Miracle-dz/UniFAR-FTUs huggingface dataset:
Forward and Backward Private Dynamic Searchable Symmetric Encryption for Conjunctive Queries

Recent research in Dynamic Searchable Symmetric Encryption (DSSE) focuses on efficient search over encrypted data while allowing updates. Unfortunately, as demonstrated by many attacks, updates can be a source of information leakage that can compromise DSSE privacy. To mitigate these attacks, forward and backward privacy of DSSE schemes have been introduced. A concerted effort of the research community has resulted in the publication of many DSSE schemes. To the best of our knowledge, however, there is no DSSE scheme supporting conjunctive queries, which achieves both forward and backward privacy. We give two DSSE schemes with forward and backward privacy, which support conjunctive queries, and they are suitable for different applications. In particular, we first introduce a new data structure termed the extended bitmap index. Then we describe our forward and backward private DSSE schemes, which support conjunctive queries. Our security analysis proves the claimed privacy characteristics, and experiments show that our schemes are practical. Compared to the state-of-the-art DSSE VBTree supporting conjunctive queries (but not backward privacy), our schemes offer search time that is a few orders of magnitude faster. Besides, our schemes claim better security (called Type-C backward privacy).
Efficient spatio-temporal point convolution

Real-time online semantic segmentation poses special challenges such as how to perform 3D convolution directly over the 3D geometric data which arrives in real-time. While different approaches exist, we propose a novel 3D convolution which operates directly on the point cloud, relies solely on the most important features that are found by the system. This eliminates the randomness proposed by other solutions, enabling only important information to be processed at runtime. Our architecture is powered by a novel heuristic search done on a two-level tree: a global interval tree and a local octree. While the global tree has the purpose of maintaining the 2D-3D information fusion, the local tree processes the neighborhood of local points into an ordered set, enabling execution of different operations. The trees update dynamically as new data arrives, while our heuristic search enables a better execution time, speeding the process from an average of 7 FPS to 11 FPS while maintaining accuracy. Through evaluation on public datasets, we show that our method gives results comparable to the state of the art, while gaining significant speed in terms of execution time.
A novel integration approach for a complex supply chain optimization problem in an export bulk port

Supply chain management is the art and science of delivering the right product in good quality, to the right customer, in the right place and at the right time. Bringing about the emergence of this much-wanted value is carried out by a synergic integration of an optimization tool within the control system rather than developing an isolated optimization solution. Then, if we move to the optimal planning tool in particular, it is believed that, the integration of planning and scheduling decisions of storage, routing and loading together making up an integrated optimization plan without contradictory goals, is vital for an efficient use of the three aforementioned bulk port operations. In this paper, we present an architecture to get the optimization tool fit into the industrial control system to consider disturbances, feedback cycles and dynamics. Then, we propose a novel approach of the integration of the three sub-optimizers using the systems engineering method. The novel aspect of this contribution is twofold. First, rather than only describing the problem before formulating the mathematical model. The systems engineering method, allowed to specify the problem with an in-depth scientific analysis, in order to get a complete and consistent mastery of the problem. Therefore, the problem formulation will not miss important use cases of the complex supply chain. Second, the resulting specified planning integration helps the whole planning tool converge in a reasonable time by avoiding quasi-infinite interaction loops.
Investigation of Saturated Drain Current Change Phases For PMOS HCI Stressed at Ibmax

During the HCI Ibmax stress, PMOS's Idsat increased first and then decreased, it's different when stressed at Vg=Vd. From relevant information [1], we suspected that electrons injection and holes tunneling affected the phenomena. To explore the physical mechanisms, we studied different stress Vd, different channel length elements, and forward/reverse measurements. We concluded that Idsat increase the most under a short channel length element, larger stress Vd and forward measurement situation because of the different ΔVinter-d and ΔVinter-s; this affected the probability of electrons injection into the oxide near the drain side, and holes tunneling into oxide near the source side.
Speech Enhancement Using NMF based on Hierarchical Deep Neural Networks with Joint Learning

In this paper, we propose a novel method which includes autoencoder and deep neural networks (DNNs) in a hierarchal structure for speech enhancement. In this method, at first, two parallel autoencoders are employed to obtain the nonnegative matrix factorization (NMF) parameters of speech and noise in a nonlinear mapping. After that, by using the spectrum of noisy speech as the input of the encoder portion of the autoencoders, the outputs of the encoders are calculated and utilized as the input of DNNs in the next hierarchies to further enhance the speech spectrum more efficiently. Also, the last three hierarchies including the decoder portion of autoencoders and the DNN will be trained in a joint learning scenario to improve the results. The proposed method is evaluated on TIMIT corpus with perceptual-evaluation-of-speech-quality (PESQ) and frequency-weighted-segmental-signal-to-noise-ratio (fwSNRseg) criterions. The obtained results show a significant improvement in both seen and unseen noises compared to baselines.
Analysis of the Effects of Boron Transient Enhanced Diffusion on Threshold Voltage Mismatch in Steep Retrograde Doping NMOSFETs with Inserted Oxygen Layers

