The commercial value of web applications has significantly increased in recent years. They progressed from simple information-sharing platforms to more sophisticated business applications. Web-based apps, unlike most other technologies, are always accessible from anywhere in the world. This makes them ideal targets for malevolent cyberattacks. Scanners can identify and mitigate an organization’s vulnerabilities. However, without a thorough awareness of a system’s weaknesses, it would be difficult to undertake effective network defense in order to keep intruders out in the real world. As a result, vulnerability scanning is an important part of a cybersecurity curriculum’s success. In this paper, we’ll look at the present state of open-source vulnerability scanning technologies. A literature review of vulnerability assessment and reporting, vulnerability scanning, vulnerability scanning technologies, security vulnerabilities, system and application security, and malicious cyber-attacks reveals that a lot of work is being done in this area. This research provides an in-depth examination of vulnerability scanning technologies. In this paper, we covered two important topics: vulnerability scanning and reporting. Then, after identifying gaps in relevant practices and presenting chosen findings, we emphasize future directions and bring this study to a conclusion. The top open-source network vulnerability scanning tools are described in detail.

This paper analyses noise propagation in positron emission tomography (PET). It presents a method to compute the effective signal-to-noise ratio provided by the time-of-flight (TOF) measurement. The proposed method can be used to compare TOF-PET systems with different and possibly event-dependent TOF-kernels, as encountered when prompt photons, such as Cherenkov photons are present, or when the detector is composed of different scintillators.

In Newton’s theory, all mass gravitates in an isotropic (spherical) manner. In this paper, we will consider aspherical – anisotropic – gravitating processes, which leads to a unique view of dark matter: dark matter is a graviton condensate. We also discuss dark energy, and the possibility of a final, 5th interaction. This paper discusses some physical phenomena, including the effects of dark matter and dark energy. A unified table of interactions is given.

16-ary amplitude phase shift keying (16APSK) is a candidate modulation for aeronautical mobile telemetry because it has better spectral efficiency and lower sensitivity to adjacent channel interference compared SOQPSK-TG. This paper compares the post-equalizer bit error rate performance of 16APSK and SOQPSK-TG in the aeronautical mobile telemetry multipath channel. The linear polarizations produced by the orthogonal dipoles in the receive antenna feed are combined in two different ways: using a 90◦ hybrid coupler to produce RHCP or LHCP and maximum likelihood combining. The first combining method is the common practice at test ranges and the second combining method is new. The results show the following. First, 16APSK with MMSE equalization outperforms SOQPSK-TG with CMA equalization for the RHCP and LHCP channels. Second, 16APSK with MMSE equalization outperforms SOQPSK-TG with CMA equalization for the maximum likelihood channels at moderate to high signal-to-noise ratios. For lower signal-to-noise ratios, SOQPSK-TG shows the best performance. Third, 16APSK with MMSE equalization operating the maximum likelihood combined channel outperforms SOQPSK-TG with CMA equalization operating on the RHCP or LHCP channels.

Main memory system design and corresponding technology requirements have become increasingly challenging for data-dominated high-performance applications. To address the leakage and scalability issues of the conventional DRAM-based memory, new memory technologies with ultra-low leakage and potential for high scalability have been explored extensively over the last decade. However, none of them are mature enough to serve as a drop-in replacement for DRAM. In this paper, we propose a hybrid main memory system solution for utilizing new memory technologies with specific features, based on the target application characteristics and system configuration. To this end, we examine two new memories, 1S-1VCMA and IGZO-based DRAM, along with conventional DRAM in the context of hybrid main memory solutions for high-capacity and low-power Pareto-optimizations, respectively. To better evaluate the power and performance, we consider the page-fault modeling in our evaluations. The results of the simulation show that different combinations of memory technologies in the hybrid memory system, different memory capacities, and different storage systems could provide a promising solution in the system regarding the characteristics of running applications and the requirements of the system.

