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Jiao Zhang

Publications and source records attributed to Jiao Zhang.

At least 19 recordsLinked to original sources
arXiv2025

Integrated Silicon Photonic Multichannel Optical Hybrid for Broadband Parallel Coherent Reception

We design and demonstrate a monolithically integrated silicon photonic multichannel optical hybrid for versatile broadband coherent reception, addressing the critical limitations of current wavelength multiplexed systems in scalability and power efficiency. The device combines a phase-compensated 90-degree optical hybrid with four robust three-stage Mach-Zehnder interferometer lattice filters, enabling 34-port functionality (two inputs and 32 outputs) for simultaneous analog and digital signal processing. Leveraging multimode interferometer designs,the chip achieves a broadband response with sub-dB passband uniformity across eight 200 GHz-spaced wavelength channels, while maintaining phase errors below 4 degrees over a 13.5 nm (1539-1552.5 nm) bandwidth with only 2.5 mW thermal tuning power.Experimentally, we validate its parallel-processing capability through RF channelizer reception (showing an average spurious-free dynamic range of 80.8 dB*Hz2/3 and image rejection ratio of 33.26 dB) and coherent optical communication (achieving 1.024 Tb/s data rate for 32-QAM signals with bit error rates far below the 20% SD-FEC threshold). The scheme enhances system performance with fully passive wavelength multiplexing integration, supporting high-fidelity uniformity and projecting scalability to 1.468 Tb/s. This work promises advancements in high-performance optoelectronic devices for next-generation AI-driven data centers and 5G-XG networks.

physics.optics↗
arXiv2025

Spatiotemporal evolution of PM2.5 diffusion in Cheng-Yu urban agglomeration in response to COVID-19 lockdown using complex network

As the decrease in human activities resulting from the COVID-19 control measures had a significant impact on air quality, the epidemic provided an opportunity to investigate the extent to which air pollution is influenced by human activities and review existing measures. However, the corresponding diffusion pattern on a city scale is seldom mentioned at present stage, therefore, we chose the Cheng-Yu urban agglomeration, which is the largest city cluster in Southwest China, as our study area during the COVID-19 period, and attempted to investigate the process of PM2.5 diffusion using a complex network method. The results displayed that there was an evident external spillover effect of PM2.5 across all regions, and the PM2.5 spillovers were concentrated in several cities in the Cheng-Yu urban agglomeration during the lockdown period, whereas they are more dispersed during the recovery period. The overall decline in the impact of PM2.5 pollution source areas on receptor areas from a normal year to the pandemic year, and the intensity of PM2.5 spillover decreases gradually as the distance from the center increases. The implementation of the lockdown measures had an impact on both the input and output patterns of PM2.5 pollution in the region, the input pattern of PM2.5 pollution exhibited higher vulnerability, while the output pattern showed higher resilience. Additionally, the spillover relationship of PM2.5 pollution varies between different blocks, with relatively simple spillover relationships observed during the lockdown period and more complex dynamics during the recovery period. These findings have highlighted the importance of joint controls in combating regional air pollution.

math.NA↗
arXiv2024

StylePrompter: Enhancing Domain Generalization with Test-Time Style Priors

In real-world applications, the sample distribution at the inference stage often differs from the one at the training stage, causing performance degradation of trained deep models. The research on domain generalization (DG) aims to develop robust algorithms that can improve the generalized performance in unseen domains by training on a few domains. However, the domain-agnostic vision model, trained on a limited number of domains using traditional domain generalization methods, cannot guarantee its effectiveness in dealing with unseen domains. The introduction of language can break the closed cognition space of the vision model, providing additional semantic information that cannot be inferred from vision-only datasets. In this paper, we propose to overcome the challenge in previous DG methods by introducing the style prompt in the language modality to adapt the trained model dynamically. In particular, we train a style prompter to extract style information of the current image into an embedding in the token embedding space and place it in front of the candidate category words as prior knowledge to prompt the model. Our open space partition of the style token embedding space and the hand-crafted style regularization enable the trained style prompter to handle data from unknown domains effectively. Extensive experiments verify the effectiveness of our method and demonstrate state-of-the-art performances on multiple public datasets. Codes will be available after the acceptance of this paper.

