Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things Technology based on LoRaWAN Revolution

With the rapid growth of the internet of Things (IoT) paradigm, another important technology appears on the research field that helps in earning a high performance wireless communication between devices, it is called low power wide area network (LPWAN). In the LPWAN technology, there are two categories: Long Rang (LoRa) and narrowband (NB-IoT). LoRa is not only a long-range network; it is also enables the transfer of information with a low transfer rate and low power consumption. Additionally, LoRa wireless area network (LoRaWAN) is a promising technology developed to overcome the IoT applications challenges. In this paper, the main contribution is focus on the revolution of LoRaWAN network technology considering the IoT requirements. Moreover, we show how this revolution is a powerful candidate in addressing and fixing the IoT challenges and obstacles. Finally, we present how LoRaWAN can handle these problems supporting our discussion with the details of the LoRaWAN characteristics.
Volatile Resistive Switching Memory Based on Ag Ion Drift/Diffusion Part I: Numerical Modeling

Resistive-switching random access memory (RRAM) devices based on filamentary switching are attracting widespread interest for their unique properties, such as high ON–OFF ratio, ultrasteep slope, good endurance, and low-current operation. Recently, volatile RRAMs based on Ag drift and diffusion were also demonstrated for possible applications such as crosspoint array selectors and neuromorphic computing. However, the mechanism of the volatile switching, namely, the spontaneous dissolution of the Ag filament, is still not clear. A deep understanding of the metallic filament formation and spontaneous disruption would strongly help the engineering of the device for optimized performances. Here, we present a numerical physics-based drift/diffusion modeling framework to describe the threshold switching, <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics, and morphological evolution of the metallic filament. The model can support TCAD-type device simulations for scaling, reliability, and variability studies.
Deep Pipelining: Efficient Pipelining of Network Function Chains with Coroutines

Network Functions Virtualization emphasizes the importance of performance in software-based network functions and in-network computing on general-purpose computers. Efficient resource utilization is challenging to run multiple network functions at high performance. This paper proposes a generalized performance optimization technique for chains of network functions on commodity CPUs, named deep pipelining. The key idea of deep pipelining is the combination of software pipelining and coroutines to reduce the overhead of high latency instructions such as memory access of network algorithms. Coroutines are used to schedule the execution of multiple pipelines of chained network functions. Microbenchmark and synthetic benchmark results shed light on both the effectiveness and the small overhead of deep pipelining. By taking the advantage of deep pipelining, we prove the effectiveness in chaining network functions with software-based layer-3 switch implementation consisting of Ethernet switching and IP routing on our operating system. The evaluation demonstrates that deep pipelining improves packet switching and routing performance by 0.11-78% compared to the existing batching-with-prefetch approach.
Energy efficient resource allocation algorithm in multi-carrier NOMA systems

In this paper, we studied the joint subchannel and power allocation problem to maximize energy efficiency (EE) for the multi-carrier non-orthogonal multiple access (MC-NOMA) systems. First, a matching algorithm is proposed to allocate the user in the subchannel. Then, the EE maximization problem is converted into a series of subproblems, which can be solved by the penalty function method. Next, the joint channel allocation and power allocation is proposed based on the Dinkelbach algorithm. Simulation results show that the proposed algorithm is superior to the traditional fractional power allocation (FTPA) scheme and the OFDMA scheme in terms of the EE.
SDN/legacy Hybrid Network Control System

The widespread replacement of traditional network to SDN is still restricted in the enterprise environment, and hybrid network where SDN and legacy network appliances coexist by necessity complementing each other is deployed usually. To answer the network parts integration issues, the hybrid network control system is developed and discussed in the present work. Thorough network topology assessment (BDDP complemented with LLDP/SNMP based legacy network discovery) and the possibility of converting SDN flow rules into the configurations of legacy appliances enables to orchestrate hybrid network as well as to enforce the network security.
High-Resolution Adaptive 3-D Urban Reconstruction Using Frugal Polarimetric Tomographic SAR Focusing Techniques

This paper addresses the 3-D urban reconstruction using a minimal number of acquisitions, i.e. only 3 acquistions. To overcome the limitations of conventional tomographic techniques encountered in the urban characterization, a polarimetric model-adaptive tomographic estimator, FP-SSF, is proposed, and solved by the proposed polarimetric alternating projection technique. Meanwhile, a new solution for FP-DML technique is derived as a variant, whose performance is evaluated using simulations and compared to the one of FP-SSF estimator. Moreover, the efffectiveness of FP-SSF estimator is demonstrated by using a dual-baseline PolInSAR data set acquired by DLR’s ESAR sensor at L-band over urban areas of Dresden. The results are validated against the ground truth derived from LiDAR data.
A New Filter Topology for Multi-Paralleled Grid-Connected Inverters

