arXiv subjects

Wen Hu

Publications and source records attributed to Wen Hu.

At least 73 records · Page 4Linked to original sources

arXiv2023

Simulating time-harmonic acoustic wave effects induced by periodic holes/inclusions on surfaces

This paper introduces the first attempt to employ a localized meshless method to analyze time-harmonic acoustic wave propagation on curved surfaces with periodic holes/inclusions. In particular, the generalized finite difference method is used as a localized meshless technique to discretize the surface gradient and Laplace-Beltrami operators defined extrinsically in the governing equations. An absorbing boundary condition is introduced to reduce reflections from boundaries and accurately simulate wave propagation on unclosed surfaces with periodic inclusions. Several benchmark examples demonstrate the efficiency and accuracy of the proposed method in simulating acoustic wave propagation on surfaces with diverse geometries, including complex shapes and periodic holes or inclusions.

math.NA↗

arXiv2023

Two-injection scenario for the hard X-ray excess observed in Mrk 421

An interesting result recently reported for Mrk 421 is the detection of a significant excess at hard X-ray energies, which could provide useful information for investigating particle acceleration and emission mechanisms in the relativistic jet. Considering a two-injection scenario, we develop a self-consistent one-zone leptonic model to understand the origin of the hard X-ray excess in Mrk 421 during the period of extremely low X-ray and very high energy (VHE) flux in 2013 January. In the model, two populations of mono-energetic ultrarelativistic electrons are injected into the emission region that is a magnetized plasmoid propagating along the blazar jet. We numerically calculate the emitting electron energy distribution by solving a kinetic equation that incorporates both shock acceleration and stochastic acceleration processes. Moreover, we infer analytic expressions relating electrons acceleration, cooling, escape and injection to the observed spectra and variability. For the injection luminosity in particular, we derive a new approximate analytical expression for the case of continual injection with a mono-energetic distribution. Based on a comparison between the theoretical predictions and the observed SED, we conclude that the hard X-ray excess observed in Mrk 421 may be due to the synchrotron radiation emitted by an additional electrons population, which is co-spatial with an electron population producing simultaneous Optical/UV, soft X-ray, and $γ$-ray emissions. The stochastic acceleration may play a major role in producing the observed X-ray spectrum.

astro-ph.HE↗

arXiv2022

Spectral-Loc: Indoor Localization using Light Spectral Information

For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way in latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localisation accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room and a large office space, demonstrate that use of light spectral information significantly reduces the localization error for the different percentiles.

cs.HC↗

arXiv2022

Photon correlation spectroscopy with heterodyne mixing based on soft-x-ray magnetic circular dichroism

Many magnetic equilibrium states and phase transitions are characterized by fluctuations. Such magnetic fluctuation can in principle be detected with scattering-based x-ray photon correlation spectroscopy (XPCS). However, in the established approach of XPCS, the magnetic scattering signal is quadratic in the magnetic scattering cross section, which results not only in often prohibitively small signals but also in a fundamental inability to detect negative correlations (anticorrelations). Here, we propose to exploit the possibility of heterodyne mixing of the magnetic signal with static charge scattering to reconstruct the first-order (linear) magnetic correlation function. We show that the first-order magnetic scattering signal reconstructed from heterodyne scattering now directly represents the underlying magnetization texture. Moreover, we suggest a practical implementation based on an absorption mask rigidly connected to the sample, which not only produces a static charge scattering signal but also eliminates the problem of drift-induced artificial decay of the correlation functions. Our method thereby significantly broadens the range of scientific questions accessible by magnetic x-ray photon correlation spectroscopy.

cond-mat.mtrl-sci↗

arXiv2022

A spectral hardening in the Fermi-LAT Data of 1ES 0502+675

The $γ$-ray spectral feature of the blazar 1ES 0502+675 is investigated by using Fermi Large Area Telescope (Fermi-LAT) Pass 8 data (between 100 MeV and 300 GeV) covering from 2008 August to 2021 April. A significant ($\sim4σ$) hardening at $\sim$ 1 GeV is found in the $γ$-ray spectrum during a moderately flaring state (MJD 55050-55350). The photon index below and above the break energy is $Γ_1=2.36\pm0.31$ and $Γ_2=1.33\pm0.11$, respectively. In the rest of the observations, the $γ$-ray spectrum can be described by a power-law form with the photon index of $\approx1.6$. In the frame of a one-zone synchrotron self-Compton (SSC) model, the spectral hardening is interpreted as the transition between the synchrotron component and the SSC component. This could be the result of a slight increase of the break/maximum Lorentz factor of the electrons.

