gold wire is studied following an orthogonal experiment route. The results show that the inhomogeneous temperature field caused by transfer molding has an affect the quality of the chip wire bonding, while the melt temperature has a great influence on the wire offset. Due to the influence of the flow rate of transfer molding, the closer the wire to the gate position is, the larger the offset will be, especially for the wire perpendicular to the gate position.
Software Ecosystems Governance – An Analysis of SAP and GNOME Platforms

Software ecosystems play an important strategy in the IT industry. They involve the interaction of a set of actors on a common technological platform that results in a number of software solutions or services. Software ecosystems face intense competition and scrutiny of stakeholders and society as a whole. Therefore, they need to be aligned with principles of ethics, accountability and transparency. The governance of software ecosystems presents strategic procedures and processes to control, maintain, and evolve the platform. This article presents the application of a conceptual model for software ecosystem governance in the context of GNOME and SAP platforms. The case studies results suggest that governance of these platforms involve the need to manage software licenses, to define the type of collaboration in relation to governance (shared or closed), and to address social, technical and business risks in software ecosystems.
Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset

Vehicular ad hoc networks (VANETs) are a subsystem of the proposed intelligent transportation system (ITS) that enables vehicles to communicate over the wireless communication infrastructure. VANETs are used in multiple applications, such as improving traffic safety and collision prevention. The use of VANETs makes the network vulnerable to various types of attacks, such as denial of service (DoS) and distributed denial of service (DDoS). Many researchers are now interested in adding a high level of security to VANETs. Machine learning (ML) methods were used for constructing a high level of security capabilities based on intrusion detection systems (IDSs). Furthermore, the vast majority of existing research is based on NSL-KDD or KDD-CUP99 datasets. Recent attacks are not present in these datasets. As a result, we employed a realistic dataset called ToN-IoT that derived from a large-scale, heterogeneous IoT network. This work tested various ML methods in both binary and multi-class classification problems. We used the Chi-square (Chi2) technique was used for feature selection and the Synthetic minority oversampling technique (SMOTE) for class balancing. According to the results, the XGBoost method outperformed other ML methods.
Adaptive Fuzzy Decentralized Control for Fractional-Order Nonlinear Large-Scale Systems with Unmodeled Dynamics

This paper addresses the decentralized control issue for the fractional-order (FO) nonlinear large-scale strict-feedback systems with unmodeled dynamics. The unknown nonlinear functions are identified by fuzzy logic systems (FLSs) and the FO dynamic signals are introduced to dominate the unmodeled dynamics. Additionally, the fractional-order dynamic surface control (FODSC) design technique is introduced into the adaptive backstepping control algorithm to avoid the issue of “explosion of complexity”. Then, an adaptive fuzzy decentralized control scheme is developed via the FO Lyapunov stability criterion. It is proved that the controlled FO systems are stable and the tracking errors can converge to a small neighborhood of zero. The simulation example is provided to confirm the validity of the put forward control scheme.
Sensorless FOC Drive Control Methods for High Speed Motors of Unmanned Multicopters

This paper presents control methods for permanent magnet synchronous motors for unmanned multi-copters. These are a sensorless back emf observer, of which the parametrisation is described, and field-oriented control optimised for high speed (for instance 200 el. krpm). These methods are verified by simulation and experiments.
Maximizing Parallel Activation of Word-Lines in MRAM-based Binary Neural Network Accelerators

Magnetic RAM (MRAM)-based crossbar array has a great potential as a platform for in-memory binary neural network (BNN) computing. However, the number of word-lines that can be activated simultaneously is limited because of the low IH/IL ratio of MRAM, which makes BNNs more vulnerable to the device variation. To address this issue, we propose an algorithm/hardware co-design methodology. First, we choose a promising MCA structure based on the sensitivity analysis to process variations. Since the selected MCA structure becomes more tolerant to the device variation when the number of 1 in input activation values decreases, we apply an input distribution regularization scheme to reduce the number of 1 in input of BNNs during training. We further improve the robustness against device variation by adopting the retraining scheme based on knowledge distillation. Experimental results show that the proposed method makes BNNs more tolerant to MRAM variation and increases the number of parallel word-line activation significantly; thereby achieving improved throughput and energy efficiency.
Machine Learning Enabled Optimization of Pick-up Process for Thin Die