Steep retrograde doping devices were fabricated using undoped epitaxial Si channels with inserted oxygen layers. The effects of boron transient enhanced diffusion (TED) on threshold voltage (Vth) mismatch were investigated. Suppression of boron TED was effective for reducing Vth mismatch of steep retrograde doping devices as well as flat doping profile devices.
Accelerated, Reactive Aging Tests of Parylene C, SiO2, and Si3N4 Packages for Chronic Neural Implants

Reliable packaging for implantable neural prosthetic devices in body fluids is a long-standing challenge for device’s chronic applications. This work studied the stability of Parylene C (PA), SiO2, and Si3N4 packages on tungsten wires using accelerated, reactive aging tests in three solutions: pH 7.4 phosphate-buffered saline (PBS), PBS+30 mM H2O2, and PBS+150 mM H2O2. Different combinations of coating thicknesses and deposition methods were studied at 67 °C. Analysis of the preliminary data shows that the pinholes/defects, cracks, and interface delamination are the main attributes of metal erosion and degradation in reactive aging solutions. Failure at the interface of package and metal is the dominating factor in the wire samples with open tips.
A framework of distribution transformer health early warning system based on EDC-GRU network

In recent years, with the rapid growth of distributed generation, the load pressure of distribution transformer is also increasing, and power fail occurs frequently. Therefore, this paper studies a distribution transformer health early warning system based on EDC-GRU network. Firstly, we clarify the design idea of the system. Secondly, we define all the architecture and function. Finally, the result shows that our system can predict the future health status of distribution transformer, through the analysis of the operation data of each distribution transformer and make the early warning in time, which can inform people to take corresponding measures so as to reduce the failure rate, and maintain the health status of the distribution network.
Resilient Control Strategy for Detection and Mitigation of Disruptions in Microgrids

A distributed power system, including several microgrids, is always vulnerable to attacks which can result in malfunctioning. In order to protect the system from such attacks, the problem of resilient consensus in microgrids has been discussed in this paper. A network of microgrids as a multiagent system is considered in the presence of misbehaving agents that might make updates arbitrarily. By using the quantization algorithm, each agent will take integer values under directed communication links. The normal agents aim to reach consensus while the malicious ones become isolated. This can be achieved by the use of a special approach of mean subsequence reduced algorithms (MSR) in which all normal agents ignore extreme values received by their neighbors before updating their states. The agents are studied under synchronous and asynchronous updates. Moreover, a controller is suggested that can make the system reach consensus even though, the normal agents only can access their neighbors' values with time-delays. Finally, numerical examples and simulation results are provided.
A Method of Safe and Fast Bluetooth Connection and Energy Saving for Educational Environment

In today’s digital era, the combination of digital technology and education to promote education has become a trend. One example is the pen control system. In the current mainstream pen control system, the communications between the smart pen and the smart terminal device like a tablet computer all use Bluetooth technology. This paper designs the self-adaptive identification and pairing scheme between the Bluetooth pen and the smart tablet by analyzing the Bluetooth protocol structure. This paper implements the application software of the Bluetooth master based on the Android tablet,which illustrates the feasibility of the scheme.
Polymer/paper-based double touch mode capacitive pressure sensing element for wireless control of robotic arm

In this work, a large area, low cost and flexible polymer/paper-based double touch mode capacitive pressure sensor is demonstrated. Garage fabrication processes are used which only require cutting, taping and assembly of aluminum (Al) coated polyimide (PI) foil, PI tape and double-sided scotch tape. The presented pressure sensor operates in different pressure regions i.e. normal (0 to 7.5 kPa), transition (7.5 to 14.24 kPa), linear (14.24 to 54.9 kPa) and saturation (above 54.9 kPa). The advantages of the demonstrated double touch mode capacitive pressure sensors are low temperature drift, long linear range, high pressure sensitivity, precise pressure measurement and large die area. The linear output along with a high sensitivity range (14.24 to 54.9 kPa pressure range) of the sensor are utilized to wirelessly control the movement of a robotic arm with precise rotation and tilt movement capabilities.
A Framework for Optimum Determination of LCL-Filter Parameters for N-Level Voltage Source Inverters Using Heuristic Approach

