Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain

The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as <inline-formula> <tex-math notation="LaTeX">$f-x$ </tex-math></inline-formula> denconvolution and <inline-formula> <tex-math notation="LaTeX">$f-x$ </tex-math></inline-formula> singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tradeoff between random noise suppression and seismic signal preservation.
A Permissioned Blockchain Based Feature Management System for Assembly Devices

With the increasing spread and adoption of electronics and software as integral parts of all kinds of physical devices, such devices are becoming controlled by their embedded software. Correspondingly, the manufacturing business has started the transition from selling hardware to selling features (e.g. “insane mode” and “ludicrous mode” in Tesla Model S). Consequently, a trustworthy system to automate such a process becomes essential. This article introduces a permissioned blockchain-based feature management system for assembly devices. Firstly, it leverages software licensing technology to control assembly devices’ features. Secondly, by recording the license ownership transaction data in a permissioned blockchain, the approach (1) takes advantage of blockchain’s trust mechanism and its distributed nature to improve the trustworthiness of the feature management system, and (2) adopts the permissioned blockchain technology to ensure that the license transactions are only visible and applicable to authenticated actors. We further describe an implementation, a proof-of-concept evaluation focusing on functionality and performance, as well as a security analysis.
EEG Enhancement by Auto DNNs with Regularization of Spatial Feature Loss

Electroencephalography (EEG) can be applied in medical diagnosis forecasts via using Brain-Computer Interface (BCI) technology. EEG signals are low voltage signals that are susceptible to various types of noise such as 50 Hz power frequency, noise between the electrodes and the skin and so on. In this work, an enhancement method for EEG data based on a deep neural network (DNN) architecture search method in which the spatial feature loss acts as a regularizer while training the end-to-end network for best noise removal effect is proposed. The proposed system realizes noise reduction by using DNNs, which employs an alternative objective function combining spatial feature loss with time-domain feature loss. The spatial feature can be obtained by Common Spatial Pattern (CSP) algorithm. Experimental results show that auto DNNs with regularization of spatial feature loss can efficiently eliminate the simulated noise in EEG data and makes the mean square error between predicted values and real values as small as 0.06. In addition, the proposed objective function outperforms objective function with single time-domain feature loss. Meanwhile, the number of parameters in auto DNNs is obviously less than other models by 81.7% to 94.2% and also less when using proposed objective function than not use it by 28.6%. These results demonstrate that proposed DNNs based method can reduce parameters and computation. Therefore the proposed method is promising for the wearable application and embedded scenarios.
Research on Active Obstacle Avoidance Control Strategy for Intelligent Vehicle Based on Active Safety Collaborative Control

Aiming at the issue of active obstacle avoidance control for intelligent vehicles under emergency, an active safety collaborative control system integrated with adaptive cruise control (ACC), rear steering control (RSC), and rollover braking control (RBC) is developed. The proposed novel control architecture consists of a supervisor, an upper controller, and a lower controller. The supervisor decides which control mode to be used based on multi-sensors information of the driving vehicle. Active rear wheel steering based on lateral acceleration feedback and active braking control strategies based on fuzzy PID are added into the control model to enhance driving stability. To make the vehicle motion control model suitable for highway path tracking with obstacle avoidance under emergency, a priority is given to the RBC controller even though the vehicle speed is lower than the expected speed when the rollover index exceeds the safety threshold. A nonlinear vehicle simulation model is established to validate the proposed control scheme based on Carsim. Finally, a Carsim-Simulink co-simulation model is constructed, and simulation results show that the proposed integrated control strategy based on active safety collaborative control has good performance in both path tracking and driving stability, and has better stability of rollover under emergency.
Secure Edge Computing Management Based on Independent Microservices Providers for Gateway-Centric IoT Networks

Edge computing is an emerging computing paradigm that distributes the computational capability to the edge of networks for enabling the computation near to the environment where the sensors and actuators are deployed. Therefore, from the network edge, heterogeneous solutions can be provided to the Internet based on sufficient computing ability. Nevertheless, computing and networking resources are constrained for devices in the network edge. Providing secure services from edge computing is a challenge based on constrained resources. In this paper, we propose a secure edge computing to provide management of device, data, user and additional services based on deploying independent microservices providers with a security gateway on an edge gateway. The edge gateway is the hub of a local network where multiple IoT devices are deployed to interact with the physical environment for sensing and actuating. The gateway provides the management functionalities through microservices based on multiple independent server modules. Each gateway-centric local network has a standalone management service based on the gateway. For providing secure edge computing services through the edge gateway, a security gateway is deployed on the proposed edge gateway to provide Representational State Transfer Application Programming Interfaces to expose the security services to the Internet instead of microservices from management modules. Moreover, a client support gateway is deployed in the edge gateway to provide services of User Interface and access forwarding based on web sessions to support user authentication and authorization with the security gateway. Based on the proposed edge gateway including client support and security gateway, IoT clients and IoT devices are enabled to communicate for providing secure edge services of access and visualization to users.
Long-Term Aging of Al2O3 Passivated and Unpassivated Flexible a-IGZO TFTs