cs.CV↗
arXiv2024

SeqBalance: Congestion-Aware Load Balancing with no Reordering for RoCE

Remote Direct Memory Access (RDMA) is widely used in data center networks because of its high performance. However, due to the characteristics of RDMA's retransmission strategy and the traffic mode of AI training, current load balancing schemes for data center networks are unsuitable for RDMA. In this paper, we propose SeqBalance, a load balancing framework designed for RDMA. SeqBalance implements fine-grained load balancing for RDMA through a reasonable design and does not cause reordering problems. SeqBalance's designs are all based on existing commercial RNICs and commercial programmable switches, so they are compatible with existing data center networks. We have implemented SeqBalance in Mellanox CX-6 RNICs and Tofino switches. The results of hardware testbed experiments and large-scale simulations show that compared with existing load balancing schemes, SeqBalance improves 18.7% and 33.2% on average FCT and 99th percentile FCT.

cs.NI↗
arXiv2024

Swarm: Cost-Efficient Video Content Distribution with a Peer-to-Peer System

As ByteDance's business expands, the substantial infrastructure expenses associated with centralized Content Delivery Network (CDN) networks have rendered content distribution costs prohibitively high. In response, we embarked on exploring a peer-to-peer (P2P) network as a promising solution to alleviate the escalating costs of content distribution. However, the decentralized nature of P2P often introduces performance challenges, given the diversity and dispersion of peer devices. This study introduces Swarm, ByteDance's innovative hybrid system for video streaming. Swarm seamlessly integrates the robustness of a conventional CDN with the cost-efficiency of a decentralized P2P network. Its primary aim is to provide users with reliable streaming quality while minimizing traffic expenses. To achieve this, Swarm employs a centralized control plane comprised of a tracker cluster, overseeing a data plane with numerous edge residual resources. The tracker also takes on the responsibility of mapping clients to servers. Addressing the performance disparities among individual peer servers, Swarm utilizes our proprietary multipath parallel transmission method for communication between clients and peer servers. Operating stably for six years, Swarm now manages over a hundred thousand peer servers, serving nearly a hundred million users daily and saving the company hundreds of millions of RMB annually. Experimental results affirm that, while significantly cutting costs, Swarm performs on par with traditional CDNs.

cs.NI↗
arXiv2023

Towards Reliable Domain Generalization: A New Dataset and Evaluations

There are ubiquitous distribution shifts in the real world. However, deep neural networks (DNNs) are easily biased towards the training set, which causes severe performance degradation when they receive out-of-distribution data. Many methods are studied to train models that generalize under various distribution shifts in the literature of domain generalization (DG). However, the recent DomainBed and WILDS benchmarks challenged the effectiveness of these methods. Aiming at the problems in the existing research, we propose a new domain generalization task for handwritten Chinese character recognition (HCCR) to enrich the application scenarios of DG method research. We evaluate eighteen DG methods on the proposed PaHCC (Printed and Handwritten Chinese Characters) dataset and show that the performance of existing methods on this dataset is still unsatisfactory. Besides, under a designed dynamic DG setting, we reveal more properties of DG methods and argue that only the leave-one-domain-out protocol is unreliable. We advocate that researchers in the DG community refer to dynamic performance of methods for more comprehensive and reliable evaluation. Our dataset and evaluations bring new perspectives to the community for more substantial progress. We will make our dataset public with the article published to facilitate the study of domain generalization.

cs.CV↗
arXiv2023

A Scalable Arrangement Method for Aperiodic Array Antennas to Reduce Peak Sidelobe Level

Peak sidelobe level reduction (PSLR) is crucial in the application of large-scale array antenna, which directly determines the radiation performance of array antenna. We study the PSLR of subarray level aperiodic arrays and propose three array structures: dislocated subarrays with uniform elements (DSUE), uniform subarrays with random elements (USRE), dislocated subarrays with random elements (DSRE). To optimize the dislocation position of subarrays and random position of elements, the improved Bat algorithm (IBA) is applied. To draw the comparison of PSLR effect among these three array structures, we take three size of array antennas from small to large as examples to simulate and calculate the redundancy and peak sidelobe level (PSLL) of them. The results show that DSRE is the optimal array structure by analyzing the dislocation distance of subarray, scanning angle and applicable frequency. The proposed design method is a universal and scalable method, which is of great application value to the design of large-scale aperiodic array antenna.

eess.SP↗
arXiv2022

Toward 6G TK$μ$ Extreme Connectivity: Architecture, Key Technologies and Experiments

