Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of wind intensity data from reanalysis data using a shallow neural network

Global change is one of the outstanding problems nowadays. This is the reason why considerable attention, and economic resources to monitor climate variables have increased. Wind data constitute one of the key elements that determine the local climate. In this paper, the performance of a shallow neural net (SNN) is tested to simulate remote sensing wind intensity data from reanalysis data from nearby location. As a result, a sequence of wind data with more spatial resolution can be achieved, allowing the availability of more data at the local scale.
A Cavity-Backed Circular SIW Antenna for X-band Applications

A cavity-backed circular substrate integrated waveguide (SIW) antenna is presented in this paper. The proposed antenna is designed to radiate at 10 GHz. It has high power handling capacity along with unidirectional high broadside peak gain of 7.3 dBi due to its cavity-backed structure. By varying the radius of the annular slot, the antenna can be operated throughout the X-band and hence, frequency tunability is achieved with its simple structure. The antenna exhibits a fractional bandwidth (FBW) of 4.1% at its resonant frequency and a front-toback ratio of 15 dB. The proposed antenna is a suitable candidate for X-band high gain unidirectional narrowband applications.
Stackelberg Strategy for Uncertain Markov Jump Delay Stochastic Systems

In this letter, a robust Stackelberg game for a class of uncertain Markov jump delay stochastic systems (UMJDSSs) is investigated. After introducing some definitions and preliminaries, we derive the conditions for the existence of a robust Stackelberg strategy set by means of cross coupled stochastic bilinear matrix inequalities (CCSBMIs), such that the upper bound of each decision maker’s cost function is minimized. To overcome difficulties in solving the CCSBMIs, a feasible numerical algorithm based on the Krasnoselskii iteration sequence is proposed, which consists of linear matrix inequalities and cross coupled stochastic matrix equations (CCSMEs). It is shown that the weakly convergence property is attained. Finally, a practical example is solved to demonstrate the effectiveness and efficiency of the proposed scheme.
Resource Provisioning for Cyber–Physical–Social System in Cloud-Fog-Edge Computing Using Optimal Flower Pollination Algorithm

The rise of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship among humans, computers and physical environment. The key technologies to design CPSS directly related to multi-disciplinary technologies including cyber-physical systems (CPS) and cyber-social systems (CSS). Unfortunately, the design of CPSS is not an easier process because of the network heterogeneity, complex hardware and software entities. At the same time, fog computing is emerged as an expansion of cloud computing which efficiently addresses the abovementioned issue. Resource provisioning is a main technology involved in fog computing. This paper devises a novel fuzzy clustering with flower pollination algorithm called FCM-FPA as a resource provisioning model for fog computing. At the earlier stage, the resource attributes are standardized and normalized. Next, the fuzzy clustering with FPA is developed for partitioning the resources and the scalability of resource searching has been minimized. At last, the presented resource provisioning algorithm based on optimized fuzzy clustering has been devised. The performance of the proposed FCM-FPA model has been tested using a set of two benchmark Iris and Wine dataset. The experimental outcome ensured that the FCM-FPA model has shown proficient results over the compared methods by offering maximum user satisfaction and effective resource provisioning.
Towards Usable Cloud Storage Auditing

Cloud storage security has gained considerable research efforts with the wide adoption of cloud computing. As a security mechanism, researchers have been investigating cloud storage auditing schemes that enable a user to verify whether the cloud keeps the user's outsourced data undamaged. However, existing schemes have usability issues in compatibility with existing real world cloud storage applications, error-tolerance, and efficiency. To mitigate this usability gap, this article proposes a new general cloud storage auditing scheme that is more usable. The proposed scheme uses the idea of integrating linear error correcting codes and linear homomorphic authentication schemes together. This integration uses only one additional block to achieve error tolerance and authentication simultaneously. To demonstrate the power of the general construction, we also propose one detailed scheme based on the proposed general construction using the Reed Solomon code and the universal hash based MAC authentication scheme, both of which are implemented over the computation-efficient Galois field <inline-formula><tex-math notation="LaTeX">$\mathrm {GF}{(2^8)}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi> GF </mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:msup><mml:mn>2</mml:mn><mml:mn>8</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href="xiang-ieq1-2998462.gif"/></alternatives></inline-formula>. We also show that the proposed scheme is secure under the standard definition. Moreover, we implemented and open-sourced the proposed scheme. Experimental results show that the proposed scheme is orders of magnitude more efficient than the state-of-the-art scheme.
A Context-Aware Data-Driven Algorithm for Small Cell Site Selection in Cellular Networks

