results: 研究人员成功地恢复了分配多plexed 30GBd QPSK和20GBd 8QAM信号的全场，并在80公里长的SSMF上实现了。Abstract
We demonstrate a silicon photonic dual-polarization phase retrieval receiver. The receiver recovers phase from intensity-only measurements without a local oscillator or transmitted carrier. We design silicon waveguides providing long delays and microring resonators with large dispersion to enable symbol-to-symbol interference and dispersive projection in the phase retrieval algorithm. We retrieve the full field of a polarization-division multiplexed 30-GBd QPSK and 20-GBd 8QAM signals over 80 km of SSMF.
摘要
我们实现了一种半导体光学双极化阶段征 recuperator。该接收器从强度 alone 测量中 recuperate 阶段，不需要本地抗器或传输的干扰信号。我们设计了半导体波导，以实现长时间延迟和微环 resonator 的大扩散，以启用符号到符号干扰和扩散减法在phaseretrieval算法中。我们在80 km SSMF 上 Retrieval 整个场的半导体波分 multiplexed 30 GBd QPSK 和 20 GBd 8QAM 信号。
Can 5G NR Sidelink communications support wireless augmented reality?
paper_authors: Ashutosh Srivastava, Qing Zhao, Yi Lu, Ping Wang, Qi Qu, Zhu Ji, Yee Sin Chan, Shivendra S. Panwar
for: 这 paper 的目的是研究 NR SL 是否可以支持 AR 应用。
methods: 这 paper 使用系统水平的模拟来分析 NR SL 是否可以支持 AR。
results: 研究结果表明，当前 SL 标准规定不足以支持高级 AR 应用程序，但可以支持 simpler 预览和文件传输。 authors 还提出了两种加强 SL 资源分配的方案，它们有可能为 AR 应用程序提供显著性能提升。Abstract
Smart glasses that support augmented reality (AR) have the potential to become the consumer's primary medium of connecting to the future internet. For the best quality of user experience, AR glasses must have a small form factor and long battery life, while satisfying the data rate and latency requirements of AR applications. To extend the AR glasses' battery life, the computation and processing involved in AR may be offloaded to a companion device, such as a smartphone, through a wireless connection. Sidelink (SL), i.e., the D2D communication interface of 5G NR, is a potential candidate for this wireless link. In this paper, we use system-level simulations to analyze the feasibility of NR SL for supporting AR. Our simulator incorporates the PHY layer structure and MAC layer resource scheduling of 3GPP SL, standard 3GPP channel models, and MCS configurations. Our results suggest that the current SL standard specifications are insufficient for high-end AR use cases with heavy interaction but can support simpler previews and file transfers. We further propose two enhancements to SL resource allocation, which have the potential to offer significant performance improvements for AR applications.
摘要
智能眼镜支持增强现实（AR）技术有可能成为未来互联网的主要连接媒介。为保证用户体验质量，AR眼镜需要具有小型化的形 factor和长时间的电池生命周期，同时满足AR应用的数据速率和延迟要求。为了延长AR眼镜的电池生命周期， computation和处理 involved in AR可以卸载到配套设备，如智能手机，通过无线连接。在这篇论文中，我们使用系统级别的模拟来分析SL在支持AR方面的可行性。我们的模拟器包括3GPP SL的物理层结构和 mac层资源调度，标准3GPP通道模型和MCS配置。我们的结果表明，当前SL标准规格不够用于高级AR应用程序，但可以支持简单的预览和文件传输。我们还提出了两种SL资源分配的改进方案，这些改进方案具有提高AR应用程序性能的潜在性。
Time-Reflection of Microwaves by a Fast Optically-Controlled Time-Boundary
paper_authors: Thomas R. Jones, Alexander V. Kildishev, Mordechai Segev, Dimitrios Peroulis
for: 这 paper 是 investigate the time-reflection of electromagnetic (EM) waves in a medium with abrupt property changes.
methods: The authors use a periodically-loaded microstrip line with optically-controlled picosecond-switchable photodiodes to observe the time-reflection of microwave pulses at the highest frequency ever observed (0.59 GHz).
