Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Four-Level Inverter-Based Dynamic Voltage Restorer With Single DC Power Source

This paper proposes a simplified four-level (S4L) inverter based dynamic voltage restorer (DVR). In the proposed configuration, the dual buck stage can output full, two thirds, and one third of dc link voltage. Including the zero voltage provided by the two-level inverter, the output of the proposed inverter can be four levels. With the same switching frequency, the S4L inverter based DVR achieves better performance than the existing two-level and three-level inverters based DVRs. Moreover, compared with the existing four-level inverter based DVRs, it requires less active switches/diodes and only requires a single DC power source. The experimental results are provided for the validation of the proposed system.
Performance Comparison Between Network Time Security Protocol Drafts: Improvements and Accuracy of the Latest NTS Draft

This paper compares the time synchronization performance of the standard Network Time Protocol (NTP) versus secured NTP using the Network Time Security (NTS) protocol and describes the improvements of the current draft-ietf-ntp-using-nts-for-ntp-17 (NTS-17) compared to draft-ietf-ntp-using-nts-for-ntp-06 (NTS-06) already investigated in [1]. The measurements are based on the implementations of these NTS drafts by the Ostfalia University of Applied Science, both using an NTS-ready NTP version likewise implemented by the Ostfalia, and show the effects of the NTS security mechanisms on time synchronization accuracy.
Energy Efficient Fuzzy Time Slot Scheduling Scheme For Massive Connections

Massive machine-type communication (mMTC) is a typical application scenario of the next generation mobile communication. To minimize energy consumption and keep the system reliable, we propose an energy efficient scheduling scheme. The proposed system offloads the user equipments (UEs) by clustering, and allocates limited resources to the clusters effectively. In the proposed fuzzy time slot scheduling scheme, we adopt sleep mode, grant-free access and the fuzzy time slot to reduce energy consumption. Simulation results show that the proposed scheme can satisfy the requirement of massive machine-type communication and achieve better performance than the contention-based access and time division multiple access (TDMA) in the massive connectivity scenario. Besides, the proposed scheme gets a better trade-off between the probability of collision and the energy consumption.
NVLevel: A High Performance Key-Value Store for Non-Volatile Memory

With the development of byte-addressable non-volatile memory (NVM) technologies, such as Intel/Micron's 3D-XPoint memory and phase change memory (PCM), it is possible to persistently store data in hybrid memory systems consisting of DRAM and NVM. NVM-enabled hybrid memory systems are important to in-memory databases and computing, especially to large-scale in-memory data storage and processing. In this paper, we redesign the cache layer of LevelDB, which is a persistent key-value database based on the LSM-tree, and present NVLevel to provide low latency and high throughput for applications running in NVM-based hybrid memory systems. In particular, we propose two new designs over the LSM-tree. First, we propose Multi-Memtable to offer a write-friendly cache layer for the LSM-tree. Second, we present Hash-Skiplist, which is a hash-based Skiplist that has better read performance than the traditional Skiplist. With the new design of write-optimized Multi-Memtable and read-optimized Hash-Skiplist, NVLevel can achieve high performance both in read-intensive and write-intensive workloads. Our experiments with popular benchmarks including DBBench and YCSB show that NVLevel achieves better write throughput and latency compared to Huge-Memtable, which is the state-of-the-art improvement of the LSM-tree over NVM. Moreover, the overall performance of NVLevel in multi-thread environments is over 2 times higher than that of Huge-Memtable.
UFMC-Based Interference Management for Heterogeneous Small-Cell Networks

In this paper, we apply the advanced waveform universal filtered multi-carrier (UFMC) to a heterogeneous small-cell network, where both the inner-cell interference and the inter-cell interference are considered. By employing UFMC, a dynamic spectrum allocation scheme and a precoding scheme are proposed to mitigate the interference. The proposed UFMC based interference management schemes are analyzed in detail, and the achievable data rate of the proposed scheme is derived. Simulation results show that the proposed UFMC based schemes can efficiently mitigate the interference, and reduce the effect of frequency offsets.
QAM Constellation Division Based User Grouping Transmission for Massive MIMO Uplink Systems