In mobile networks, detecting and eliminating areas with poor performance is key to optimize end-user experience. In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems to pinpoint problematic locations because the origin of such measurements (i.e., user location) is not registered in most cases. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this paper, a data-driven methodology is proposed to detect the best sites for new small cells to improve network performance based on attributes of connections, such as radio link throughput or data volume, in the radio interface. Unlike state-of-the-art approaches, based on data from only one source (e.g., radio signal level measurements or social media), the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged messages from two social networks (Twitter and Flickr).
SHIP: Storage for Hybrid Interconnected Processors

Drivers for accessing storage are complex. In addition to the complexity involved in using the NVMe protocol, navigating filesystems requires multiple serialized storage accesses and data processing between each access. As a result, efforts to create storage drivers for FPGAs, so that FPGAs can directly access storage without help from a CPU, have either failed, require too many resources/time, or remove some of the functionality expected by storage users (such as removing the filesystem) [1], [2].
A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryption-decryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system.
Fast Machine Fault Diagnosis Using Marginalized Denoising Autoencoders Based on Acoustic Signal

Recently, an increasing popularity of data-driven deep learning research in the field of machine fault diagnosis has been observed. Stacked Denoising Autoencoder (SDA), as a classic type of deep learning method, has been successfully used to learn effective representations for machine fault diagnosis. However, those previous studies always encounter with the inherent limitations of SDA: high computational cost, time-consuming training, and lack of scalability to high-dimensional data. Unfortunately, those limitations can restrict the applicability of those studies in real-world applications, which require timely model upgrade and fast real-time diagnosis. Besides, most previous studies concentrate on the vibration signal, and thus lack the attention towards other kinds of sensor data like acoustical signal. Therefore, to address the two problems above, inspired by the marginalized Stacked Denoising Autoencoder (mSDA), we adopt a variant of SDA for fast fault diagnosis on sound signal. In this way, the required stochastic gradient descent based on back propagation in traditional deep learning methods is replaced by a forward closed-form solution. Opposite to the time-consuming works which demand training thousands of parameters during optimization, this deep architecture only needs to determine a few hyper parameters in advance. To verify the effectiveness and efficiency of the proposal on sound signal, extensive empirical evaluation on a publicly available sound signal dataset of gear fault is carried on. Thorough comparisons with some state-of-the-art faulty diagnosis approaches, confirm the superiority of the proposal in high diagnostic accuracy and lower computational cost.
A study on multiple target resolving with in beam width using super resolution algorithmic approach in phased array radar

Several studies have been proposed in literature discussing the resolvability of signals using several super resolution algorithms. But most of these literatures seldom discuss about the practical implementation issues and some of them are practically infeasible. This paper discusses more practical issues while combining radar signal processing with conventional super resolution technique specifically MUSIC. We are trying to migrate from conventional pencil beam scanning which is commonly used to resolve targets and look upon more practical aspects when we go deep into implementation using super resolution techniques in practical radar environments.
Enhanced Three-Core Three-Mode Optical Transmission System Based on Probabilistic Shaping With Low Complexity MIMO Equalization Algorithm

A novel few-mode multi-core (FMMC) optical fiber transmission system based on probabilistic shaping (PS) with low complexity multiple-input multiple-output (MIMO) equalization method is proposed and demonstrated in this paper. Compared with the traditional FMMC counterpart, the total bit error rate (BER) is reduced by 3 times after 50km fiber transmission with the optical signal-to-noise ratio (OSNR) in the range of 12 to 20 dB. At a bit error rate of forward error correction (FEC) limit, the optical transmission system proposed in this paper outperforms the traditional one by at least 3dB OSNR improvement. When the OSNR is 20 dB and the BER is lower than the FEC limit, the transmission distance is doubled.
Decentralized Continuous Game for Task Offloading in UAV Cloud