The resonance frequency of the multi-paralleled grid-connected inverters with LCL filter may be affected by the parallel number due to the impact of the grid impedance, which may deteriorate the performance of the active damping methods aimed at fixed resonance frequency. In this paper, a new topology is proposed using common capacitors to acquire a fixed resonance frequency. Furthermore, a passive damping design is realized based on this topology which greatly reduces the number of damping devices compared with conventional passive damping strategy. Finally, simulations are performed to verify it.
Real-Time Particle Spectrometry in Liquid Environment Using Microfluidic-Nanomechanical Resonators

Hollow nanomechanical resonators represent a promising technique for particle spectrometry, as their design allows highly sensitive particle mass sensing in liquid environments by putting together the good mechanical behavior of a nanomechanical resonator vibrating in vacuum or gas environment with physiological compatibility of liquid environments for biological applications. Nevertheless, for real-world practical applications these sensors require not only a high mass sensitivity but also a high-throughput particle flow. In this work, we use a fast-response and low-cost hollow nanomechanical resonator which let us measure up to 10 particles per second. However, this unprecedented particle velocities brings new implications related to the entanglement between the mechanics and the microfluidics in this structure. We realized that the measured particle masses depend on the fluid velocity. The study of this phenomenon demonstrates the need to introduce a correction factor in mass sensing dependent on particle velocity.
Model Predictive Control Based Energy Management of a Household Microgrid

Pushed by sustainable policies, energy engagement and cost reduction, the installed photovaltaic (PV) capacity has a rapidly growth among householders. However, challenges that come with it are the management of excess PV and improvement of economic benefit. This paper modeled a grid-connected residential microgrid composed of PV, battery, water tank, cleaning pump, HVAC, lighting and other electricity usage. An advanced energy management strategy based on model predictive control (MPC) is proposed to redistribute power consumption, achieve minimization of energy cost and improve self-consumption ability through load shifting. A penalty coefficient that is large but decreasing with time can be added to the MPC cost function to improve self-consumption ratio if there is no feed-in tariff. Simulation results are given to verify the effectiveness of the proposed strategy. Finally, the microgrid platform has been established, which applied open platform communication (OPC) technology to ensure the real-time operation of MPC control. Experiment results demonstrate the validation and feasibility of the proposed MPC based strategies.
Localization and Navigation in Autonomous Driving: Threats and Countermeasures

With the growing number of research studies on autonomous driving, a lot of new technologies for self-driving cars are also constantly emerging. While significantly promoting the development of autonomous vehicles, these emerging technologies also have potential vulnerabilities, which may bring up some nonnegligible security threats. Loopholes existing in localization and navigation technologies, which are the key elements of route planning and navigation, could be utilized by adversaries to manipulate autonomous driving navigation by hijacking valuable vehicles, goods, or even target characters. Toward this end, we provide in this article a basic guide summarizing the security threats and attack schemes, and we propose countermeasures specifically for localization and navigation technologies in autonomous vehicles. After that, we present a novel route spoofing attack that could also be implemented against future autonomous driving navigation. With such implementation, we demonstrate that effectively, routes of targeted drivers can be usurped and hence mislead drivers to take a wrong route, go by a malicious place, and even be tracked in real time.
Quality Determination by Using Support Vector Machine in Gas Welding Applications

The robots used in the manufacturing industry and the sensors from the automation system can be used to automatically perform quality checks. Gas welding robots can operate autonomously, but quality controls are carried out manually by means of laboratory tests. In this study, a method which can work fast in real time quality control applications is proposed by using the data obtained from the robots used in the production system. In this study, comparison of other classification algorithms which can be used in this field has been made. First of all, sensor data on the robots and production system were taken and quality control of the product at the end of the process was made and the entire process was classified. The processes in the obtained data were analyzed as raw data and statistical values were examined. Support Vector Machines, Decision Trees, Random Forests and Logistic Regression algorithms are used to classify the data. The algorithms used in the data set were successfully applied and a success rate of 87% was obtained with the Support Vector Machines.
Revisiting User Privacy for Certificate Transparency