The use of voltage source multilevel inverters (VS-MLIs) has grown enormously over the last decade, and it is expected that these inverters will be deployed in future power grids (smart grids), especially for medium voltage–high power applications. This paper investigates the application of passive power filters (PPFs) for harmonic mitigation at the output of VS-MLIs for more efficient grid integration of renewable energy sources. It proposes a generic model, using a heuristic approach, for the optimum design of an LCL power filter at the output of VS-MLIs. The proposed model transforms the LCL filter design problem into an optimization problem and applies a genetic algorithm (GA) to solve it. The objective function to be optimized is a multi-objective function based on inverters’ total harmonic distortion and energy losses. The optimization problem is subject to applied design constraints. As a main optimization objective, a precise evaluation methodology for the inverter power losses is presented, which was built according to a practical switching device datasheet. The method is applicable to any VS-MLI topology and any number of levels (N). As a case study, the proposed design optimization approach was implemented to optimally design the LCL filter at the output of a grid connected 11 KV, 5 MVA 7-level cascaded H-bridge multilevel invert (CHB-MLI). The IGBT module Device IGBT of type FZ400R65KE3 by Infineon was used for simulation. MATLAB-SIMULINK is used for the modelling and simulation. We found that the proposed design approach is more generic, efficient, and simple to apply than conventional design approaches which requires more system detail, relies more on the designer’s experience, and normally does not results in optimum design. The proposed approach is generic and can be applied for different VS-MLIs topologies and for any number of levels (N).
Study of Gold Particles in HFSS with Varying Physical Parameters and Arrangements

Radio Frequency (RF) based sensors have shown great potential for the qualitative and quantitative analysis of different chemical and biological entities. In this work, the feasibility of performing RF based sensing by using gold particles has been explored with the help of the computational tool, HFSS (High Frequency Structure Simulator). The analysis is performed for an Interdigitated Capacitor based sensor. Various physical parameters of cylindrical gold particles such as radius and thickness have been varied, and the electrical parameters of the sensor systems have been studied. In particular, the reflection coefficients have been determined for various cases. In addition to the physical parameters, different configurations of the gold particles such as their linear and non-linear arrangements have been analyzed. The analyses offer promising results for the development of efficient sensing platforms.
Wafer Map Classifier using Deep Learning for Detecting Out-of-Distribution Failure Patterns

Pattern analysis of wafer maps in semiconductor manufacturing is critical for failure analysis aspects or activities that increase yield. As deep learning becomes more popular than ever, research on the wafer map classification is active. However, more accurate pattern classification and data processing methods are required for the accuracy of commonality analysis to find suspected facilities using wafer map classification. It is difficult to represent all types of wafer maps in dozens of forms, and the frequency of wafer map shapes that vary with yield changes also requires the processing of undefined pattern wafer map data. We define out-of-distribution data of wafer map data that does not identify in the pattern classifier and suggest a network that uses the convolutional neural network (CNN) with residual units and training methods to classify it efficiently. We employ 15,436 real wafer map data for pattern classification and detection of out-of-distribution data.
Transactional Data Analytics for Inferring Behavioural Traits in Ethereum Blockchain Network

Ethereum is a blockchain based development platform for users to build and deploy decentralized applications and smart contracts. Transactions substituting money over Ethereum space are carried out using Ethereum’s cryptocurrency ‘Ether’. Though the decentralization and public ledger recording of the transactions proves its limpidity, the anonymity of the users, hiding their true identity behind the addresses echoes the need for discerning the behavioural traits in transactions. The appalling nature of Ethereum transactions to have both, security and threat at a comparable level, demands analytics for inferring traits for better perspectives and insights. This research work focuses on characteristic analysis of Ethereum transaction space for inferring behavioural traits in supervised and unsupervised context. In an unsupervised environment, raw transaction data is extradited to a tabular transaction structure by selecting appropriate features. The data is further clustered and validated to form coherent groupings of similar patterns. The clusters are characteristically analyzed based on the features through Radar plots for inferring behavioural traits. In supervised context, labelled transactions are represented using histograms and feature-based characteristic analysis is performed to infer traits. Such analytics lay foundations for future deeper analysis that can be used to discover trends and patterns to understand the transaction network, enhancement of trading strategies, identification of bot activities, anomaly detection and several others.
Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control.
Trajectory Based Motion Planning for On-Ramp Merging-Situations Considering Extended Evaluation Criteria