UAV cloud which integrates the flexibility and re-silience of mobile cloud computing (MCC) with multiple UAV system provides drones the ability of processing compute-intensive application by offloading task to cloud. However, such task with heterogeneous quality of experience (QoE) requirement generated by massive drones becomes a troublesome burden for cloud resource allocation. Especially the endurance issue related to the energy efficiency makes the problem more complicated. This paper proposes a game theory based decentralized continuous offloading algorithm. Each drone in the UAV cloud optimizes the percentage of offloading task executed at cloud, while minimizes its overhead composed by QoE requirement and energy consumption. This algorithm can be proved to a potential game that can reach a bilateral satisfaction Nash Equilibrium (NE) by finite iteration. Numerical results under various scenario corroborate not only the effectiveness and stability of the proposed continuous offloading game, but also the superiority of computation complexity and communication overhead.
Modeling the Ionosphere Using Extended Spherical Harmonics Approach for Generation of IRNSS/NavIC based Precise Ionospheric Maps/Products (NPIM) over Indian Region

IRNSS (Indian Regional Navigation Satellite System)/NavIC (Navigation with Indian Constellation) is a regional satellite based navigation system over Indian region being developed by Indian Space Research Organisation (ISRO). There are many error sources affecting the navigation performance of satellite based navigation systems. Among them, the error due to ionosphere is the pre dominant source of error especially while working with radio frequencies in real time as well as in post processing mode for areas of ionospheric research. In real time mode, ionospheric error corrections are available to users as broadcast ionospheric corrections for single frequency users and frequency dependent advantage for dual frequency users to correct the ionospheric delay in their line of sight. But there are some applications where precise ionospheric corrections are required in post processed mode for their scientific research and analysis which is a global requirement over equatorial region which is more prone to ionospheric anomalies and high ionospheric variations. Precise ionospheric products in general provide precise total electron content (TEC) values for the identified grid points over the globe at a specified height above the earth considering a thin shell approximation of the ionosphere. These precise ionospheric products are usually generated by monitoring the ionosphere using ground station network ranging to navigation satellites orbiting the earth i.e., GNSS (Global Navigation Satellite System) data. A perfect example is the Global Ionosphere Maps (GIM) generated using IGS (International GNSS Service) stations network. In this paper, effort has been made to generate precise ionospheric products similar to GIM using NavIC ionospheric data for grid points identified over Indian region. A novel technique based on spherical harmonics with an extended polynomial fit is designed for generating NavIC based Precise Ionospheric Maps (NPIM). The generated NPIM over Indian region using this technique is then compared and evaluated with GIM data over Indian region. It is found that NPIM provides better accuracy of 2 to 4 TECU (TEC units) as against 5 to 6 TECU by GIM data over Indian region.
Design and Implementation of Portable Smart Wireless Pedestrian Crossing Control System

Walking is a daily activity that requires an appropriate crosswalk as a key part of the provision of safe transportation infrastructure. In this paper, we design and develop a portable smart wireless control system for pedestrian crossing areas to manage the traffic automatically and allow the pedestrian, like school children, to cross the road safely and effortlessly. The system incorporates the concept of smart sensing to detect the presence of pedestrians and in turn, automatically controls the crosswalk traffic lights. The system composes of two Arduino microcontrollers, two infrared PIR motion sensors, and a bidirectional wireless communication link based on Bluetooth for mitigating wiring installation and transmitting the signal among traffic light units on both roadsides. The system is fabricated and implemented as a portable LED-based traffic light testbed. The developed system is tested and validated in a real environment with a 6 m road width on the university campus. According to the obtained results, the system worked effectively and fulfilled the design criteria where the communication between both sides lights is successfully functioning and the PIR sensors can accurately detect the existence of pedestrians. The developed system is cost-effective, energy-efficient, easy to install, and maintenance-free.
A Comprehensive Survey on Various Machine Learning Methods used for Intrusion Detection System

With the advance in technology, now a day’s cyber-attack is more sophisticated which is not easily detected by the any intrusion detection system (IDS). Since most of the user store their private and sensitive information into the computer or any other digital media so providing security to these computers from the attacker is the essential requirement of each user. As number of intrusion detection system have been proposed in the last few decades. These IDS are mainly classified in two different types named signature based intrusion detection system and anomaly based intrusion detection system. The main objective of this paper is to compare various existing IDS with their strength and weakness. This paper will also discuss various machine learning approach and data sets which are used to detect intrusion. This paper will also discuss various challenges which makes IDS design more challenging.
Information Transfer by Surface-Plasmon-Polariton Wave Propagating on Silver/Silicon Interface