results: The authors present experimental evidence of the phase-conjugation nature of time-reflected waves and demonstrate the feasibility of realizing Photonic Time-Crystals at GHz frequencies.Abstract
When an electromagnetic (EM) wave is propagating in a medium whose properties are varied abruptly in time, the wave experiences refractions and reflections known as "time-refractions" and "time-reflections", both manifesting spectral translation as a consequence of the abrupt change of the medium and the conservation of momentum. However, while the time-refracted wave continues to propagate with the same wave-vector, the time-reflected wave is propagating backward with a conjugate phase, despite the lack of any spatial interface. Importantly, while time-refraction is always significant, observing time-reflection poses a major challenge - because it requires a large change in the medium occurring within a single cycle. For that reason, time-reflection of EM waves was observed only recently. Here, we present the observation of microwave pulses at the highest frequency ever observed (0.59 GHz), and the experimental evidence of the phase-conjugation nature of time-reflected waves. Our experiments are carried out in a periodically-loaded microstrip line with optically-controlled picosecond-switchable photodiodes. Our system paves the way to the experimental realization of Photonic Time-Crystals at GHz frequencies.
摘要
Observing time-reflection is a challenging task, as it requires a significant change in the medium within a single cycle. As a result, time-reflection of EM waves has only recently been observed. In this study, we report the observation of microwave pulses at the highest frequency ever recorded (0.59 GHz), and provide experimental evidence of the phase-conjugate nature of time-reflected waves. Our experiments were conducted in a periodically-loaded microstrip line using optically-controlled picosecond-switchable photodiodes. Our system paves the way for the experimental realization of Photonic Time-Crystals at GHz frequencies.
Integrate-and-fire circuit for converting analog signals to spikes using phase encoding
paper_authors: Javier Lopez-Randulfe, Nico Reeb, Alois Knoll
for: 本研究旨在提出一种基于泛型神经网络的硬件实现，用于处理感知器数据。
methods: 本研究使用了逐渐增强的泛型神经网络模型，并在物理板上实现了快速的时间编码技术。
results: 研究人员通过实现一种可适应的刺激时间控制方法，实现了将连续的感知器数据转换为时间编码的脉冲。这种方法可以减少能耗，并且可以在终端neuromorphic应用中实现快速的处理速度。Abstract
Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end neuromorphic applications. First, to directly encode analog signals to spikes to bypass the need for an analog-to-digital converter (ADC). Second, to use temporal encoding techniques to maximize the spike sparsity, which is a crucial parameter for fast and efficient neuromorphic processing. In this work, we propose an adaptive control of the refractory period of the leaky integrate-and-fire (LIF) neuron model for encoding continuous analog signals into a train of time-coded spikes. The LIF-based encoder generates phase-encoded spikes that are compatible with digital hardware. We implemented the neuron model on a physical circuit and tested it with different electric signals. A digital neuromorphic chip processed the generated spike trains and computed the signal's frequency spectrum using a spiking version of the Fourier transform. We tested the prototype circuit on electric signals up to 1 KHz. Thus, we provide an end-to-end neuromorphic application that generates the frequency spectrum of an electric signal without the need for an ADC or a digital signal processing algorithm.