astro-ph.HE↗

arXiv2021

On the narrow spectral feature at $\sim$3 TeV in the MAGIC spectrum of Mrk 501

Using a time-dependent one-zone leptonic model that incorporates both shock acceleration and stochastic acceleration processes, we investigate the formation of the narrow spectral feature at $\sim3$ TeV of Mrk 501 which was observed during the X-ray and TeV flaring activity in July 2014. It is found that the broadband spectral energy distribution (SED) can be well interpreted as the synchrotron and synchrotron-self-Compton emission from the electron energy distribution (EED) that is composed by a power-law (PL) branch and a pileup branch. The PL branch produces synchrotron photons which are scattered by the electrons of the pileup branch via inverse-Compton scattering and form the narrow spectral feature observed at the TeV energies. The EED is produced by two injection episodes, and the pileup branch in EED is caused by shock acceleration rather than stochastic acceleration.

astro-ph.HE↗

arXiv2021

Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization

Localization is important for a large number of Internet of Things (IoT) endpoint devices connected by LoRaWAN. Due to the bandwidth limitations of LoRaWAN, existing localization methods without specialized hardware (e.g., GPS) produce poor performance. To increase the localization accuracy, we propose a super-resolution localization method, called Seirios, which features a novel algorithm to synchronize multiple non-overlapped communication channels by exploiting the unique features of the radio physical layer to increase the overall bandwidth. By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments. We design a Seirios prototype and evaluate its performance in an outdoor area of 100 m $\times$ 60 m, and an indoor area of 25 m $\times$ 15 m, which shows that Seirios can achieve a median error of 4.4 m outdoors (80% samples < 6.4 m), and 2.4 m indoors (80% samples < 6.1 m), respectively. The results show that Seirios produces 42% less localization error than the baseline approach. Our evaluation also shows that, different to previous studies in Wi-Fi localization systems that have wider bandwidth, time-of-fight (ToF) estimation is less effective for LoRaWAN localization systems with narrowband radio signals.

eess.SP↗

arXiv2021

Kinetic powers of the relativistic jets in Mrk 421 and Mrk 501

Using the standard one-zone synchrotron self-Compton (SSC) model and the Markov chain Monte Carlo (MCMC) technique, we systematically analyze the quasi-simultaneous multi-wavelength (MWL) spectral energy distributions (SEDs) of Mrk 421 and Mrk 501 during states of relatively low activity. With this model in place, a semi-analytical method is developed to examine the uncertainty in jet power estimation caused by degeneracy of the radiative models. The semi-analytical method, in combination with the MCMC technique, allows us to explore the jet properties over a wide range of the variability timescale. Our results seem to support: (1) In both Mrk 421 and Mrk 501 the jets are powered by rapidly rotating black holes (BHs). The BH spin in Mrk 501 could be lower than that in Mrk 421 or possibility they are equal under the assumption of Blandford-Znajek mechanism. (2)The energy losses, which could be used to form the large-scale radio structure, are important for reconciling the differences of the kinetic power derived from the observations of the large-scale structure and the SED fitting results. Moreover, the jet energy losses in the propagation are more significant for Mrk 501 than for Mrk 421.

astro-ph.HE↗

arXiv2021

Correlations between $γ$-ray luminosity and magnetization of the jet as well as relativistic electron injection power:cases for Mrk 421, 3C 454.3 and 3C 279