The limitations of both electronic and optical interconnects inside integrated circuits (ICs) may be overcome by interconnects in which information is transferred by pulse-modulated SPP waves guided by the planar interface of a metal and silicon. We simulated the evolution of a pulse carried by an SPP wave guided by a planar silver/silicon interface in the time domain. Evaluation of the Pearson correlation coefficient indicated that information can be transferred over long distances without significant loss of fidelity; thus, SPP-wave-based interconnects can act as effective conduits of information in ICs.
Enhanced Performance of Generalized Energy Based Spectrum Sensing with Adaptive Threshold

In this paper, we employ generalized energy detector (GED) (with parameter, p) for spectrum sensing and study its performance under additive white Gaussian noise with Gaussian primary user statistics. Here, we consider the distribution of test statistics of GED to follow Gamma distribution. To evaluate the performance of GED, we setup the test bench under Gamma distribution of test statistics to obtain probability of detection over probability of false alarm and probability of error (PE) over signal-to-noise ratio (SNR). Later, we optimize the threshold for GED under Gamma distribution for test statistics and obtain a closed form expression for optimal threshold. The optimal threshold is employed to study the performance of GED. The enhanced performance of GED with optimal threshold is observed under Gamma distributed test statistics, and in particular, for GED parameter p = 1 under Gamma distribution. This choice of $p$ consistently provides least PE over SNR under optimized threshold setup. However, without employing optimized threshold, the performance of GED for different values of $p$ is almost similar. In our performance analysis, we choose the number of samples, $N = 10$ which means that the demand for sensing time is less. Further, we study the performance of the GED under the influence of different types of fading channels. Again we observe the performance of GED does not change much for different $p$ values under the influence of fading.
Stability Analysis of Nonlinear Impulsive Stochastic Systems with Lyapunov Indefinite Derivative

The properties of input-to-state stability (ISS), integral input-to-state stability (IISS) and stochastic input-to-state stability (SISS) are discussed for impulsive stochastic nonlinear systems with Lyapunov indefinite derivative in this paper. The Lyapunov time derivative is indefinite, it can be positive or negative, which means it can be applied to a wider range than other ordinary Lyapunov function. Through the use of average dwell-time (ADT) method, the ISS, SISS and IISS properties are derived in the paper. By now, there is no literature on such properties of nonlinear impulsive stochastic systems with Lyapunov indefinite derivatives. In the end, an example is presented to illustrate the validity of this outcome.
Work-in-Progress: Towards an International Data Spaces Connector for the Internet of Things

In the age of digitalization, data protection plays an important role. Data is created, modified and shared over the Internet between data owners and data users. A key issue in this context is the respect of data sovereignty. The International Data Spaces (IDS) Reference Architecture has been developed in order to preserve data sovereignty. In this regard, Internet of Things (IoT) devices, such as sensors, play an important role for providing data. However, an IoT device is not capable of being integrated directly into the IDS by default. We propose an approach to enable the IDS for vendor independent IoT devices, using an open interoperability standard for the IoT specified by the Open Geospatial Consortium (OGC). This allows data owners to benefit from providing their own data while retaining control over it.
Design of a Compact SIW Diplexer with Square Cavities for C-Band Applications

In this paper, a novel design of compact substrate integrated waveguide (SIW) diplexer with square cavities for C-band applications is presented. This diplexer is working on evanescent-mode propagation and operating below the cut-off frequency of the cavity. First, the filtering response of triangular-slot loaded on one square cavity surface is investigated. This response of the cavity is then applied on two cascaded filters to obtain the diplexer characteristics. To validate the concept, a narrow band filter and diplexer are designed and simulated. The insertion losses at lower and upper passbands centered at 6.16 GHz and 7.72 GHz are obtained as 0.83 dB and 0.77 dB, respectively. The proposed diplexer provides a fractional bandwidth more than 5.1% at each passband with high isolation better than 22 dB. These features show that the proposed diplexer is a suitable candidate for C-band application.