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Ultrashort pulse sources are complex and resource-intensive. To reduce overhead and simplify operations, we had previously developed a method to deliver ultra-short pulses through fiber-optic links to multiple locations and to characterize them remotely using a compact detector module. We created a pulse pair with varying delays at the central location using a pulse shaper before launching them into the fiber links and measured the first and second-order autocorrelations at the satellite location. However, this method proved inadequate for detecting the effects of optical nonlinearities as the spectral broadening seen by a pulse pair with varying degrees of overlap differs from that of a pair of pulses undergoing nonlinear broadening separately. To overcome this drawback, we propose to launch a variable-delay pulse pair with no temporal overlap to avoid combined nonlinear distortions in the fiber link and measure the autocorrelations at the output by adding a fixed-delay interferometer to our detector module. The in-house fabricated fixed-delay element consisted of a quartz plate with its surfaces coated by partially reflecting Bragg mirrors. Using this modified setup, we have been able to detect the nonlinear distortions encountered by sub∼400fs pulses in the delivery links.

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas. [ABSTRACT FROM AUTHOR]

In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives. This has catalyzed the ascension of evolutionary multi-objective optimization (EMO) as a prevalent approach. Despite the effectiveness of the EMO paradigm, the analysis of resultant solution sets presents considerable challenges. This is primarily attributed to the high-dimensional nature of the data and the constraints imposed by static visualization methods, which frequently culminate in visual clutter and impede interactive exploratory analysis. To address these challenges, this paper introduces ParetoLens, a visual analytics framework specifically tailored to enhance the inspection and exploration of solution sets derived from the multi-objective evolutionary algorithms. Utilizing a modularized, algorithm-agnostic design, ParetoLens enables a detailed inspection of solution distributions in both decision and objective spaces through a suite of interactive visual representations. This approach not only mitigates the issues associated with static visualizations but also supports a more nuanced and flexible analysis process. The usability of the framework is evaluated through case studies and expert interviews, demonstrating its potential to uncover complex patterns and facilitate a deeper understanding of multi-objective optimization solution sets. A demo website of ParetoLens is available at https://dva-lab.org/paretolens/. [ABSTRACT FROM AUTHOR]

Map annotation interpretation is crucial for geographic information extraction and intelligent map analysis. This study addresses the challenges associated with interpreting Chinese map annotations, specifically visual complexity and data scarcity issues, by proposing a hybrid intelligence-based multi-source unstructured Chinese map annotation interpretation method (HI-CMAIM). Firstly, leveraging expert knowledge in an innovative way, we constructed a high-quality expert knowledge-based map annotation dataset (EKMAD), which significantly enhanced data diversity and accuracy. Furthermore, an improved annotation detection model (CMA-DB) and an improved annotation recognition model (CMA-CRNN) were designed based on the characteristics of map annotations, both incorporating expert knowledge. A two-stage transfer learning strategy was employed to tackle the issue of limited training samples. Experimental results demonstrated the superiority of HI-CMAIM over existing algorithms. In the detection task, CMA-DB achieved an 8.54% improvement in Hmean (from 87.73% to 96.27%) compared to the DB algorithm. In the recognition task, CMA-CRNN achieved a 15.54% improvement in accuracy (from 79.77% to 95.31%) and a 4-fold reduction in NED (from 0.1026 to 0.0242), confirming the effectiveness and advancement of the proposed method. This research not only provides a novel approach and data support for Chinese map annotation interpretation but also fills the gap of high-quality, diverse datasets. It holds practical application value in fields such as geographic information systems and cartography, significantly contributing to the advancement of intelligent map interpretation. [ABSTRACT FROM AUTHOR]