By fitting high-quality and simultaneous multi-wavelength (MWL) spectral energy distributions (SEDs) at multiple epochs with a one-zone leptonic jet model, we study jet properties of the three famous blazars Mrk 421, 3C 454.3 and 3C 279. In the jet model, the emitting electron energy distributions (EEDs) are calculated by solving the kinetic equation of electron injection, escape, adiabatic and radiative energy losses. To explore multi-dimensional parameter space systematically, we employ a Markov chain Monte Carlo (MCMC) fitting technique. The properties of emission regions we derived here are consistent with those in previous studies, e.g., the particle-dominated and low-magnetization jet. The new finding is that there is a tight correlation between $γ$-ray luminosity and electron injection power and an anti-correlation between $γ$-ray luminosity and jet magnetization parameter. The results suggest that same energy-dissipative mechanism (like a shock) could be operating in the jets of different types of blazars, and the origin of $γ$-ray flares is associated with the particle acceleration process.

astro-ph.HE↗

arXiv2021

Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.

eess.SP↗

arXiv2020

Switchable X-ray Orbital Angular Momentum from an Artificial Spin Ice

Artificial spin ices (ASI) have been widely investigated as magnetic metamaterials with exotic properties governed by their geometries. In parallel, interest in X-ray photon orbital angular momentum (OAM) has been rapidly growing. Here we show that a square ASI with a programmed topological defect, a double edge dislocation, imparts OAM to scattered X-rays. Unlike single dislocations, a double dislocation does not introduce magnetic frustration, and the ASI equilibrates to its antiferromagnetic (AF) ground state. The topological charge of the defect differs with respect to the structural and magnetic order; thus, X-ray diffraction from the ASI produces photons with even and odd OAM quantum numbers at the structural and AF Bragg conditions, respectively. The magnetic transitions of the ASI allow the AF OAM beams to be switched on and off by modest variations of temperature and applied magnetic field. These results demonstrate ASIs can serve as metasurfaces for reconfigurable X-ray optics that could enable selective probes of electronic and magnetic properties.

cond-mat.mes-hall↗

arXiv2020

Nephalai: Towards LPWAN C-RAN with Physical Layer Compression

We propose Nephalai, a Compressive Sensing-based Cloud Radio Access Network (C-RAN), to reduce the uplink bit rate of the physical layer (PHY) between the gateways and the cloud server for multi-channel LPWANs. Recent research shows that single-channel LPWANs suffer from scalability issues. While multiple channels improve these issues, data transmission is expensive. Furthermore, recent research has shown that jointly decoding raw physical layers that are offloaded by LPWAN gateways in the cloud can improve the signal-to-noise ratio (SNR) of week radio signals. However, when it comes to multiple channels, this approach requires high bandwidth of network infrastructure to transport a large amount of PHY samples from gateways to the cloud server, which results in network congestion and high cost due to Internet data usage. In order to reduce the operation's bandwidth, we propose a novel LPWAN packet acquisition mechanism based on Compressive Sensing with a custom design dictionary that exploits the structure of LPWAN packets, reduces the bit rate of samples on each gateway, and demodulates PHY in the cloud with (joint) sparse approximation. Moreover, we propose an adaptive compression method that takes the Spreading Factor (SF) and SNR into account. Our empirical evaluation shows that up to 93.7% PHY samples can be reduced by Nephalai when SF = 9 and SNR is high without degradation in the packet reception rate (PRR). With four gateways, 1.7x PRR can be achieved with 87.5% PHY samples compressed, which can extend the battery lifetime of embedded IoT devices to 1.7.

cs.NI↗

arXiv2020

Simultaneous Energy Harvesting and Gait Recognition using Piezoelectric Energy Harvester

Piezoelectric energy harvester, which generates electricity from stress or vibrations, is gaining increasing attention as a viable solution to extend battery life in wearables. Recent research further reveals that, besides generating energy, PEH can also serve as a passive sensor to detect human gait power-efficiently because its stress or vibration patterns are significantly influenced by the gait. However, as PEHs are not designed for precise measurement of motion, achievable gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. To classify gait reliably while simultaneously storing generated energy, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signal. Second, we propose a long short-term memory (LSTM) network-based classifier to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12% higher recall and harvests up to 127% more energy while consuming 38% less power compared to the state-of-the-art.

eess.SP↗

arXiv2020

A Novel Emergency Light Based Smart Building Solution: Design, Implementation and Use Cases