Driven by the demand of artificial intelligent, high-performance computing, electric vehicle and smart city, semiconductor industry is building high performance and high integrated systems. To increase the energy efficiency of these complicated systems, particularly in power modules, reducing the power consumption is needed. Thinning the die is one of the solutions, which can provide a lot of advantages including faster heat dissipation, low junction temperature, low electrical resistance, etc. However, due to its sensitivity of stress, processing of thin die is a challenging, including die pick-up process. In this paper, we proposed a methodology using machine learning and finite element method (FEM) to optimize the pin designs with the objective to minimize the stress in the die during pick-up process. A dynamic numerical model was first built up to simulate the stress distribution in the die during pick-up process, and a Gaussian process regression was applied to generate the relationships between pin parameters and die stress. Meanwhile, a Bayesian optimization was used to optimize the parameters. The optimized pin designs using the above methodology was further validated by FEM. Compared with traditional DOE method, the proposed methodology using machine learning and FEM exhibits much higher effectiveness in parameter optimization with less testing cost and efforts.
Linear Discriminant Analysis for Apples Fruit Variety Based on Color Feature Extraction

Choosing apples in large quantities depends on human eyesight which is still objective, so it needs a sorting process using technology so that it is feasible as industrial raw material. One technology that can be applied is to implement digital image processing. Through image processing, segmentation and classification techniques are deemed appropriate to carry out the sorting process. In this study, apples were classified by type, namely Braeburn Apples, Granny Smith, Pink Lady, Red Yellow, Golden and Red Delicious. In this study, 500 training images and 400 testing images of apples were used. The classification algorithm chosen is Linear Discriminant Analysis (LDA). LDA was chosen because it has characteristics in distinguishing two or several objects, can be used to reduce dimensions, and can divide data into several groups. Based on these advantages, LDA will be used in classifying apples based on skin color. To distinguish the characteristics of the fruit color, RGB $\mathrm{L}^{*}\mathrm{a}^{*}\mathrm{b}$ color extraction is used. The application is made using the Matlab 2017b programming language. The research that has been done has produced an accuracy of 98%.
Cluster-Based Communication protocol and architecture for a wastewater purification system intended for irrigation

The remote location of agricultural fields leads to the difficulty of deploying Precision Agriculture (PA) systems as there is no Internet access in those areas. Therefore, the use of long-range wireless technologies such as LoRa can provide connectivity to rural areas and allow monitoring PA systems remotely. In this paper, a heterogeneous architecture and protocol that allows communication with both WiFi and LoRa, including multiple hops in LoRa are presented. The design is based on a tree topology comprised of electronic devices deployed on different areas of interest for PA systems such as the canals of irrigation water, the fields, and the urban areas that generate wastewater. A set of practical tests with different configurations have been performed to determine the correct operation of the proposed protocol. The results show that the consumed bandwidth for both 433 MHz and 868 MHz frequency bands remained within the limits for the most restrictive LoRa configurations. Therefore, different deployment needs can be addressed with the implementation of this proposal. Furthermore, the use of packet transmission delays of 500 ms at the CH node results in high successful packet delivery rates.
Highly Conductive Silver Nanowire Transparent Electrodes Hybridized with Laminated Multi-Layer MXene

Silver nanowires (AgNWs) and new-type two-dimensional material MXene have shown great prospects for applications in new-generation flexible electronic devices due to their brilliant properties. In this work, high-performance silver nanowire/MXene hybrid transparent conductive electrodes (TCEs) were prepared by a solution method. It studied the influence of AgNW concentration on the photoelectric properties of TCEs. Then, several-layer or even single-layer MXene were obtained by a delamination process and were added to the AgNW networks to prepared flexible hybrid electrodes on polyethylene naphtholate (PEN) substrates. The hybrid TCEs showed a low sheet resistance of 24.4 Ω/sq with the transmittance of 90.6% at 550nm. Moreover, the MXene sheets covered on the surface could improve the conductivity of the film and block the oxidation of silver nanowires. The results mean that the novel AgNW/MXene TCEs have great potential for the practical applications of high-performance flexible electronic devices.
Electrode Materials for Hydrogen Production by Alkaline-Water Electrolysis Powered by Renewable Energy Sources