Amongst the new materials studied for the fabrication of high-performance flexible thin-film transistors (TFTs), amorphous indium-gallium-zinc-oxide (a-IGZO) exhibits a combination of advantages that enables its application in commercial electronics. Hence, it is crucial to understand the electrical stability of a-IGZO TFTs over long periods of time. In this work, we present the effects of long-term aging on Al<sub>2</sub>O<sub>3</sub> passivated and unpassivated flexible a-IGZO TFTs over a period of 80 months (<inline-formula> <tex-math notation="LaTeX">$\approx 6.5$ </tex-math></inline-formula> years). It is found that although remaining functional, these devices are influenced by different instability effects. More specifically, positive gate bias stress experiments indicate that the Al<sub>2</sub>O<sub>3</sub> passivation layer contributes to the degradation of the devices’ performance. These results show that the Al<sub>2</sub>O<sub>3</sub> passivation, although beneficial to the initial device stability, does not prevent the degradation of the passivated devices in comparison with their unpassivated counterparts after long periods of storage.
Non-Coherent Massive MIMO-OFDM Down-Link Based on Differential Modulation

Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are wireless radio technologies adopted by the new Fifth Generation (5G) of mobile communications. A very large number of antennas (massive MIMO) is used to perform the beam-forming of the transmitted signal, either to reduce the multi-user interference (MUI), when spatially multiplexing several users, or to compensate the path-loss when higher frequencies than microwave are used, such as the millimeter-waves (mm-Waves). Usually, a coherent demodulation scheme (CDS) is used in order to exploit MIMO-OFDM, where the channel estimation and the pre/post-equalization processes are complex and time consuming operations, which require a considerable pilot overhead and also increase the latency of the system. As an alternative, non-coherent techniques based on a differential modulation scheme have been proposed for the up-link (UL). However, it is not straightforward to extend these proposals to the down-link (DL) due to the (usually) reduced number of antennas at the receiver side. In this paper we overcome this problem, and assuming that each user equipment (UE) is only equipped with one single antenna, we propose the combination of beam-forming with a differential modulation scheme for the DL, enhanced by the frequency diversity. The new transmission and reception schemes are described, and the signal-to-interference-plus-noise ratio (SINR) and the complexity are analysed. The numerical results verify the accuracy of the analysis and show that our proposal outperforms the existing CDS with a significant lower complexity.
Study on Multilevel Resistive Switching Behavior With Tunable ON/OFF Ratio Capability in Forming-Free ZnO QDs-Based RRAM

Resistive random access memories (RRAMs) have attracted tremendous attention due to their miniaturized size, low power consumption, high response speed, and simple fabrication requirements, which are regarded as a promising candidate for future nonvolatile memories. However, the traditional binary storage RRAMs fail to meet the high-density requirement in the area of big data. One of the ways to overcome this obstacle is to realize multilevel state transitions in a unit RRAM. Herein, we propose a facile spin-coating method to develop a multilevel RRAM based on Al/poly(methyl methacrylate) (PMMA)/ZnO quantum dots (QDs)/PMMA/ZnO QDs/PMMA/fluorine-doped tin oxide (FTO) structure, with the significant merits of typical forming-free bipolar resistive switching behavior, low-power operating voltage, stable endurance, and excellent retention. In specific, the ON/OFF ratio of the proposed ZnO QDs-based device can be adjusted by tuning the concentration of the ZnO QDs. Moreover, a multilevel RRAM performance with distinct four-level resistance states is obtained by varying the compliance current, which demonstrates that the proposed device has a specific application in multilevel data storage. The conductive filament model, combining the enhanced internal field and robust formation of the conductive filament, is introduced to explain the mechanism of the resistive switching behavior and multilevel performance. This work paves a new way for its prospect in constructing high-density data storage RRAMs.
Synthesizing Foot and Ankle Kinematic Characteristics for Lateral Collateral Ligament Injuries Detection

