arXiv subjects

Ping Wei

Publications and source records attributed to Ping Wei.

33 records · Page 2Linked to original sources

arXiv2023

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%.

physics.ins-det↗

arXiv2022

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.

physics.ins-det↗

arXiv2022

Generative Steganography Network

Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized for their poor performances. In this paper, we propose an advanced generative steganography network (GSN) that can generate realistic stego images without using cover images. We firstly introduce the mutual information mechanism in GS, which helps to achieve high secret extraction accuracy. Our model contains four sub-networks, i.e., an image generator ($G$), a discriminator ($D$), a steganalyzer ($S$), and a data extractor ($E$). $D$ and $S$ act as two adversarial discriminators to ensure the visual quality and security of generated stego images. $E$ is to extract the hidden secret from generated stego images. The generator $G$ is flexibly constructed to synthesize either cover or stego images with different inputs. It facilitates covert communication by concealing the function of generating stego images in a normal generator. A module named secret block is designed to hide secret data in the feature maps during image generation, with which high hiding capacity and image fidelity are achieved. In addition, a novel hierarchical gradient decay (HGD) skill is developed to resist steganalysis detection. Experiments demonstrate the superiority of our work over existing methods.

cs.CV↗

arXiv2021

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli \textit{random finite set} (RFS) uniquely characterized by existence, classification, class-conditioned mode and class\&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel \textit{centralized} and \textit{distributed} JDTC \textit{Bernoulli filters} (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.

eess.SP↗

arXiv2020

Green Resource Allocation and Energy Management in Heterogeneous Small Cell Networks Powered by Hybrid Energy

In heterogeneous networks (HetNets), how to improve spectrum efficiency is a crucial issue. Meanwhile increased energy consumption inspires network operators to deploy renewable energy sources as assistance to traditional electricity. Based on above aspects, we allow base stations (BSs) to share their licensed spectrum resource with each other and adjust transmission power to adapt to the renewable energy level. Considering the sharing fairness among BSs, we formulate a multi-person bargaining problem as a stochastic optimization problem. We divide the optimization problem into three parts: data rate control, resource allocation and energy management. An online dynamic control algorithm is proposed to control admission rate and resource allocation to maximize the transmission and sharing profits with the least grid energy consumption. Simulation results investigate the time-varying data control and energy management of BSs and demonstrate the effectiveness of the proposed scheme.

cs.NI↗

arXiv2019

Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network

This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, xpressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.

eess.SY↗

arXiv2018

Joint Estimation of DOA and Frequency with Sub-Nyquist Sampling in a Binary Array Radar System

Recently, several array radar structures combined with sub-Nyquist techniques and corresponding algorithms have been extensively studied. Carrier frequency and direction-of-arrival (DOA) estimations of multiple narrow-band signals received by array radars at the sub-Nyquist rates are considered in this paper. We propose a new sub-Nyquist array radar architecture (a binary array radar separately connected to a multi-coset structure with M branches) and an efficient joint estimation algorithm which can match frequencies up with corresponding DOAs. We further come up with a delay pattern augmenting method, by which the capability of the number of identifiable signals can increase from M-1 to Q-1 (Q is extended degrees of freedom). We further conclude that the minimum total sampling rate 2MB is sufficient to identify $ {K \leq Q-1}$ narrow-band signals of maximum bandwidth $B$ inside. The effectiveness and performance of the estimation algorithm together with the augmenting method have been verified by simulations.

eess.SP↗

arXiv2017

Predicting Human Activities Using Stochastic Grammar

This paper presents a novel method to predict future human activities from partially observed RGB-D videos. Human activity prediction is generally difficult due to its non-Markovian property and the rich context between human and environments. We use a stochastic grammar model to capture the compositional structure of events, integrating human actions, objects, and their affordances. We represent the event by a spatial-temporal And-Or graph (ST-AOG). The ST-AOG is composed of a temporal stochastic grammar defined on sub-activities, and spatial graphs representing sub-activities that consist of human actions, objects, and their affordances. Future sub-activities are predicted using the temporal grammar and Earley parsing algorithm. The corresponding action, object, and affordance labels are then inferred accordingly. Extensive experiments are conducted to show the effectiveness of our model on both semantic event parsing and future activity prediction.

cs.CV↗

arXiv2017

Consensus-based joint target tracking and sensor localization

In this paper, consensus-based Kalman filtering is extended to deal with the problem of joint target tracking and sensor self-localization in a distributed wireless sensor network. The average weighted Kullback-Leibler divergence, which is a function of the unknown drift parameters, is employed as the cost to measure the discrepancy between the fused posterior distribution and the local distribution at each sensor. Further, a reasonable approximation of the cost is proposed and an online technique is introduced to minimize the approximated cost function with respect to the drift parameters stored in each node. The remarkable features of the proposed algorithm are that it needs no additional data exchanges, slightly increased memory space and computational load comparable to the standard consensus-based Kalman filter. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation experiments on both a tree network and a network with cycles as well as for both linear and nonlinear sensors.