Sixth-generation (6G) networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency, and $μ$s-scale latency. To this end, an original three-layer 6G network architecture is designed to realise uniform full-spectrum cell-free radio access and provide task-centric agile proximate support for diverse applications. The designed architecture is featured by super edge node (SEN) which integrates connectivity, computing, AI, data, etc. On this basis, a technological framework of pervasive multi-level (PML) AI is established in the centralised unit to enable task-centric near-real-time resource allocation and network automation. We then introduce a radio access network (RAN) architecture of full spectrum uniform cell-free networks, which is among the most attractive RAN candidates for 6G TK$μ$ extreme connectivity. A few most promising key technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz wireless access and spatiotemporal two-dimensional channel coding are further discussed. A testbed is implemented and extensive trials are conducted to evaluate innovative technologies and methodologies. The proposed 6G network architecture and technological framework demonstrate exciting potentials for full-service and full-scenario applications.

cs.NI↗
arXiv2022

The Tianlai dish array low-z surveys forecasts

We present the science case for surveys with the Tianlai dish array interferometer tuned to the $\left[ 1300, 1400 \right] \mathrm{MHz}$ frequency range. Starting from a realistic generation of mock visibility data according to the survey strategy, we reconstruct a map of the sky and perform a foreground subtraction. We show that a survey of the North Celestial Polar cap during a year of observing time and covering an area of $150 \, \mathrm{deg^2}$ would reach a sensitivity of $ 1.5-2 \, \mathrm{mK} $ per $1 \, \mathrm{MHz} \times 0.25^2 \, \mathrm{deg^2 }$ voxel and be marginally impacted by mode-mixing. Tianlai would be able to detect a handful $(\sim 10)$ of nearby massive \HI clumps as well as a very strong cross-correlation signal of 21\,cm intensity maps with the North Celestial Cap Survey optical galaxies. We have also studied the performance of a mid-latitude survey, covering $\sim 1500 \, \mathrm{deg^2}$ centered on a declination of $δ=55^\circ$, which overlaps the Sloan Digital Sky Survey footprint. Despite a higher noise level for the mid-latitude survey, as well as significant distortions due to mode mixing, Tianlai would be able to detect a highly significant cross-correlation between the 21\,cm signal and the Sloan spectroscopic galaxy sample. Using the extragalactic signals from either or both of these surveys, it will be possible to assess the impact of calibration uncertainties, antenna pattern uncertainties, sources of noise, and mode mixing for future surveys requiring higher sensitivity.

astro-ph.CO↗
arXiv2022

Group decision making with q-rung orthopair hesitant fuzzy preference relations

This paper mainly studies group decision making (GDM) problem based on q-rung orthopair hesitant fuzzy preference relations (q-ROHFPRs). First, the definitions of q-ROHFPR and additive consistent q-ROHFPR are introduced. The consistency index of q-ROHFPR is used to judge whether the matrix of q-ROHFPR is acceptable. For the q-ROHFPR matrix that does not meet the acceptable consistency, two optimization models are established for deriving the acceptably additive consistent q-ROHFPRs. In order to make the q-ROHFPR matrix of decision makers still satisfy the consistency after aggregation, this paper extends the q-rung orthopair hesitant fuzzy weighted geometric average operator (q-ROHFWGA). At the same time, in order to verify whether decision makers can reach consensus after aggregation, a consensus index based on distance is offered. Based on this consensus index, an optimization model that satisfies consistency and consensus is constructed to solve the priority vector, and develop a consistency and consensus-based approach for dealing with group decision-making (GDM) with q-ROHFPRs. Finally, the case in this paper verifies the validity and accuracy of the group decision-making model, and also verifies that the q-ROHFPR consistency and consensus management model proposed in this paper can solve the q-rung orthopair hesitant fuzzy preference group decision-making problem.

math.OC↗
arXiv2021

Rota-Baxter Coalgebras

We introduce the notion of Rota-Baxter coalgebra which can be viewed as the dual notion of Rota-Baxter algebra. We provide some concrete examples and establish various properties of this new object. We also consider comodules over Rota-Baxter coalgebras.

math.RA↗
arXiv2021

The Tianlai Dish Pathfinder Array: design, operation and performance of a prototype transit radio interferometer