Deep learning has been applied in healthcare, where features of patients are read by the computer to assist with diagnosis and treatment. In sports medicine, kinematic characteristics of injuries need to be defined. Patients’ data are then acquired amplified to training deep learning models. In this study, we tracked motions of lower extremities in patients with lateral collateral ligament injuries of the ankle. Key kinematic characteristics of injuries were identified by comparing patients to normal individuals. The deep convolutional generative adversarial networks (DCGANs) was employed to synthesize a modest-sized labeled dataset to avoid the problems raised from using large-scale manual labeling data. We then fed a combination of real and synthesized data to train long short-term memory (LSTM) networks to detect patients with ligament injuries. The results showed that combined data yielded a better outcome, measured by classification accuracy and f1-score, than solely using the patient data or with a large quantity of synthesized single range of motion feature.
An Analytical Jitter Tolerance Model for DLL-Based Clock and Data Recovery Circuits

This article presents the theoretical analyses and experimental results about jitter tolerance for delay-locked loop (DLL)-based clock and data recovery (CDR), which is generally used in an embedded clock serial link. From the proposed S-domain model, we prove that DLL-based CDR has superior low-frequency jitter tolerance than PLL-based CDR, whereas, assuming the ideal case, high-frequency jitter tolerance of DLL-based CDR is only a half of that of PLL-based CDR. In addition, the jitter tolerance characteristics of both PLL- and DLL-based CDRs are analyzed in a practical environment. Finally, the consistency of analysis is verified from measurement results using 2.7-Gb/s enhanced reduced-voltage differential signaling (eRVDS) receiver.
Revealing Similarities in Android Malware by Dissecting their Methods

One of the most challenging problems in the fight against Android malware is finding a way to classify them according to their behavior, in order to be able to utilize previously gathered knowledge in analysis and prevention.In this paper we introduce a novel technique that discovers similarities between Android malware samples by comparing fragments of executed traces (strands) generated from their most suspect methods. This way we can accurately pinpoint which (possibly) malicious behaviors are shared between these different samples, allowing for easier analysis and classification.We implement this approach in a tool, StrAndroid, that we evaluate on a few dataset of malware and ransomware samples, comparing its results to an existing similarity tool.
Complementary Coded CDMA Systems With CP-Free OFDM

In a frequency division complementary coded CDMA (FD-CC-CDMA) system, multiple tuners have to be used for multi-carrier modulation, which has a very high complexity and power consumption. In this paper, we propose a CP-free OFDM scheme to implement multi-carrier modulation in an FD-CC-CDMA system, which is named as CP-free OFDM-CC-CDMA system. The transmitter in the CP-free OFDM-CC-CDMA system offers a unique flexibility to adjust the amount of data processed at the same time to ensure that the OFDM block size exceeds the delay spread of a multipath channel, which attributes to effective mitigation of multipath induced inter-symbol interference (ISI). At the receiver of a CP-free OFDM-CC-CDMA system, minimum mean square error (MMSE) equalization and MMSE combining are used to mitigate multi-user interference (MUI) and inter-carrier interference (ICI). The obtained results show that a CP-free OFDM-CC-CDMA system offers a much higher transmission efficiency than FD-CC-CDMA and OFDM-DS-CDMA systems. In addition, the proposed CP-free OFDM-CC-CDMA system outperforms OFDM-DS-CDMA system in terms of bit error rate due to its inherent powerful diversity gain.
Collaborative and Interactive Detection and Repair of Activity Labels in Process Event Logs

Process mining uses computational techniques for process-oriented data analysis. The use of poor quality input data will lead to unreliable analysis outcomes (garbage in - garbage out), as it does for other types of data analysis. Among the key inputs to process mining analyses are activity labels in event logs which represent tasks that have been performed. Activity labels are not immune from data quality issues. Fixing them is an important but challenging endeavour, which may require domain knowledge and can be computationally expensive. In this paper we propose to tackle this challenge from a novel angle by using a gamified crowdsourcing approach to the detection and repair of problematic activity labels, namely those with identical semantics but different syntax. Evaluation of the prototype with users and a real-life log showed promising results in terms of quality improvements achieved.
Event-Triggered Media Stream Bandwidth Adjustment in IoT-Based Home Networks

As the coming maturity of Internet of Things, many home-networked devices with various sensors are deployed in residential environments. It is important to accommodate many devices that compete for bandwidth allocation to transmit data inwards and outwards through a certain home network domain. This paper proposes a novel mechanism which can adjust media stream qualities and transmission bandwidth allocations to different devices. This mechanism is agile against dynamic changes of data workload and finite network resources between inside and outside of a home network. Practical demonstration exhibits that the effects are able to maintain differentiated media transfer services in IoT-based home networks.
An Automotive LiDAR SoC for 240 × 192-Pixel 225-m-Range Imaging With a 40-Channel 0.0036-mm2 Voltage/Time Dual-Data-Converter-Based AFE