Deployment of Internet of Things (IoT) in smart buildings has received considerable interest from both the academic community and commercial sectors. Unfortunately, widespread adoption of current smart building solutions is inhibited by the high costs associated with installation and maintenance. Moreover, different types of IoT devices from different manufacturers typically form distinct networks and data silos. There is a need to use a common backbone network that facilitates interoperability and seamless data exchange in a uniform way. In this paper, we present EMIoT, a novel solution for smart buildings that breaks these barriers by leveraging existing emergency lighting systems. In EMIoT, we embed a wireless LoRa module in each emergency light to turn them into wireless routers. EMIoT has been deployed in more than 50 buildings of different types in Sydney Australia and has been successfully running over two years. We present the design and implementation of EMIoT in this paper. Moreover, we use the deployment in a residential building as a use case to show the performance of EMIoT in real-world environments and share lessons learned. Finally, we discuss the advantages and disadvantages of EMIoT. This paper provides practical insights for IoT deployment in smart buildings for practitioners and solution providers.

cs.NI↗

arXiv2020

A Survey of COVID-19 Contact Tracing Apps

The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems. COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic. Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions. In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals. However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability. In this article, we provide the first comprehensive review of these much-discussed tracing app attributes. We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage. We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large.

cs.CR↗

arXiv2020

Evidence for the Alternating Next-Nearest Neighbor model in the dynamic behavior of a frustrated antiferromagnet

X-ray photon correlation spectroscopy (XPCS) enables us to study dynamics of antiferromagnets. Using coherent soft X-ray diffraction, we resonantly probe Mn and Co Bragg peaks in the frustrated magnetic chain compound Lu2CoMnO6 significantly below the Neel temperature. Bragg peaks of incommensurate order slide towards commensurate 'up up down down' order with decreasing temperature. Antiferromagnetic inhomogeneities produce speckle within the Bragg peaks, whose dynamics are probed by XPCS and compared to the classic Axial Next-Nearest Neighbor Interaction model of frustration. The data supports a novel model prediction: with decreasing temperature the dynamics become faster.

cond-mat.str-el↗

arXiv2020

Skin-MIMO: Vibration-based MIMO Communication over Human Skin

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we propose Skin-MIMO, a deep learning based CSI acquisition technique to accurately predict CSI entirely based on inertial sensor (accelerometer and gyroscope) measurements at the transmitter, thus obviating the need for channel sounding. Based on experimental vibration data, we show that Skin-MIMO can improve MIMO capacity by a factor of 2.3 compared to Single-Input-Single-Output (SISO) or open-loop MIMO, which do not have access to CSI. A surprising finding is that gyroscope, which measures the angular velocity, is found to be superior in predicting skin vibrations than accelerometer, which measures linear acceleration and used widely in previous research for vibration communications over solid objects.

cs.NI↗

arXiv2020

On the Injection of Relativistic Electrons in the Jet of 3C 279

The acceleration of electrons in 3C 279 is investigated through analyzing the injected electron energy distribution (EED) in a time-dependent synchrotron self-Compton + external Compton emission model. In this model, it is assumed that relativistic electrons are continuously injected into the emission region, and the injected EED [$Q_e^\prime(γ^\prime)$] follows a single power-law form with low- and high-energy cutoffs $\rm γ_{min}'$ and $\rm γ_{max}'$, respectively, and the spectral index $n$, i.e, $Q_e^\prime(γ^\prime)\proptoγ^{\prime-n}$. This model is applied to 14 quasi-simultaneous spectral energy distributions (SEDs) of 3C 279. The Markov Chain Monte Carlo fitting technique is performed to obtain the best-fitting parameters and the uncertainties on the parameters. The results show that the injected EED is well constrained in each state. The value of $n$ is in the range of 2.5 to 3.8, which is larger than that expected by the classic non-relativistic shock acceleration. However, the large value of $n$ can be explained by the relativistic oblique shock acceleration. The flaring activity seems to be related to an increased acceleration efficiency, reflected in an increased $γ'_{\rm min}$ and electron injection power.

astro-ph.HE↗