摘要
处理感知数据使用聚合神经网络（SNN）在数字神经omorphic芯片上需要将连续的分析信号转换为脉冲。有两种措施可能实现低能耗和快速处理速度的端到端神经omorphic应用。第一种是直接将分析信号编码为脉冲，以避免使用分析数字转换器（ADC）。第二种是使用时间编码技术来最大化脉冲稀疏性，这是端到端神经omorphic处理中的关键参数。在这种工作中，我们提出了适应控制LIF神经元模型的储能期的自适应控制，以编码连续分析信号为一列时间编码脉冲。LIF模型生成的脉冲编码在与数字硬件兼容的方面具有优势。我们在物理芯片上实现了神经元模型，并对它们进行了不同的电信号测试。一个数字神经omorphic芯片处理生成的脉冲链，并使用脉冲版 Fourier 变换计算信号的频率谱。我们测试了板件circuit，并在电信号频率范围内达到1KHz。因此，我们提供了一种端到端神经omorphic应用，可以无需ADC或数字信号处理算法，对电信号频率谱进行计算。
Implementation of hyperspectral inversion algorithms on FPGA: Hardware comparison using High Level Synthesis
results: 本研究提供了一个统一的评估框架，可以帮助选择最佳的扩spectrum reconstruction算法和设计体系，以满足特定应用场景的需求。Abstract
Hyperspectral imaging is gathering significant attention due to its potential in various domains such as geology, agriculture, ecology, and surveillance. However, the associated processing algorithms, which are essential for enhancing output quality and extracting relevant information, are often computationally intensive and have to deal with substantial data volumes. Our focus lies on reconfigurable hardware, particularly recent FPGAs. While FPGA design can be complex, High Level Synthesis (HLS) workflows have emerged as a solution, abstracting low-level design intricacies and enhancing productivity. Despite successful prior efforts using HLS for hyperspectral imaging acceleration, we lack a comprehensive research to benchmark various algorithms and architectures within a unified framework. This study aims to quantitatively evaluate performance across different inversion algorithms and design architectures, providing insights for optimal trade-offs for specific applications. We apply this analysis to the case study of spectrum reconstruction processed from interferometric acquisitions taken by Fourier transform spectrometers.
摘要
干部成像技术在不同领域如地质、农业、生态和监测等领域受到了广泛关注，因为它们在数据处理和图像分析方面具有广泛的应用前景。然而，与这些处理算法相关的数据处理和图像分析过程经常具有较高的计算复杂度和大量数据量。我们的研究重点在于可编程的硬件，尤其是最新的场程 gates （FPGAs）。虽然FPGA 设计可能是复杂的，但高级语言synthesis（HLS）工作流程已经出现，它可以抽象低级设计细节，提高生产力。 DESPITE previous efforts have been made to accelerate hyperspectral imaging using HLS, we lack a comprehensive study that benchmarks various algorithms and architectures within a unified framework. This study aims to quantitatively evaluate the performance of different inversion algorithms and design architectures, providing insights for optimal trade-offs for specific applications. We apply this analysis to the case study of spectrum reconstruction processed from interferometric acquisitions taken by Fourier transform spectrometers.
Waveform Manipulation Against DNN-based Modulation Classification Attacks
results: 选择混合波形参数的精细控制可以在AWGN和折射通道中降低侦测器的分类性能至10%以下，无损LRx的性能。Abstract
In this paper we propose a method for defending against an eavesdropper that uses a Deep Neural Network (DNN) for learning the modulation of wireless communication signals. Our method is based on manipulating the emitted waveform with the aid of a continuous time frequency-modulated (FM) obfuscating signal that is mixed with the modulated data. The resulting waveform allows a legitimate receiver (LRx) to demodulate the data but it increases the test error of a pre-trained or adversarially-trained DNN classifier at the eavesdropper. The scheme works for analog modulation and digital single carrier and multi carrier orthogonal frequency division multiplexing (OFDM) waveforms, while it can implemented in frame-based wireless protocols. The results indicate that careful selection of the parameters of the obfuscating waveform can drop classification performance at the eavesdropper to less than 10% in AWGN and fading channels with no performance loss at the LRx.