In this paper, we develop a data-driven performance-based evaluation framework for a novel electric vertical takeoff and landing aircraft (eVTOL) in the context of Urban Air Mobility (UAM) applications. First, a two-stage comprehensive simulation framework is developed to generate a benchmark database for the performance evaluation of both UAS Traffic Management (UTM) algorithms and high-fidelity eVTOL dynamical models. In the developed simulation framework, we implement UTM algorithms and incorporate real-world constraints, e.g., vertiport infrastructures and different wind conditions. From the developed simulation framework, we generate 1,213,010 flight profiles. These flight profiles are used in a model based eVTOL performance evaluation tool as inputs to compute the physical performance of 3 types of eVTOLs. Due to the high computational cost of model-based eVTOL performance evaluation approaches, a clustering-based sampling procedure is employed to reduce the redundancy in the generated flight profiles and utilize the re-sampled flight profiles to form an eVTOL performance analysis dataset. We then train and compare several machine learning models on the eVTOL performance analysis dataset to predict: performance variables-flight conditions, aerodynamic coefficients, aircraft electronics, electric motor and propeller efficiencies. Finally, we deploy the proposed data-driven models in the framework and reduce the eVTOL performance inference time to real-time. The implementation of the proposed framework can be found on GitHub: https://github.com/mrinmoysarkar/eVTOL_performance_evaluation.git

In-Memory Computation(IMC) of Neural-Network(NN) inference is done by performing the Multiply-ACcumulate(MAC) operation in the analog domain. Parallelly digitising MAC voltages by fitting Analog to Digital Converters(ADCs) within dense memory-pitches is a fundamental challenge for IMC engines. IMC works thus far rely on clipping the MAC-PDF to reduce the dynamic range, reducing the per data-line(DL) ADC precision requirement. In this work, we show that the per-DL ADC precision can be reduced even further by focusing on quantising the input Conditioned MAC-PDF(CMPDF), which spans a sub-range in the total MAC-PDF. We demonstrate on hardware a technique to locate the CMPDF in one-shot by tracking its mean. We show that quantisation levels about the CMPDF mean can be skewed to only span the portion of CMPDF that yields positive ReLU inputs, provided MACs are implemented as complete sums. Compared to symmetrically spanning CMPDF, this requires 20% to 40% fewer references at iso-accuracy for the investigated neural network layers. Hardware measured results for Fully-Connected NN inference on MNIST yielded < 1% accuracy drop when MAC-voltages were quantised with 4 bit references about the CMPDF mean as compared to full-range 6 bit ADC sensing the MAC-voltages. MATLAB evaluation using 3.5 to 4.2 bit CMPDF quantisation for MNIST-FCNN, CIFAR-10 Resnet-20 and VGG-11 inference yielded < 1% accuracy drop as compared to full-range 6 to 7 bit MAC quantisation.

We propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model.

For magnetic field calculations, cylindrical permanent magnets are often approximated as ideal, azimuthally symmetric solenoids. Despite the frequent usage of this approximation, research papers demonstrating the validity and limitations of this approach are scarcely available. In this paper, the experimentally derived magnetic field of a cylindrical permanent magnet is compared with the analytically calculated magnetic field of an ideal solenoid. An experimental setup for measuring the magnetic field distribution is demonstrated and employed for gathering the data. The proposed setup allows to measure the distributions of the axial and radial components of the magnetic field surrounding the magnet. The experimental data is in a very good agreement with the theoretical predictions, confirming the validity of using the model of an ideal solenoid for predicting a magnetic field distribution of a permanent magnet.

A document by Chengjun Li . Click on the document to view its contents.

This paper investigates the performance of high altitude platforms (HAPS) based free-space optical (FSO) communication links including HAPS-to-ground station (downlink), ground-to-HAPS (uplink) and HAPS-to-HAPS (horizontal link) communications. The effects of  attenuation loss, atmospheric turbulence, pointing error and angle-of-arrival (AOA) are taken into account. Also, the application of adaptive optics correction, one of the most effective turbulence mitigation techniques, is analyzed using the Zernike polynomials representation. Closed-form expressions are obtained for probability density function (PDF), cumulative distribution function (CDF), Rytov variance, adaptive optics filter function and outage probability mainly in terms of Meijer’s G function when both no adaptive optics correction is used and adaptive optics correction is applied. Some selected results are presented depending on the various  parameters such as the HAPS altitude, the ratio of vertical and horizontal deviations, beam waist, Zenith angle, height of ground station, receiver aperture diameter, channel state threshold and wind speed. The performance improvement with adaptive optics correction is investigated by removing different Zernike modes. We show that the downlik performance outperforms the uplink, the performance of the horizontal link sharply increases above certain altitude and communication links benefit from the adaptive optics correction up to a certain level in terms of performance improvement.

This paper investigates the performance of a non-orthogonal multiple access (NOMA) enabled full-duplex (FD) device-to-device (D2D) communication network that is underlaid in the cellular uplink. The uplink cellular user is licensed to serve the BS. For improving spectrum utilization efficiency, it also concurrently relays D2D information in FD mode. The transmit powers of D2D nodes are chosen such that the cumulative interference at the BS is below the Interference Temperature Limit (ITL). Using the statistical channel knowledge and successive interference cancellation at the D2D nodes, an expression for the D2D throughput is derived in closed-form. Thereafter, using the approximate throughput expressions, a closed-form expression of ITL apportioning parameter is derived in closed form that maximizes the D2D throughput while ensuring desired quality of service at the BS. Computer simulations demonstrate accuracy of the derived expressions.