In this paper, a novel QAM constellation division based user grouping communication scheme is proposed for the massive MIMO uplink systems. In the proposed scheme, orthogonal pilot sequences are assigned to adjacent cells to eliminate the pilot contamination between adjacent cells, while the users in the same cell are divided into groups and each group uses one pilot sequence. In each group, a QAM constellation is divided into two PAM constellations for the two users to implement uplink transmission. With the help of the pilot sequences, the base station (BS) firstly estimates the sum channel of each group, and then executes signal detection by adopting the matched filter detector based on the estimated sum channel as well as the asymptotic orthogonality between different channels while the number of BS antennas goes to infinity. Furthermore, the effect of the large-scale fading coefficients of the channels on the average symbol error rate (SER) of all the users is investigated, and an explicit and concrete user grouping approach is proposed to decrease the average SER. Finally, computer simulations illustrate that our proposed communication scheme performs well and the proposed user grouping approach is obviously effective.
GestureVLAD: Combining Unsupervised Features Representation and Spatio-Temporal Aggregation for Doppler-Radar Gesture Recognition

In this paper we propose a novel framework to process Doppler-radar signals for hand gesture recognition. Doppler-radar sensors provide many advantages over other emerging sensing modalities, including low development costs and high sensitivity to capture subtle gestures with precision. Furthermore, they have attractive properties for ubiquitous deployment and can be conveniently embedded into different devices. In this scope, current recognition methods still rely in deep CNN-LSTM and $3D$ CNN-LSTM structures that require sufficient labelled data to optimize millions of parameters and significant amount of computational resources for inference; which limits their deployment. Indeed, subtle gestures recognition is a challenging task due to the high variability of gestures among different subjects. To overcome the challenges in the recognition task and the limitations of the current methods, we propose a shallow learning approach for gesture recognition, that is based on unsupervised range-Doppler features representation, along with a learnable pooling aggregation via NetVLAD. The proposed framework can encode extremely valuable information across time, and results in features that are highly discriminative for hand gesture recognition. Experimentation on publicly available Doppler-radar data shows that the proposed framework outperforms state-of-the-art approaches in terms of recognition accuracy and speed for sequence-level hand gesture classification.
Speeding up LDPC Decoder by Inter-Frame Pipeline for Wireless Laser Communications

In order to meet the requirements of scenarios such as free space optical (FSO) communications using wireless laser, we need to improve the existing decoding structure of LDPC codes to achieve the goal of high throughput and high reliability. Aiming at solving a problem of idle time between layers in the column-serial layered decoder of LDPC codes, based on the dynamic multi-frame processing (DMFP) strategy we proposed a scheme called inter-frame pipeling (IFP) which can utilize the idle time for speeding up the decoder. Compared with the DMFP strategy the IFP has a doubled reduction of decoding delay. Taking a (4008,3507) LDPC code as an example, the field-programmable gate array (FPGA) based implementation shows that the IFP scheme can increase throughput by at least 30%, approximately reaching to 2.5Gbps.
PassDB: A Password Database Using 3D Bloom Filter

A Bloom Filter (BF) is a probabilistic data structure for approximate membership filtering. Besides, the Bloom Filter is applied in diverse Network systems to enhance a system's performance and reduce the memory consumption, for instance, Named-Data Networking, Software-Defined Networks, and Wireless Sensor Networking. Bloom Filter consumes a tiny amount of RAM space to store information of large sets of data. On the contrary, Bloom Filter is unexplored in the password database. In this paper, we present a novel password management system using the 3-Dimensional Bloom Filter (3DBF), called PassDB which features a) low space consumption, b) irrecoverable, c) irreversible, and d) high security. PassDB uses twelve 3DBF to avoid false positive. In addition, we present extremely high accuracy Bloom Filter and the accuracy is 99.99% with false positive of 0.000001882.
FENet/IP: Uncovering the Fine-Grained Structure in IP Addresses