Public key infrastructure (PKI) based on certificate authorities is one of the cornerstones of secure communication over the internet. Certificates issued as part of this PKI provide authentication of web servers among others. Yet, the PKI ecosystem is susceptible to certificate misissuance and misuse attacks. To prevent those attacks, Certificate Transparency (CT) facilitates auditing of issued certificates and detecting certificates issued without authorization. Users that want to verify inclusion of certificates on CT log servers contact the CT server directly to retrieve inclusion proofs. This direct contact with the log server creates a privacy problem since the users' browsing activities could be recorded by the log server owner. Lueks and Goldberg (FC 2015) suggested the use of Private Information Retrieval (PIR) in order to protect the users' privacy in the CT ecosystem. With the immense amount of certificates included on CT log servers, their approach runs into performance issues, however. Nevertheless, we build on this approach and extend it using multi-tier Merkle trees, and render it practical using multi-server PIR protocols based on distributed point functions (DPFs). Our approach leads to a scalable design suitable to handle the increasing number of certificates and is, in addition, generic allowing instantiations using secure accumulators and PIRs. We implement and test this mechanism for privacy-preserving membership proof retrieval and show that it can be integrated without disrupting existing CT infrastructure. Most importantly, even for larger CT log sizes of 2^31 certificates, the performance overhead is less than 9 milliseconds in total.
Templated Self-Assembly of Microcomponents Using Water-Oil Interface

We report a method to construct a three-dimensional integrated structure of microcomponents (MC) using water/oil (W/O) interface as a template. By forming a W/O interface at a specific part of a template created by 3D stereolithography to define the size and shape of the area to which MCs adsorb, it became possible to form an integrated structure of MCs, such as photofabricated parts and microbeads, with a high degree of freedom. We also demonstrated immobilization of MCs on a template using a photo-crosslinking gel in an aqueous solution, and transfer to a solid substrate as methods of handling regularly integrated patterns of MCs.
Research on Digital Predistortion of Power Amplifier Based on Innovation Adaptive Kalman Filter

In this paper, the adaptive digital predistorter (DPD) based on full Volterra (FV) series is studied for nonlinearity compensation of power amplifier (PA). In order to overcome the weakness of the traditional Kalman filter (TKF), we propose an adaptive Kalman filter (AKF) algorithm based on innovation which does not require prior knowledge of measurement noise. The innovation is introduced into the Kalman filter to correct the Kaman gain by calculating the maximum likelihood optimal estimating of the innovation variance. With this method, the proposed scheme can effectively suppress the measurement noise and reduce the adverse effect of noises on the accuracy of coefficients identification. The simulation results show that the proposed AKF algorithm can effectively track the change of actual noise, and the accuracy of coefficients identification in noise environment is improved by about 20%-50% than that of TKF. Furthermore, the proposed AKF has a better performance in terms of power spectral density and bit error rate (BER) than TKF, which demonstrates the prospective and validity in nonlinearity compensation for PA.
Mosaic-Like Monolayer RGO/AG Film via Ultrafast Two-Dimensional Assembly for High Performance Room-Temperature Gas Sensor

In this paper, we present a novel resistive room temperature NO2 gas sensor. The device is fabricated through precisely assembling 2D mosaic-like reduced graphene oxide/silver (RGO/Ag) composites, on the interdigitated Cr/Au electrodes. The obtained sensor has ultrahigh sensitivity compared with the previously published gas sensor. Significantly, it recovers fully and fast to the initial state at room temperature without any external power treatment, like heating or irradiation. The remarkable performance of the sensor, achieved with a facile fabrication process, has a significant influence on the future industrial gas sensing devices.
PV- and Battery-Ratio for very large Modular PV Parks with DC Coupled Battery Converters

Dropping storage and PV panel prices are benefiting large PV projects in the megawatt range with integrated battery storage systems (BSS). This paper introduces a modular design approach such for large PV-battery parks. With this approach a dispatchable generation for arbitrary load-profiles for volatile PV power by means of batteries is achieved. The park is comprised of multiple central inverters, which can have differently sized DC coupled storage systems. The focus of this paper lies on the trade-off between the oversizing of the PV array and the dimensioning of the storage system, so that the delivered energy at the point of common coupling (PCC) of every central inverter has less to no volatilities. A tool, which uses PV UI curves of one year for four different locations to find a ratio between the installed PV panel power and the battery capacity for every desired power duration, has been developed. A distinction between limited and unlimited charging power is made and the dependency of the nominal capacity of both parameters to the equivalent full load hours is evaluated. In addition, the influence of the sizing of the PV array and the battery on the dc-link voltage and filter selection is investigated.
Private Verification for FPGA Bitstreams

Abstract : We introduce private verification, a novel paradigm for trustworthy microelectronics design verification. Private verification methods and software simultaneously meet two requirements: (1) comprehensively verifying the design and (2) maintaining the privacy of certain aspects of the design, such as its implementation details or design format. We present an implementation of such a tool, entitled PV-Bit, which is capable of verifying the contents of FPGA bitstreams without exposing the details of the vendor-proprietary bitstream format or posing other security risks.
Intelligence Enabled SDN Fault Localization via Programmable In-band Network Telemetry