eess.SY↗

arXiv2017

Joint DOA and Frequency Estimation with Sub-Nyquist Sampling

In this paper, to jointly estimate the frequency and the direction-of-arrival(DOA) of the narrowband far-field signals, a novel array receiver architecture is presented by the concept of the sub-Nyquist sampling techniques. In particular, our contribution is threefold. i) First, we propose a time-space union signal reception model for receiving array signals, where the sub-Nyquist sampling techniques and arbitrary array geometries are employed to decrease the time-domain sampling rate and improve the DOA estimation accuracy. A better joint estimation is obtained in the higher time-space union space. ii) Second, two joint estimation algorithms are proposed for the receiving model. One is based on a trilinear decomposition from the third-order tensor theory and the other is based on subspace decomposition. iii) Third, we derive the corresponding Cramér\text{-}Rao Bound (CRB) for frequency and DOA estimates. In the case of the branch number of our architecture is equal to the reduction factor of the sampling rate, it is observed that the CRB is robust in terms of the number of signals, while the CRB based on the Nyquist sampling scheme will increase with respect to the number of signals. In addition, the new steer vectors of the union time-space model are completely uncorrelated under the limited number of sensors, which improves the estimation performance. Furthermore, the simulation results demonstrate that our estimates via the receiver architecture associated with the proposed algorithms closely match the CRB according to the noise levels, the branch number and the source number as well.

cs.IT↗

arXiv2017

Joint DOA and Frequency Estimation with Sub-Nyquist Sampling for More Sources than Sensors

In this letter, we apply previous array receiver architecture which employs time-domain sub-Nyquist sampling techniques to jointly estimate frequency and direction-of-arrival(DOA) of narrowband far-field signals. Herein, a more general situation is taken into consideration, where there may be more than one signal in a subband. We build time-space union model, analyze the identification of the model, and give the maximum signal number which can be classified. We also proof that the Cramer-Rao Bound (CRB) is lower than that of which employs Nyquist sampling. Simulation results verify the capacity to estimate the number of sources. Meanwhile, simulations show that our estimation performance closely matches the CRB and is superior for more sources than sensors, especially when the minimum redundancy array (MRA) is employed.

cs.IT↗

arXiv2016

A Simplified Sub-Nyquist Receiver Architecture for Joint DOA and Frequency Estimation

Joint estimation of carrier frequency and direction of arrival (DOA) for multiple signals has been found in many practical applications such as Cognitive Radio (CR). However, Nyquist sampling mechanism is costly or implemented due to wide spectrum range. Taking advantage of sub-Nyquist sampling technology, some array receiver architectures are proposed to realize joint estimation of carrier frequency and DOA. To further decrease equivalent sampling rate and hardware complexity, we propose a simplifying receiver architecture based on our previous work. We come up with joint DOA and frequency estimation algorithms for the novel architecture. The simulations demonstrate that the receiver architecture and the proposed approaches are feasible.

cs.IT↗

arXiv2014

Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function

Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation owing to advantages over conventional ones. However the performance of compressive sensing (CS)-based estimation methods decreases when true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this work, we analyze the convexity of the DML function in the vicinity of the global solution. Especially under the condition of large array, we search for an approximately convex range around the ture DOAs to guarantee the DML function convex. Based on the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments show that the rough convex range accords well with the exact convex range of the DML function with large array and demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed.

cs.IT↗

arXiv2012

Viscous boundary layer properties in turbulent thermal convection in a cylindrical cell: the effect of cell tilting

We report an experimental study of the properties of the velocity boundary layer in turbulent Rayleigh-Bénard convection in a cylindrical cell. The measurements were made at Rayleigh numbers $Ra$ in the range $2.8\times10^{8}<Ra<5.6\times10^{9}$ and were conducted with the convection cell tilted with an angle $θ$ relative to gravity, at $θ=0.5^{o}$, $1.0^{o}$, $2.0^{o}$, and $3.4^{o}$, respectively. The fluid was water with Prandtl number $Pr=5.3$. It is found that at small tilt angles ($θ\le 1^{o}$), the measured viscous boundary layer thickness $δ_{v}$ scales with the Reynolds number $Re$ with an exponent close to that for a Prandtl-Blasius laminar boundary layer, i.e. $δ_{v} \sim Re^{-0.46\pm0.03}$. For larger tilt angles, the scaling exponent of $δ_{v}$ with $Re$ decreases with $θ$. The normalized mean horizontal velocity profiles measured at the same tilt angle but with different $Ra$ are found to have an invariant shape. But for different tilt angles, the shape of the normalized profiles is different. It is also found that the Reynolds number $Re$ based on the maximum mean horizontal velocity scales with $Ra$ as $Re \sim Ra^{0.43}$ and the Reynolds number $Re_σ$ based on the maximum rms velocity scales with $Ra$ as $Re_σ \sim Ra^{0.55}$, with both exponents do not seem to depend on the tilt angle $θ$. We also examined the dynamical scaling method proposed bys Zhou and Xia [Phys. Rev. Lett. 104, 104301 (2010)] and found that in both the laboratory and the dynamical frames the mean velocity profiles show deviations from the theoretical Prandtl-Blasius profile, with the deviations increase with $Ra$. But profiles obtained from dynamical scaling in general have better agreement with the theoretical profile. It is also found that the effectiveness of this method appears to be independent of $Ra$.

physics.flu-dyn↗

arXiv2006

Easily Testable Necessary and Sufficient Algebraic Criteria for Delay-independent Stability of a Class of Neutral Differential Systems

This paper analyzes the eigenvalue distribution of neutral differential systems and the corresponding difference systems, and establishes the relationship between the eigenvalue distribution and delay-independent stability of neutral differential systems. By using the ``Complete Discrimination System for Polynomials", easily testable necessary and sufficient algebraic criteria for delay-independent stability of a class of neutral differential systems are established. The algebraic criteria generalize and unify the relevant results in the literature. Moreover, the maximal delay bound guaranteeing stability can be determined if the systems are not delay-independent stable. Some numerical examples are provided to illustrate the effectiveness of our results.

math.DS↗