摘要
在这篇论文中，我们提出了一种防止侦测者使用深度神经网络（DNN）学习无线通信信号的方法。我们的方法基于在扩展时频域内混合扩展时频域调制（FM）隐藏信号和模拟数据之后，将模拟信号发射到侦测者。这种方法使得合法接收器（LRx）可以解模数据，但是使得预训练或恶意训练的DNN分类器在侦测者上增加测试错误率。该方案适用于分形式无线协议，并且可以在模拟调制和数字单个载波和多载波扩展分多处理（OFDM）波形上实现。结果表明，选择混合信号的参数可以在AWGN和折射通道中降低侦测者的分类性能至10%以下，而不会对合法接收器产生影响。
On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring
paper_authors: Sulaiman Aburakhia, Abdallah Shami for: This paper focuses on the problem of peak-to-average power ratio (PAPR) control in vibration-based condition monitoring (VBCM) systems, and proposes a lightweight autoencoder-based signal companding scheme to improve power efficiency and mitigate the impact of nonlinear distortion.methods: The proposed scheme employs a lightweight reconstruction autoencoder with a compression-based activation function in the source to compress the vibration signals, and a denoising-expansion autoencoder in the destination to expand the compressed signals while minimizing noise enhancement.results: The experimental results demonstrate the effectiveness of the proposed companding scheme in preventing nonlinear distortion, improving the efficiency of power amplification in the source, and restoring the PAPR characteristics in the destination while avoiding the undesired effect of noise expansion. The proposed scheme is able to maintain the waveform of the vibration signal and improve the reliability of condition monitoring.Here is the information in Simplified Chinese text:for: 这篇论文关注了VBMC系统中的峰峰值平均功率比(PAPR)控制问题，并提议了一种基于自适应神经网络的信号压缩方案，以提高功率效率和减少非线性扭曲的影响。methods: 提议的方案使用了一种轻量级的重建自适应神经网络，在源端使用压缩基于活动函数来压缩振荡信号，并在目的端使用扩展基于活动函数来扩展压缩后的信号，以最小化噪声增强。results: 实验结果表明，提议的压缩方案能够避免非线性扭曲，提高源端的功率压缩效率，并在目的端 restore PAPR 特性，避免噪声增强的不良影响。Abstract
Vibration-based condition monitoring (VBCM) is widely utilized in various applications due to its non-destructive nature. Recent advancements in sensor technology, the Internet of Things (IoT), and computing have enabled the facilitation of reliable distributed VBCM where sensor nodes are deployed at multiple locations and connected wirelessly to monitoring centers. However, sensor nodes are typically constrained by limited power resources, necessitating control over the peak-to-average power ratio (PAPR) of the generated vibration signals. Effective control of PAPR is crucial to prevent nonlinear distortion and reduce power consumption within the node. Additionally, avoiding nonlinear distortion in the vibration signal and preserving its waveform is essential to ensure the reliability of condition monitoring. This paper conducts an in-depth analysis of the PAPR of vibration signals in VBCM systems, evaluates the impact of nonlinear power amplification on the system performance, and proposes a lightweight autoencoder-based signal companding scheme to control the PAPR to improve power efficiency and mitigate the impact of nonlinear distortion. The proposed scheme employs a lightweight reconstruction autoencoder with a compression-based activation function in the source to compress the vibration signals and avoid increasing the average power of the compressed signal. In the destination, the proposed scheme uses a denoising-expansion autoencoder to expand the compressed signals while minimizing noise enhancement during the expansion process. The experimental results demonstrate the effectiveness of the proposed companding scheme in preventing nonlinear distortion, improving the efficiency of power amplification in the source, and restoring the PAPR characteristics in the destination while avoiding the undesired effect of noise expansion.
摘要
随着互联网技术的发展，质量监测技术（VBCM）在各种应用中广泛应用，主要是因为它的非 destruktive 特点。现有的感知技术，互联网技术和计算技术的进步，使得可靠的分布式VBCM成为可能，感知节点在多个位置被无线连接到监测中心。然而，感知节点通常受到有限的电力资源的限制，因此控制发射器的峰值平均功率比（PAPR）是关键的。有效地控制PAPR可以避免非线性扭曲和降低节点内部的电力消耗。此外，保持振荡信号的波形不受扭曲是重要的，以确保 condition monitoring 的可靠性。本文进行了深入的PAPR振荡信号在VBCM系统中的分析，评估了非线性增强器对系统性能的影响，并提议了一种轻量级自适应压缩 schemes。该方案使用轻量级重建自适应 neural network 来压缩振荡信号，以避免增加平均功率。在目的地，该方案使用一个扩展自适应 neural network 来扩展压缩信号，以最小化扩展过程中的噪声增强。实验结果表明，提议的压缩 schemes 可以避免非线性扭曲，提高源端的电力增强效率，并在目的地Restore PAPR 特点，而不是扩展过程中的噪声增强。