Effectiveness of electrode materials for electrolizers working with renewable energy sources are discussed. The electrochemical behavior of low-alloy chrome-molybdenum-vanadium steel (12Cr1MoV) in a 5 mol.L−1 sodium hydroxide solution is presented and compared with steel of ordinary quality (Steel 3) and nickel that are used in electrolyzers for producing hydrogen. Weight loss tests show that the chrome-molybdenum-vanadium steel is more corrosion-resistant in the tested environment than Steel 3. Overvoltage of oxygen evolution reaction on 12Cr1MoV steel under galvanostatic conditions of 200 m.Arcmr−2 is 40 mV lower than on Steel 3 and 150 mV lower than on nickel. Chrome-molybdenum-vanadium steel is perspective to replace Steel 3 in alkaline water electrolysis both as electrode material and as substrate for different catalytic coatings. Multilayer coating [(Ni-Cu)/(Mi-Mi(OH)2)]n is proposed for use in alkaline electrolysis of water and in fuel cells in systems with renewable energy sources. Proposed electrodes are suitable for use in electrolyzes powered by renewable energy sources as they show lower corrosion rate during pauses and load changes.
Deep Reinforcement Learning for Load-Balancing Aware Network Control in IoT Edge Systems

Load balancing is directly associated with the overall performance of a parallel and distributed computing system. Although the relevant problems in communication and computation have been well studied in data center environments, few works have considered the issues in an IoT edge scenario. In fact, processing data in a load balancing way for the latter case is more challenging. The main reason is that, unlike a data center, both the data sources and the network infrastructure in an IoT edge system can be dynamic. Moreover, with different performance requirements from IoT networks and edge servers, it will be hard to characterize the performance model and to perform runtime optimization for the whole system. To tackle this problem, in this work, we propose a load-balancing aware networking approach for efficient data processing in IoT edge systems. Specifically, we introduce an IoT network dynamic clustering solution using the emerging deep reinforcement learning (DRL), which can both fulfill the communication balancing requirements from IoT networks and the computation balancing requirements from edge servers. Moreover, we implement our system with a long short term memory (LSTM) based Dueling Double Deep Q-Learning Network (D3QN) model, and our experiments with real-world datasets collected from an autopilot vehicle demonstrate that our proposed method can achieve significant performance improvement compared to a benchmark solution.
Vehicle Autonomous Navigation with Context Awareness

Nowadays, many models performing global robotic navigation exist, and they are capable to reach their set destination safely and autonomously. However, most of them don’t take into account the information coming from the context in which such navigation is occurring, resulting in a severe information loss. Without context-aware navigation, it is not possible to build a model to let the vehicle adapt its behaviour to the situation, in the way a human driver spontaneously does. It is therefore needed a study on how to connect the contextual information with the robot’s control loop. For our solution we built an ontology-based structure that helps the vehicle reason in real-time and change its own behaviour in function of given contextual information. After the definition of the Context of Navigation, in this paper we propose an approach to the problem of encoding the Context Awareness in the Autonomous Navigation’s controller. Finally, such approach is put to the test in a simulator, to discuss the results achieved.
Safe Adaptive Deep Reinforcement Learning for Autonomous Driving in Urban Environments. Additional Filter? How and Where?

Autonomous driving (AD) provides a reliable solution for safe driving by replacing human drivers responsible for the majority of accidents. The emergence of Machine Learning, specifically Deep Reinforcement Learning (DRL), and its ability to solve complex games proved its potential to address AD challenges. However, model-free methods still suffer from safety-related issues that can be resolved using safe-DRL approaches. The addition of model-based safety filters to the learning-based algorithms provides safety bounds on their performance and constraint satisfaction. In this paper, we investigate the addition of a safety filter based on Model Predictive Control and show an increase in mean testing episode reward by 110% from −75 mean episode reward during testing for 50 episodes for Deep Deterministic Policy Gradient ( $DDPG$ ) to 7.758. We study the impacts of safety filters (7.758 mean reward), heuristic rules, bounded additive noises (0.49% performance increase comparing to noise-free case), and exploration (3.425 mean reward) on the learning algorithm. We compare the effects of filters in the context of simulated exploration and bounded exploration and prove that bounded exploration results in 9.86% increase in mean reward and 12.95% decrease in std comparing to the other method. Additionally, inspired by Deep Internal Learning and biological mechanisms like brain plasticity, we investigate the idea of using each sample for training only once instead of utilizing stochastic batches which increases the mean testing accumulated reward by 1.87% and leads to the best performance (7.942 mean reward and 0.048 std). Finally, the results demonstrate better automotive results for our proposed method than DDPG. Our proposed method, DDPG with safety filter in bounded exploration and adaptive learning under noisy input conditions, has a success rate of 100% under different traffic densities for the simulation environment used in this paper and our assumptions. The proposed method’s automotive results are shown for a braking scenario to avoid collision with other road users.
A PVT Tolerant True Random Number Generator Based on Oscillator Phase Under Sub-Harmonic Injection Locking