Classifying IP addresses based on network traffic behavior is essential, such as network measurement, network QoS, and network security. Many previous studies have focused on coarse-grained classification, and these do not meet the increasingly diverse needs of applications. In this paper, we introduce FENet/IP, a novel approach for uncovering Internet address structure via learning the continuous feature representation of IP device's connection patterns, employ a combination of low-dimensional embedding and machine learning techniques to probabilistically model IP addresses. Our approach is useful in exposing the fine-grained structural characteristics of the existing Internet address space. Experimental results show the effectiveness of FENet/IP over existing state-of-the-art techniques. In several real-world networks from active IP addresses, we have achieved very high classification accuracy and stability.
The Effects of Self-Regulation in Hippocampus Activation Using Different Types of Real-Time Neurofeedback

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is a non-invasive imaging technique which can extract the concurrent brain state and provide neurofeedback to subjects in an online fashion so that subjects can self-regulate their internal brain state accordingly. In this area, most studies give the continuous NF that is obtained upon every fMRI data acquisition. Although the time resolution of NF signal is maximized, the continuous NF may have some drawbacks. Subjects can be dispersed in the condition of emotion regulation because (1) there is an inherent delay between the hemodynamic response which is caused by neuronal activity and associated NF, (2) there is a limit on the cognitive attention available to simultaneously assess NF and keep with the regulation. In this study, we investigated the differences of the capability to up-regulate activity of hippocampus between groups. The three groups were presented with continuous and intermittent NF and a control group receiving sham NF signal using positive autobiographical memories recalling. Compared with the NF groups, there was no remarkable regulating effect in the control group. The performance of hippocampal regulation in the intermittent group was better than it in the continuous neurofeedback group. In general, we not only showed that the NF was a prerequisite to learn self-regulation of hippocampus activity but also showed that intermittent NF was superior to continuous NF presentation. Training with rtfMRI-NF has promising translational value in future therapeutic process of emotionally related mental disorders.
Pream: Enhancing HPC Storage System Performance with Pre-Allocated Metadata Management Mechanism

With the convergence of high performance computing (HPC) and big data, processing a large volume of scientific data on HPC systems is getting increased attentions. However, supporting these data-intensive workloads on HPC systems that are geared for compute-intensive workloads presents a new challenge in data management. Typical HPC systems consist of a large collection of compute nodes and use parallel file systems (PFSs) for persistent data storage. Although PFSs provide concurrent I/O bandwidth and perform well for large sequential write/read requests, its performance is bottlenecked by expensive metadata operations. Moreover, data-intensive applications with bursty I/O patterns or generate a large number of temporary files exacerbate the shortcomings. In this paper, we propose Pream, a light-weight metadata management framework that aim to address these challenges. Pream targets scenarios of supporting data-intensive workloads that generates a huge number of temporary files on diskless compute nodes. Pream pre-allocates file metadata from the metadata server, and manages these metadata locally to accelerate metadata operations. While newly created temporary files keep residing in PFSs, open/create requests of these temporary files can be handled by Pream locally without connecting with PFSs. Our evaluation demonstrates that Pream can outperform Lustre in many workloads and reduce latency of metadata operation efficiently.
2-Hopper: Accurately Estimate Individual and Social Attributes of Social Networks With Fewer Repeats via Random Walk