In recent years, great progress in the area of automated and autonomous driving has been made. Nevertheless, key areas such as situation awareness and maneuvering still remain challenging, especially in urban areas and at highway junctions. In the following work a novel trajectory planner for automated lane merge situations will be presented. While our approach is adaptable to any merge scenario, we focus in this work on the most challenging maneuver, the on-ramp freeway merging. We present novel metrics for safety and comfort, such as merge-duration and lane usage and combine them with established key metrics such as time-to-collision, acceleration etc. Using these metrics, we validate our approach in a 3D physics simulation for two scenarios: dense highway traffic and highD-scenario based on real vehicle data.
Fuzzy – Sliding Mode Versus Integral Sliding Mode Controller for a Quadrotor with Mass Uncertainty under Effect of Wind

This paper presents a fuzzy sliding mode controller (FSMC) and an integral sliding mode controller (ISMC) separately to control position of a quadrotor with an uncertain mass in the presence of wind disturbance and compares the operation of these controllers. Euler-Newton method is used to derive the quadrotor dynamic equations that considers the wind effect. The outer loop controller determines the desired angles of the quadrotor which are stabilized by a feedback linearization controller as the inner loop control. Also, the stability proof of FSMC and ISMC is done. Finally, simulation results are utilized to represent the operation of the control approaches and compare their performances.
Wrapper-based feature selection: how important is the wrapped classifier?

Wrapper-based feature (subset) selection is a frequently used approach for dataset dimensionality reduction, especially when dealing with classification problems. The choice of wrapper is at the forefront of these approaches, whilst the choice of the classifier is typically based on its simplicity as to reduce the computational cost. Since the search is guided by the selected classifier, the same one is also later used for independent testing. This raises the question of how well such feature subsets are suited for other types of classifiers. In other words, can one classifier be used for finding feature subsets that are also effective for others? An investigation into this matter was performed by testing and analysing the utility of subsets found by one classifier with respect to other classifiers. It hints at the importance of classifier choice since some models, whilst used inside the wrapper, can solely conform the dataset to themselves, whilst others are less susceptible to this issue. Consequently, an insight into the robustness of the employed classifiers was gained as well.
Design of Fuzzy Controller for Non Isolated High Gain DC-DC Converter

DC-DC converters are as important in DC micro grid as transformers are in AC systems. Recently non isolated high gain (NIHG) DC-DC converter is proposed that can be a good choice for DC micro grids. As it has better efficiency at high voltage gain, it can overcome the problem of conventional converters whose efficiency drops down at high voltage gains. To use this converter in DC micro grid, an appropriate controller is required. This paper proposes fuzzy controller (FC) and its comparison with proportional, integral and derivative (PID) controller for NIHG converter. PID and FC for NIHG converter are designed and simulated in MATLAB/Simulink. The control response of NIHG converter is analyzed at different operating conditions for both controllers. As compared to PID controller, it is observed that the performance of FC for NIHG converter is better in terms of low overshoot and shows good performance on large changes in load. Moreover, PID controller has low settling time for one operating condition but its performance is badly affected by large changes in loads.
Robust Membership-Function-Dependent Decentralized Controller Design for Fuzzy Large Scale Systems

This paper considers the decentralized control problem for nonlinear large scale systems with time-varying delay. A decentralized state feedback controller is proposed to perform the control process, and ensure a prescribed $H_{\infty}$ performance level. Next, a new method is developed to relax and simplify the stabilization conditions of the system. By considering the membership functions as the components of a vector, a convex polyhedron is created to enclose the trajectory of the membership functions. Then, based on the Lyapunov-Krasoviskii functional method and by using membership-function-dependent (MFD) information, less conservative stabilization conditions are derived. Finally, the advantages and merits of the proposed method are depicted by a numerical example.
A Novel STATCOM based on Capacitor Voltage Self-Balanced Z-type Modular Multilevel Converter

This paper proposed a Z-type modular multilevel converter (Z-MMC) based static synchronous compensator (STATCOM) with capacitor voltage self-balancing ability. With series connection of Z-type submodules, the Z-MMC can generate stepped output voltage waveform with very small harmonic contents. The voltage rating of the Z-MMC can be easily scaled up by adding new ZT-SMs, which makes it suitable for medium/high voltage applications. Different from the traditional modular multilevel converter (MMC) that requires complex voltage balancing control for each capacitor, the capacitor voltages of the Z-MMC are self-balanced. Therefore, the control of the Z-MMC based STATCOM is simplified compared with the MMC based STATCOM. Simulation results confirm the effectiveness of the proposed Z-MMC based STATCOM.