Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where a BD with nonlinear energy harvesting model cooperates with decode-andforward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secure energy efficiency (SEE). Specifically, the closed-form and   asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmission power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks.  

In this work, the exact solution to the mean value theorem (MVT) problem applied to the maximum power point (MPP) calculation is presented, besides an improvement to this solution is developed based on an expression of the MPP that depends on the optimal load and the characteristic parameters of the photovoltaic (PV) module. Both approximations depend only on the natural logarithm, avoiding the evaluation of the W function. The validation of the approximations is performed by comparing them with real measurements and the numerical solution of the 5-parameter single diode model (SDM) using dataset from 11 commercial modules and 6 different technologies, as well as by comparing them with existing approximations in the literature..

On-vehicle multi-channel Ground Penetrating Radar is attracting much attention in infrastructure maintenance field. Besides the key benefits of highspeed 3D subsurface data acquisition, basic problems such as data discontinuity due to use of multiple antennas and sparse observation that arises aliasing have been a problem. This study proposes easily applicable but effective method to solve these problems. For the first issue, calibration in each channel was done by improved Sparse Blind Deconvolution (SBD) method proposed in previous research. It was found that disuse of low-intensity frequency band provides better estimation of transmitted waves. For the second issue, super resolution method was proposed. It interpolates zeros in between sparsely observed data and then apply synthetic aperture processing. Zero insertion is an operation that restores high frequencies, which makes it possible to restore frequency components above the Nyquist frequency under certain conditions. The method was confirmed to be effective on real data.

Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained T1-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ~6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.

We address the end-effector full-pose tracking control problem in free-floating space manipulators, experiencing constant non-zero linear and angular momentum. The aim is to develop an output-tracking (workspace) control law free of singularities due to parameterizing the end-effector motion and being robust against singularities of the input-output decoupling matrix (generalized Jacobian matrix). Space manipulators are modelled as open-chain multi-body systems with single- and multidegree-of-freedom joints, whose kinematics and dynamics are formulated on the Special Euclidean group SE(3). Such systems exhibit conserved (not necessarily zero) total momentum when operating in the free-floating regime, which we use to systematically reduce their dynamical equations by eliminating the base spacecraft’s motion. To avoid parameterizing the end-effector motion, we consider its full pose as the system output and develop a novel feedback linearization technique on the matrix Lie group SE(3) in the reduced phase space of the space manipulator. We then propose an intrinsic feedforward, feedback proportional-integral-derivative workspace controller involving a coordinate-free pose error function on SE(3) and velocity error on its Lie algebra. Using a Lyapunov candidate, this controller is proven to stabilize the end-effector pose to a feasible desired trajectory. The input-output decoupling matrix in the proposed control law can lose rank at some regions of the configuration space; hence, we implement a singularity-robust inverse, derived from the damped least squares method, to avoid impractical joint torques in these regions. The developed controller is implemented on a 7-degree-of-freedom manipulator onboard a spacecraft and its efficacy and robustness are demonstrated trough series of simulations.

Micro air vehicles can be used for a wide range of applications, which include drone light shows for cultural or marketing purposes, or as a replacement of fireworks. However, widespread use of this technology is precluded by a usability gap between choreography design and deployment on the target drones. In order to achieve seamless deployment of micro air vehicle choreographies, computational interfaces that obtain drone trajectories from a more conventional media such as digital images are needed. In this paper, we propose CrazyKhoreia, a low-cost, computationally efficient approach to Unmanned Aerial Vehicles (in short, UAVs) choreography design, which consists of a robotic perception system that takes a digital image, applies boundary tracing to it and obtains a safely, traversable and apparently accurate waypoint matrix as a result. We validate our trajectory generation system through two modes of operation, light painting, where the UAV can either fly through all of the waypoints or multiple UAVs may be arranged to mimic the original image. The generated trajectories on Light Painting mode by physical robots are compared against the ground truth using a data association tool by the Computer Vision Group at TUM and achieved an average RMSE error of 0.4531 metres, while on Multi-Drone Formation the observed positions are estimated from photographs on actual formations, this way the system reached a mean RMSE error of 0.136 metres.

Two-factor authentication (2FA) is a widely adopted security measure that requires users to provide an additional layer of authentication beyond a username and password in order to access protected resources. While traditional 2FA methods rely on the user to manually enter a code or token, the use of machine learning (ML) has the potential to revolutionize 2FA by enabling automatic and continuous verification of a user’s identity. In this paper, we propose a continuous zero effort 2FA system that uses ML to verify a user’s identity based on the unique characteristics of their environment, including beacon frame characteristics and RSSI values collected from Wi-Fi access points (APs). This system allows for seamless and automatic authentication without requiring any additional input or action from the user beyond their initial login. Additionally, the continuous authentication feature of the proposed system ensures that access to protected resources is maintained only when the user’s two devices are co-located. Through experiments, we demonstrate the effectiveness of the proposed system in determining the location of the user’s devices based on beacon frame characteristics and Received Signal Strength Indicator (RSSI) values. The proposed system offers a convenient and secure solution for 2FA that utilizes the power of ML to enable automatic and continuous authentication. Furthermore, its scalability, flexibility, and adjustability make it a promising option for organizations and users who need a secure and convenient authentication system. Finally, we have included three Python codes in the appendix, which provide further insights into our evaluation and analysis of the proposed system.

The paper is about reducing cyber security incidents or cybercrime where perpetrators take advantage of weak SIM card regsitration and launch attacks on the cyber space. The weak SIM card registration has been sampled from Malawi, but this can happen elsewhere in the world where they have a similar weakness.