Random-walk based sampling is widely used to characterize large graphs by producing samples in the form of nodes. However, existing random-walk based sampling methods only focus on the estimation accuracy of structural properties but suffer from repetitive samples which have adverse effects on obtaining accurate information about the structures over social networks represented by large graphs. Furthermore, these existing methods mainly characterize individual attributes while ignoring the social attributes of the nodes. In this paper, a new random-walk based method, called 2-hop neighbors based random walk or 2-Hopper, is proposed to obtain accurate estimations of both basic and social attributes with fewer repetitive samples. Specifically, 2-Hopper is able to greatly reduce redundant paths among nodes during the sampling process and thus produces few repeats. Based on 2-Hopper’s sampling process, a re-weighted estimator is proposed to accurately obtain both the individual and social properties while the latter is obtained by a newly proposed algorithm. Experimental results driven by real-world datasets show that on average 2-Hopper can reduce 4.5 times repetitive samples of the state-of-the-art random-walk based methods and obtain more accurate information about the individual and social attributes while 2-Hopper is able to estimate the structural properties of these attributes accurately over large graphs.
Effects of 3D Profiles of Adjacent Trench Areas on Rule-Based Etch Bias Retargeting Accuracy for OPC

The shrinkage of critical lithographic feature size keeps introducing new challenges to the standard rule-based etch bias retargeting of Optical Proximity Corrections (OPC). This motivates the use of model-based instead of conventional rule-based etch bias retargeting. On the other hand, model-based retargeting comes with a substantial increase in run-time and complexity. Consequently, pushing the limits of rule-based retargeting can be an optimized solution to that problem [1]. Metrology data of OPC test patterns has always been the base for OPC etch bias rule-based retargeting tables. The accuracy and repeatability of that data is a key factor in the quality of OPC applied. In this work, a comprehensive study of the OPC test patterns is carried out for cross-sections of a substrate active layer for a 110nm node Shallow Trench Isolation (STI)/ Deep Trench Isolation (DTI) technology. We aim at a more reliable understanding of 3D profile effects on the active top of the silicon substrate. Coupled with the standard metrology data, our protocol would lead to a more accurate OPC etch bias rule-based retargeting.
Optimized Design of Combline Filters with Transmission Zeros

The objective of this paper is to demonstrate how inline combline filters can be designed to implement transmission zeros in the filter transfer function. Following the design procedure described in this paper, up to N-1 transmission zeros can be implemented with a filter of order N. In addition to theory, comparisons between simulated and measured results are also presented. The agreement between simulation and measurements is indeed good, thereby fully validating the new combline filter topology, and the proposed design technique.
Three Dimensional Image-Based Radar Cross Section Extrapolation via Planar Projective Transforms

Conventional radar cross section (RCS) measurements require far-field or compact-antenna-test-range (CATR) conditions, which have strict restrictions and high implementation costs. By contrast, RCS extrapolation methods in the near zone are more convenient and practical, which become the research emphasis in recent years. This paper presents a novel RCS extrapolation method based on the combination of near-field 3D synthetic aperture radar (SAR) technique and planar projective transforms (PPT) algorithm. Firstly, to extract the target’s reflectivity distribution, we apply near-field 3D SAR imaging and obtain 3D near-field RCS (NFRCS) images. Secondly, to meet the requirement of RCS measurements, we derive a novel correction factor used to precisely expand 3D NFRCS images. Finally, we extrapolate the plane-wave responses in the azimuth-vertical dimensions, which presents as planar patterns. The proposed method utilizes the complete 3D image-based information to overcome the inherent constraints of classical RCS extrapolation methods on application scenarios, which has the advantages of broad applicability and high flexibility. The detailed derivation and implementation of this method are described in this paper. The simulation and experiment results verify that the proposed method can provide the equivalent CATR result within ±11° azimuth and elevation angles, and it is applicable to precisely measuring the point, surface, and complex scatterers.
Efficient Search of the Optimal Prefix and Suffix Invariant Size for Dynamic Time Warping