A digital true random number generator based on the oscillator phase generated by the second-order sub-harmonic injection locking phenomenon is proposed in this paper. Owing to the thermal jitter, the phase difference between a free-running (i.e., unsynchronized) oscillator and an injected external signal collapses to one of two stable solutions in a truly random manner. The injected external signal is generated from another oscillator operating at close to twice the frequency of the free-running oscillator. The random sequence extracted from the resulting stable solutions is bias- and correlation-free, nullifying the need for a bias compensation circuitry. The proposed design is demonstrated with extensive simulations to be robust under process, voltage, and temperature variations. The generated random patterns pass the National Institute of Standards and Technology test suite.
Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features

Dental caries generally occurs due to consuming foods containing carbohydrates, such as sucrose, and rarely brushing teeth, thus causing gradual damage to the layers and structures of the teeth. This study aims to build a dental caries level classification system using image processing and machine learning methods. The first step was to analyze and discover the extraction results from Hu's moment invariants. After successfully extracting the features, the classification was carried out using a Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). This study employed radiographic images of four dental caries classes consisting of Class 1, 2, 3, and 4. A total of 198 images of dental caries were used as training data and 66 images as test data. The classification obtained accuracy value of the SVM and KNN. The highest accuracy was discovered in the Fine Gaussian model of the SVM classification method with 77.6%, while the lowest accuracy was depicted in the Cubic model with 57.4%. Meanwhile, the highest accuracy by using KNN is 100% of accuracy using Fine and Weighted KNN models.
Spurious suppression through the control of their couplings: application to TM cavity filters

In this paper, a new approach for improving filter spurious free band is proposed. The main idea is to control the coupling coefficients of the spurious resonances in order to force them to cancel each other. The required coupling levels can be estimated by using an equivalent circuit. It is observed that, in contrast with what is commonly done, it is not convenient decreasing all couplings to the spurious resonances. The only coupling to be minimized is the central one, all other should instead be maximized. This procedure can be used for suppressing spurious resonances generated by higher order modes. This method has been here applied to TM cavity filters. As shown in the paper, it is in general applicable to even order filter only. In this paper, a two-pole TM cavity filter centered at 9 GHz with a 3 dB FBW of 1.45% has been designed and manufactured. By applying this technique, the spurious free band has been increased from 12.4 GHz of the initial design to 19.7 GHz. Measured results show the feasibility of the proposed approach.
Reconfigurable intelligent surface (RIS): Eigenvalue Decomposition-Based Separate Channel Estimation

Reconfigurable intelligent surface (RIS) has recently drawn significant attention in wireless communication technologies. However, identifying, modeling, and estimating the RIS channel in multiple-input multiple-output (MIMO) systems are considered challenging in recent studies. In this paper, a disassembled channel estimation framework for the RIS-MIMO system is proposed based on the eigenvalue decomposition (EVD) concept to separate the cascaded channel links and estimate each link separately. This estimation is based on modeling the RIS-MIMO channel as a keyhole MIMO system model. Numerical results show that the proposed estimation method has a low estimation time overhead while providing less estimation error.
Sequential Analysis of Drop Impact and Thermal Cycling of Electronic Packaging Structures

Electronic packaging structures go through complicated loading history such as power on-off, vibration and drop impact during the storage and serving conditions processes. All these loading applications result in the deformation and possible damage accumulation in the packaging structure of the electronic device. Considering that the thermal cycling is a standard procedure for evaluating the mechanical reliability of a packaging, we systematically investigate the combination effect of the drop and thermal cycling on the reliability of the packaging structures by using the finite element analysis. Compared with the transient analysis of drop impact, the analysis duration for thermal cycling is significantly prolonged. A representative Ball Grid Array (BGA) packaging structure is adopted to perform the sequential analysis. The strain rate and temperature dependent visco-plastic properties are considered for the Sn-3.0Ag-0.5Cu (SAC305) solder material at the solder joints. Those material properties have been validated against published experimental data. Firstly, the numerical analysis of the packaging structures subject to drop impact is conducted to obtain the distribution of plastic deformation and strain. As the plastic deformation and strain are not recoverable after the loading application, those distributions can be utilized as initial strain conditions to be imported into the subsequent analysis under thermal cycling. In the second analysis step, a temperature profile between −50°C and 125°C is applied to the packaging structure to predict the deformation and strain during the thermal cycling. The thermal fatigue life is evaluated using the Coffin-Manson model based on the predicted increment of equivalent plastic strain in the critical solder joints. More importantly, the effect of drop-impact damage on the thermal deformation and thus on the fatigue life is discussed by correlating the drop impact, thermal cycling and fatigue life as a closed loop.