Intelligent Fault localization for SDN becomes one of the most critical but difficult tasks. This paper proposes a new approach called Policy-Aware In-band Network Telemetry (PAINT) to tackle SDN fault localization. In the PAINT system, network operators define and deploy network services using a high-level Service Provisioning Language (SPL). Then, PAINT automatically parses the service policy to infer the causal relationship between service related network components and (end-to-end) service-level observable symptoms. Based on the causality model, PAINT deploys monitoring instruments for the symptoms. PAINT utilizes a dynamically created Symptom-Fault-Telemetry model to incorporate In-band Network Telemetry (INT) actions systematically into the fault reasoning process to improve the efficiency and accuracy of fault localization for SDN. PAINT has been extensively evaluated in a simulation environment for its accuracy and scalability with very positive results.
On Redundancy Reduction of Non-Recursive Second-Order Spectral-Null Codes

The code design problem of non-recursive second-Order Spectral Null (2-OSN) codes is to convert balanced information words into 2-OSN words employing the minimum possible redundancy. Let <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> be the balanced information word length. If <inline-formula> <tex-math notation="LaTeX">$k\in \,\,2 {{I}}{{N}}$ </tex-math></inline-formula> then the 2-OSN coding scheme has length <inline-formula> <tex-math notation="LaTeX">$n=k+r$ </tex-math></inline-formula>, with 2-OSN redundancy <inline-formula> <tex-math notation="LaTeX">$r \in \,\,2 {{I}}{{N}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$n\in \,\,4 {{I}}{{N}}$ </tex-math></inline-formula>. Here, we use a scheme with <inline-formula> <tex-math notation="LaTeX">$r=2 \log k+\Theta (\log \log k)$ </tex-math></inline-formula>. The challenge is to reduce redundancy even further for any given <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. The idea is to exploit the degree of freedom to select from more than one possible 2-OSN encoding of a given balanced information word. To reduce redundancy, empirical results suggest that extra information <inline-formula> <tex-math notation="LaTeX">$\delta _{k}=0.5 \log k+\Theta (\log \log k)$ </tex-math></inline-formula> is obtained. Thus, the proposed approach would give a smaller redundancy <inline-formula> <tex-math notation="LaTeX">$r^{\prime }=1.5 \log k+\Theta (\log \log k)$ </tex-math></inline-formula> less than <inline-formula> <tex-math notation="LaTeX">$r=2 \log k+\Theta (\log \log k)$ </tex-math></inline-formula>.
Strategic Value Creation through Big Data Analytics Capabilities: A Configurational Approach

Despite the documented potential of Big Data Analytics Capabilities (BDAC), it is by no means clear how they support the capacity of firms to purposefully create, extend, or modify their resource bases, i.e., dynamic capabilities (DC). This study extends current literature by exploring and elucidating various contingent big data capabilities, resources, and conditions that lead to the formation of these DCs in today's turbulent business environment. We use a qualitative approach using a cross-interview study method. Hence, we collected data through semi-structured interviews with field domain experts. In total, 27 interviews were held with key and senior informants from different firms. Co-authors analyzed the obtained data through the use of qualitative coding techniques. Our results show that there are various contingent BDAC resource solutions that drive, moderate, and condition the development of DCs. These outcomes also show that no single antecedent condition explains DCs in practice. These insights are important for firms that are becoming more data-driven. Outcomes are valuable for practice as firm executives now have insight into the process and main BDA capabilities they can focus on while planning, initiating, and evolving big data analytics projects and their digital business strategies.
DroidEvolver: Self-Evolving Android Malware Detection System

Given the frequent changes in the Android framework and the continuous evolution of Android malware, it is challenging to detect malware over time in an effective and scalable manner. To address this challenge, we propose DroidEvolver, an Android malware detection system that can automatically and continually update itself during malware detection without any human involvement. While most existing malware detection systems can be updated by retraining on new applications with true labels, DroidEvolver requires neither retraining nor true labels to update itself, mainly due to the insight that DroidEvolver makes necessary and lightweight update using online learning techniques with evolving feature set and pseudo labels. The detection performance of DroidEvolver is evaluated on a dataset of 33,294 benign applications and 34,722 malicious applications developed over a period of six years. Using 6,286 applications dated in 2011 as the initial training set, DroidEvolver achieves high detection F-measure (95.27%), which only declines by 1.06% on average per year over the next five years for classifying 57,539 newly appeared applications. Note that such new applications could use new techniques and new APIs, which are not known to DroidEvolver when initialized with 2011 applications. Compared with the state-of-the-art overtime malware detection system MAMADROID, the F-measure of DroidEvolver is 2.19 times higher on average (10.21 times higher for the fifth year), and the efficiency of DroidEvolver is 28.58 times higher than MAMADROID during malware detection. DroidEvolver is also shown robust against typical code obfuscation techniques.