Along with the rapid development of information technology and industrial IoT, a large amount of time series data are continuously produced in widespread fields, including medicine, finance, astronomy and so on. Accordingly, time series data mining has widely followed by researchers and time series classification (TSC) has also been attracting great interest over the past decade. Recent empirical evidence has suggested that the combination of the nearest neighbor classifiers (NNC) and the Dynamic Time Warping (DTW) is more efficiently than traditional methods. However, since time series data can not be segmented "perfectly" without human intervention in the raw time series, whose "head" and "tail" will tend to contribute unbalanced influence to the similarity measure, which would produce incorrect classification results. Accordingly, prefix and suffix invariant DTW (ε-DTW) was proposed to provide invariance on endpoints, which can improve the classification accuracy. In this paper, we introduce RAPIDPSISSEARCH: an efficient and exact algorithm to learn the optimal prefix and suffix invariant size (OPSIS). Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiority of our method.
Differentiated Service/Data Migration for Edge Services Leveraging Container Characteristics

The Multi-access Edge Computing (MEC) and Fog Computing paradigms are enabling the opportunity to have middleboxes either statically or dynamically deployed at network edges acting as local proxies with virtualized resources for supporting and enhancing service provisioning in edge localities. However, migration of edge-enabled services poses significant challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture that supports service migration with different options of granularity (either entire service/data migration, or proactive application-aware data migration) across heterogeneous edge devices (either MEC-based servers or resource-poor Fog devices) that host virtualized resources (Docker Containers). The most innovative elements of the technical contribution of our work include i) the possibility to select either an application-agnostic or an application-aware approach, ii) the possibility to choose the appropriate application-aware approach (e.g., based on data access frequencies), iii) an automatic edge services placement support with the aim of finding a more effective placement with low energy consumption, and iv) the in-lab experimentation of the performance achieved over rapidly deployable environments with resource-limited edges such as Raspberry Pi devices.
A New Multipactor Effect Model for Dielectric-Loaded Rectangular Waveguides

Multipactor is an electron discharge that may appear in particle accelerators and microwave devices such as filters, multiplexers, and RF satellite payloads in satellite on-board equipment under vacuum conditions. When some resonance conditions are satisfied, secondary electrons get synchronized with the RF fields, and the electron population inside the device grows exponentially leading to a multipactor discharge. This multipactor discharge has some negative effects that degrade the device performance: increase of signal noise and reflected power, heating of the device walls, outgassing, detuning of resonant cavities, and even the partial or total destruction of the component. The main aim of this investigation is to show a new model for the analysis of multipactor effect in partially dielectric-loaded rectangular waveguides. For this purpose, a CAD software has been developed which allows the simulation and design of these structures out of the operation points in which this undesired multipactor effect occurs, including the main factors that drive it.
Expedited Eviction of Invalid Flow Entries for SDN-Based EPC Networks

The explosive growth in mobile data traffic requires a significant redesign of the existing 3GPP Evolved Packet Core (EPC) network’s data and control planes. As a new type of EPC architecture, Software defined Networking (SDN) has been used in a large number of scenarios due to the fact that it realizes flexible and efficient control of the network, and promotes the evolution of the network to a programmable and scalable direction. However, the transition of traditional networking model to SDN architectures poses scalability issues because of the limitation of Ternary Content Addressable Memory (TCAM) in size. Facing the traffic explosion on future networks with resource-constrained architectures, TCAM storage space is not enough to bear so much traffic flows so that it not only causes performance degradation in data transmission but also results in scalability and cost-efficiency issues. In this paper, we propose an early eviction strategy to evict the invalid flow entries simper and faster to address this issue, based on detecting the message of timeout and the disconnect signaling of Stream Control Transmission Protocol (SCTP). The behavior of detection is achieved by adding a specific SDN rule set with a transparent layer in between the controller and switches, referred to as a SCTP detector. Different with the original solutions, the SCTP detector can delete invalid flow entries in time according to the layer of the transmission disconnection instead of relying on the timeout mechanism. We will also demonstrate the superiority of our proposed solution in reducing the flow entries occupancy and improving the table-miss rate through a series of simulation results.