This paper provides a limited solution for Lemoine’s conjecture that includes more numbers than previously found on record: 10^9. Lemoine’s conjecture, named after mathematician Émile Lemoine, states that all odd integers greater than 5 can be represented as the sum of an odd prime number and an even semiprime1. Using Python & C++, both popular programming languages often used for automating tasks and general programming, this conjecture can be proved for all values that fall under the criteria up to 10^10. The process of checking each number in Python utilizes math, a standard Python library, along with assisting functions, such as a prime number verification function.

Manuscript submitted to the IEEE Industry Applications Society’s Open Journal of Industry Applications on January 2023. Abstract: Power hardware-in-the-loop (PHIL) is an experimental technique that uses power amplifiers and real-time simulators for studying the dynamics of power electronic converters and electrical grids. PHIL tests provide the means for functional validation of advanced control algorithms without the burden of building high-power prototypes during early technology readiness levels. However, replicating the behavior of high-power systems with laboratory scaled-down converters (SDCs) can be complex. Inaccurate scaling of the SDCs coupled with an exclusive focus on instantaneous voltages and currents at the fundamental frequency can lead to PHIL results that are only partially relatable to the high-power systems under study. Test beds that fail to represent switching frequency harmonics cannot be used for studying harmonic penetration or loss characterization of large-scale converters. To tackle this issue, this paper proposes a harmonic-invariant scaling method (HISM) that exploits the VA rating of preexisting laboratory SDCs for more accurately replicating harmonic phenomena in a PHIL test bench. Firstly, a theoretical analysis of the proposed method is presented and, subsequently, the method is validated with MATLAB simulations and experimental tests.

This paper presents a novel error compensation method for resolvers to improve motor drive performance. It is shown that the nature of the error terms of the position information provided by the resolver is deterministic to a large extent, as they mainly stem from the physical imperfections regarding the resolver and its mounting. This motivates a model-based approach of extracting the error terms and modelling them as the sum of sinusoidal waveforms. Therefore, the resolver output signal can be corrected by the recorded error model without any delay. In the proposed method, the memory requirement and computation burden to the CPU is negligible. Thus, the method is very convenient for practical applications. To determine the error model, hence the compensation parameters, an offline calibration procedure is run once and the extracted parameters can be used for the entire life cycle of the motor as long as the mounted resolver is not removed. Enhanced performance parameters of the motor driver system are verified on a 100kW Internal Permanent Magnet Synchronous Motor (IPMSM) test setup and the results are presented. It is experimentally shown that the proposed compensation algorithm is able to decrease the peak-to-peak position error magnitude from 3.834 to 0.322 degrees (91.6% reduction).

Over long transmission distances, the use of an erbium-doped fiber amplifier (EDFA) reduces attenuation in the information signal. In addition to single-mode fiber (SMF), other dispersion reduction technologies are required today to achieve higher transmission ranges at higher data rates. Some common techniques for reducing dispersion effects in fiber-optic communications are dispersion compensated fiber (DCF) and fiber Bragg gratings (FBG). This paper describes different types of DCF techniques using cascaded uniform and chirped FBGs. The Opti system 7.0 software is used to compare the results of DCF technology for pre, post, and balanced fiber arrangement with and without cascaded FBGs at a rate of 10 Gbps on a 120 km SMF.

Hereby, we present a new design for an integrated wavelength meter. The device works by coupling an unknown monochromatic light source to four microring resonators of different free spectral range containing a phase modulator. The output of each ring is coupled to a separate photodiode (PD). The setup relies on modulating the spectral position of ring resonances while monitoring transmitted power with PDs. Thanks to data acquired during calibration, is possible to relate such output powers to a unique wavelength. While state-of the art designs allow for either high precision or broad range, our system does not require any tradeoff between those two characteristic, achieving up to 8 pm precision (for a ring Q factor equal to 2*10^5) and 100 nm of wavelength range. We will discuss how losses influence the measurement speed and the resolution of the device. The structure, although platform independent, will be realized in InP.

Mobile crowdsensing (MCS) is a promising sensing paradigm which allows users to outsource a range of sensing tasks to a crowd of mobile workers with mobile devices. Location-dependent MCS, as the name implies, is a geographically-dependent sensing paradigm in which service requestors outsource location-specific tasks to many workers with mobile devices, and the workers accepting the tasks collect data at a particular location by physically arriving at the desired locations. Many efforts have been devoted to protecting location privacy of the workers accepting the tasks while ensuring task allocation accuracy and efficiency. In 2022, Wang et al. proposed BSIF, a blockchain-based MCS system (published in IEEE Journal on Selected Areas in Communications (JSAC)) that can prevent not only illegitimate participants but also location privacy leakage. For the purpose of protecting location privacy, they proposed a location-based symmetric key generation algorithm, which coordinates session keys for the target ranges between the service requestor and the workers accepting the tasks without revealing the location information. However, in this paper, we will point out a flaw in their location-based symmetric key generation algorithm, which indicates that BSIF does not work. Furthermore, we propose a novel approach to achieve the location-based symmetric key generation using a witness encryption scheme.

A hardware and software pipeline is created to perform wideband interference cancellation through real-time beamforming.

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments.

A document by He Zhang . Click on the document to view its contents.

Fake News has been spreading widely throughout the world as the booming internet era has started worldwide. Now, more people have access to the Internet than ever, which has led to a significant rise in spreading fake news. So, to solve this issue, it would be highly impossible to manually remove every phony news article. To tackle the above problem of checking on information related to the source, content, or news publisher to categorize it as genuine or fake, we take the help of Machine Learning to classify the information on the web as True or False. Therefore this paper explores the different types of ML classifiers to detect fake news. Therefore, this study will use textual properties of the news dataset we took from Kaggle to distinguish a piece of news as fake or real. Furthermore, with these properties, we will train our model using different ML classification algorithms to evaluate the performance of the dataset collected.