This article presents a 40-channel high-resolution automotive LiDAR system-on-chip (SoC), which utilizes the world’s first dual-data converter (DDC). The proposed DDC consolidates the functions of ADC and TDC into a single circuitry and achieves acquisition of both high-precision time and voltage data from the input, realizing a 5<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> smaller analog front-end (AFE) area than prior arts. Such innovations lead us to a 40-channel AFE integration of the SoC without silicon cost increase, which characterizes our LiDAR system with 2<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> higher pixel resolution. To enhance the ADC’s signal-to-noise and distortion ratio (SNDR) without sacrificing the area efficiency, a calibration-free and variation-tolerant voltage-controlled oscillator (VCO)-based ADC with a multiphase pulse density modulator (MP-PDM)-based feedback is proposed. Our proof-of-concept LiDAR system achieves a measurement range of 225 m at 70-klx direct sunlight, with a resolution of 240 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 192 pixels at 10 frames per second (FPS). To quantitatively measure the effect of the resolution improvement of our LiDAR, we evaluate the LiDAR’s detection accuracy of small targets 75 m away. Due to the 2<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> higher vertical pixel resolution, our LiDAR can detect targets with 2<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> smaller height. The 3-D point cloud of the LiDAR captured outdoor in the wild is presented.
Design of Self Tuning PID Controller Based on Competitional PSO

In this work, a new particle swarm optimization (PSO)-based optimization algorithm, and the idea of a running match is introduced and employed in a non-linear system PID controller design. This algorithm aims to modify the formula of velocity calculating of the general PSO method to increase the diversity of the searching process. In this process of designing an optimal PID controller for a non-linear system, the three gains of the PID controller form a particle, which is a parameter vector and will be updated iteratively. Many of those particles then form a population. To reach the PID gains which are optimum, using modified velocity updating formula and position updating formula, the position of all particles of the population will be moved into the optimization direction. In the meanwhile, an objective function may be minimized as the performance of the controller get improved. To corroborate the controller functioning of this method, a non-linear system known as inverted pendulum will be controlled by the designed PID controller. The results confirm that the new method can show excellent performance in the non-linear PID controller design task.
Ultrahigh-Density 3-D Vertical RRAM With Stacked Junctionless Nanowires for In-Memory-Computing Applications

The Von-Neumann bottleneck is a clear limitation for data-intensive applications, bringing in-memory computing (IMC) solutions to the fore. Since large data sets are usually stored in nonvolatile memory (NVM), various solutions have been proposed based on emerging memories, such as OxRAM, that rely mainly on area hungry, one transistor (1T) one OxRAM (1R) bit-cell. To tackle this area issue, while keeping the programming control provided by 1T1R bit-cell, we propose to combine gate-all-around stacked junctionless nanowires (1JL) and OxRAM (1R) technology to create a 3-D memory pillar with ultrahigh density. Nanowire junctionless transistors have been fabricated, characterized, and simulated to define current conditions for the whole pillar. Finally, based on Simulation Program with Integrated Circuit Emphasis (SPICE) simulations, we demonstrated successfully scouting logic operations up to three-pillar layers, with one operand per layer.
Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

Detailed land cover change in multitemporal images is an important application for earth science. Many techniques have been proposed to solve this problem in different ways. However, accurately identifying changes still remains a challenge due to the difficulties in describing the characteristics of various change categories by using single-level features. In this article, a multilevel feature representation framework was designed to build robust feature set for complex change detection task. First, four different levels of information from low level to high level, including pixel-level, neighborhood-level, object-level, and scene-level features, were extracted. Through the operation of extracting different level features from multitemporal images, the differences between them can be described comprehensively. Second, multilevel features were fused to reduce the dimension and then used as the input for supervised change detector with initial limited labels. Finally, for reducing the labeling cost and improving the change detection results simultaneously, active learning was conducted to select the most informative samples for labeling, and this step together with the previous steps were iteratively conducted to improve the results in each round. Experimental results of three pairs of real remote sensing datasets demonstrated that the proposed framework outperformed the other state-of-the-art methods in terms of accuracy. Moreover, the influences of scene scale for high-level semantic features in the proposed approach on change detection performance were also analyzed and discussed.