The Tianlai Dish Pathfinder Array is a radio interferometer designed to test techniques for 21~cm intensity mapping in the post-reionization universe as a means for measuring large-scale cosmic structure. It performs drift scans of the sky at constant declination. We describe the design, calibration, noise level, and stability of this instrument based on the analysis of about $\sim 5 \%$ of 6,200 hours of on-sky observations through October, 2019. Beam pattern determinations using drones and the transit of bright sources are in good agreement, and compatible with electromagnetic simulations. Combining all the baselines, we make maps around bright sources and show that the array behaves as expected. A few hundred hours of observations at different declinations have been used to study the array geometry and pointing imperfections, as well as the instrument noise behaviour. We show that the system temperature is below 80~K for most feed antennas, and that noise fluctuations decrease as expected with integration time, at least up to a few hundred seconds. Analysis of long integrations, from 10 nights of observations of the North Celestial Pole, yielded visibilities with amplitudes of 20-30~mK, consistent with the expected signal from the NCP radio sky with $<10\,$mK precision for $1 ~\mathrm{MHz} \times 1~ \mathrm{min}$ binning. Hi-pass filtering the spectra to remove smooth spectrum signal yields a residual consistent with zero signal at the $0.5\,$mK level.

astro-ph.IM↗
arXiv2021

AlgoSCR: An algorithm for Solar Contamination Removal from radio interferometric data

Hydrogen intensity mapping is a new field in astronomy that promises to make three-dimensional maps of the matter distribution of the Universe using the redshifted $21\,\textrm{cm}$ line of neutral hydrogen gas (HI). Several ongoing and upcoming radio interferometers, such as Tianlai, CHIME, HERA, HIRAX, etc. are using this technique. These instruments are designed to map large swaths of the sky by drift scanning over periods of many months. One of the challenges of the observations is that the daytime data is contaminated by strong radio signals from the Sun. In the case of Tianlai, this results in almost half of the measured data being unusable. We try to address this issue by developing an algorithm for solar contamination removal (AlgoSCR) from the radio data. The algorithm is based on an eigenvalue analysis of the visibility matrix, and hence is applicable only to interferometers. We apply AlgoSCR to simulated visibilities, as well as real daytime data from the Tianlai dish array. The algorithm can remove most of the solar contamination without seriously affecting other sky signals and thus makes the data usable for certain applications.

astro-ph.IM↗
arXiv2020

Data Processing Pipeline For Tianlai Experiment

The Tianlai project is a 21cm intensity mapping experiment aimed at detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily affected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline software called {\tt tlpipe} to process the drift scan survey data from the Tianlai experiment. It performs offline data processing tasks such as radio frequency interference (RFI) flagging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhonov regularization for $m$-mode analysis. In this paper we describe the design and implementation of the {\tt tlpipe} and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.

astro-ph.IM↗
arXiv2020

Numerology Selection for OFDM Systems Based on Deep Neural Networks

In order to support diverse scenarios and deployments, the numerology of orthogonal frequency division multiplexing (OFDM) is defined for the parametrization of subcarrier spacing and cyclic prefix (CP). The time-frequency dispersion of mobile radio channels and the channel noise result in different performance deterioration in different numerologies. In this letter, we propose a deep neutral network (DNN) approach for numerology selection of OFDM systems. Considering the inter-symbol interference (ISI), inter-carrier interference (ICI) and noise level, the SNR loss is established as the objective to be minimized. We extract the power delay profile, mobile velocity and noise power as the input features to the DNN. The proposed DNN learns from the channel characteristics to obtain the optimal numerology selection. Simulation results show that the proposed DNN achieves better performance than the existing methods. The decision boundaries of different numerologies are also illustrated to show the application range according to the channel characteristics.

eess.SP↗
arXiv2020

The Tianlai Cylinder Pathfinder Array: System Functions and Basic Performance Analysis

The Tianlai Cylinder Pathfinder is a radio interferometer array designed to test techniques for 21 cm intensity mapping in the post-reionization Universe, with the ultimate aim of mapping the large scale structure and measuring cosmological parameters such as the dark energy equation of state. Each of its three parallel cylinder reflectors is oriented in the north-south direction, and the array has a large field of view. As the Earth rotates, the northern sky is observed by drift scanning. The array is located in Hongliuxia, a radio-quiet site in Xinjiang, and saw its first light in September 2016. In this first data analysis paper for the Tianlai cylinder array, we discuss the sub-system qualification tests, and present basic system performance obtained from preliminary analysis of the commissioning observations during 2016-2018. We show typical interferometric visibility data, from which we derive the actual beam profile in the east-west direction and the frequency band-pass response. We describe also the calibration process to determine the complex gains for the array elements, either using bright astronomical point sources, or an artificial on site calibrator source, and discuss the instrument response stability, crucial for transit interferometry. Based on this analysis, we find a system temperature of about 90 K, and we also estimate the sensitivity of the array.

astro-ph.IM↗
arXiv2018

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

cs.NI↗