This paper provides a definition of back-propagation through geometric correspondences for morphological neural networks. In addition, dilation layers are shown to learn probe geometry by erosion of layer inputs and outputs. A proof-of-principle is provided, in which predictions and convergence of morphological networks significantly outperform convolutional networks.

Intensity-modulation and direct-detection (IM/DD) transmission over short-reach optical fiber links, require electronic dispersion compensation (EDC) at the transmitter and/or electronic equalization at the receiver. Recently, the iterative Gerchberg-Saxton (GS) algorithm was demonstrated for EDC in IM/DD systems, through treating the amplitude at the transmitter and the phase prior-to the direct detection receiver as a degree of freedom. In Part I of this work, three GS approaches using finite impulse response (FIR) filters for EDC in IM/DD systems are demonstrated. The first two are closely related and rely on a cascaded FIR structure, while the third offers a novel noniterative EDC solution using a single GS optimized static FIR filter. This is achieved through decoupling pattern dependent aspects of transmission from the GS iterations by targeting a single impulse at the DD receiver. With every successive iteration an impulse response for the GS filter emerges and sets the FIR tap weights. It is also demonstrated that closed-form analytical expressions for the GS filter impulse response can be obtained through small-signal frequency-domain analysis. The FIR filter is simulated using 8-bit finite-precision arithmetic. An adaptive T spaced post feed-forward equalizer (FFE) is utilized for mitigating residual chromatic dispersion. It is shown, that a T/2-spaced pre-EDC FIR filter with 417 taps can support 56 Gb/s non-return-to-zero (NRZ) on-off keying (OOK) transmission over 80 km of single mode fiber (SMF) with a chirp-free Mach-Zehnder modulator (MZM). Part II of this work, presents experimental demonstration of the non-iterative GS FIR filter proposed and simulated in this article.

We analyzed the widely popular UTKFace dataset to implement a Convolutional Neural Network (CNN) algorithm, used to predict gender given an image of the human. Over the course of the experiment, we observed various statistics of the dataset, to give a strong representation of what could drive the results our model returned. By further investigating the current policy as it comes to data collection and privacy, we hope to suggest practices that will lead humanity to a safer, more equal future. Through the analysis of images from popular datasets, optimization of machine learning models, and the changing information industry standards, we aim to find and assess a difference in accuracies of gender-prediction models when it comes to race.

In this manuscript, an innovative method for the detection and the estimation of multiple targets in a radar system employing orthogonal frequency division multiplexing is illustrated. The core of this method is represented by a novel algorithm for the detection of multiple superimposed two-dimensional complex tones in the presence of noise and the for estimation of their parameters. This algorithm is based on a maximum likelihood approach and combines a single tone estimator with a serial cancellation procedure. Our numerical results lead to the conclusion that the developed method can achieve a substantially better accuracy-complexity trade-off than various related techniques in the presence of closely spaced targets.

This paper is about preserving the Cloud data whose idea is to cure everyday problems with computing. Cloud computing is fundamentally pooling the resources virtually and the resources like storage are provided to the end-users through the web. Information preservation, security, uniform quality, and interoperability are some issues related to cloud computing. However, the most essential issue is Security and how it is ensured by a cloud supplier. Security of data can be offered by means of cryptography. This paper presents an improved composite data protection mechanism to protect data on the cloud from illegitimate access.

Compared to a long-stator linear synchronous motor (LLSM), a long-stator doubly-fed linear motor (DFLM) exhibits two distinct advantages for high-speed maglev application: 1) It has two additional degrees of freedom with both the frequency and the phase angle of the mover to improve its control accuracy and dynamic responses; 2) It can use the slip power to feed the mover contactlessly and directly. In order to achieve the propulsion, the levitation, and the contactless power transfer through the common air-gap magnetic field, the “quasi-synchronous operation (QSO)” principle of the DFLM is proposed for high-speed maglev train. Based on the DFLM modeling and distinctive constraints for maglev train, the operation point analysis of the performance evaluation is further investigated. To validate the feasibility of the DFLM application for high-speed maglev train, a unit DFLM prototype is designed and tested, and the corresponding simulation studies of the QSO control structure are investigated.