Fluorescence endoscopy technology utilizes a light source of a specific wavelength to excite the fluorescence signals of biological tissues. This capability is extremely valuable for the early detection and precise diagnosis of pathological changes. Identifying a suitable experimental approach and metric for objectively and quantitatively assessing the imaging quality of fluorescence endoscopy is imperative to enhance the image evaluation criteria of fluorescence imaging technology. In this study, we propose a new set of standards for fluorescence endoscopy technology to evaluate the optical performance and image quality of fluorescence imaging objectively and quantitatively. This comprehensive set of standards encompasses fluorescence test models and imaging quality assessment protocols to ensure that the performance of fluorescence endoscopy systems meets the required standards. In addition, it aims to enhance the accuracy and uniformity of the results by standardizing testing procedures. The formulation of pivotal metrics and testing methodologies is anticipated to facilitate direct quantitative comparisons of the performance of fluorescence endoscopy devices. This advancement is expected to foster the harmonization of clinical and preclinical evaluations using fluorescence endoscopy imaging systems, thereby improving diagnostic precision and efficiency. [ABSTRACT FROM AUTHOR]

Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories3, 4–5, gradient-boosted decision trees6, 7, 8–9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains.Tabular Prior-data Fitted Network, a tabular foundation model, provides accurate predictions on small data and outperforms all previous methods on datasets with up to 10,000 samples by a wide margin. [ABSTRACT FROM AUTHOR]

Background: Integrating nonlinear behavior into the architecture of artificial neural networks is regarded as essential requirement to constitute their effectual learning capacity for solving complex tasks. This claim seems to be true for moderate-sized networks, i.e., with a lower double-digit number of layers. However, going deeper with neural networks regularly turns into destructive tendencies of gradual performance degeneration during training. To circumvent this degradation problem, the prominent neural architectures Residual Network and Highway Network establish skip connections with additive identity mappings between layers. Methods: In this work, we unify the mechanics of both architectures into Capsule Networks (CapsNet)s by showing their inherent ability to learn skip connections. As a necessary precondition, we introduce the concept of Adaptive Nonlinearity Gates (ANG)s which dynamically steer and limit the usage of nonlinear processing. We propose practical methods for the realization of ANGs including biased batch normalization, the Doubly-Parametric ReLU (D-PReLU) activation function, and Gated Routing (GR) dedicated to extremely deep CapsNets. Results: Our comprehensive empirical study using MNIST substantiates the effectiveness of our developed methods and delivers valuable insights for the training of very deep nets of any kind. The final experiments on Fashion-MNIST and SVHN demonstrate the potential of pure capsule-driven networks with GR. [ABSTRACT FROM AUTHOR]

To effectively eliminate the nonlinear distortion caused by the modulation depth (C value) drift and carrier phase delay (θ) in the phase-generated carrier (PGC) demodulation scheme, the PGC-PDC-EKF joint algorithm is presented, which combines phase delay compensation (PDC) with an extended Kalman filter (EKF). The θ is accurately extracted and compensated by the PDC module. Furthermore, with the EKF algorithm, the harmonic distortion of the demodulated signal due to the fluctuation of C value is suppressed. The experimental results indicate that θ is compensated accurately with a resolution of 0.01745 rad. The signal-to-noise and distortion ratio (SINAD) of the improved scheme reaches 54.01 dB, which is 18.03 dB higher than the PGC-Arctan algorithm on average. The total harmonic distortion (THD) is as low as −62.28 dB, which is 26.04 dB lower than the PGC-Arctan algorithm. The linearity of the demodulation system exceeds 99.99%. The proposed method provides a significant reference for demodulation schemes of interferometric fiber optic sensing systems in practical applications. [ABSTRACT FROM AUTHOR]

The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks. [ABSTRACT FROM AUTHOR]

The application of DFT precoding in mitigating peak-to-average power ratio (PAPR) issues in optical wireless communication systems under power constraints is well established. However, the channel spatial diversity for multiusers needs to be considered. This paper proposes a joint DFT and spacetime precoding technique to support multiple users and mitigate the PAPR for optical wireless communication systems when OFDM modulation is used. In our study, we introduce the weighted Bussgang theorem as an alternative method for evaluating the communication performance of hard-clipped systems due to the transmitted power constraint. Our numerical results demonstrate the effectiveness of this approach in analyzing SNR and BER performance. [ABSTRACT FROM AUTHOR]

In this study, it is shown that an efficient organic optocoupler (OPC) can be fabricated using commercially available and solution-processable organic semiconductors. The transmitter is a single-active-layer organic light-emitting diode (OLED) made from a well-known polyparavinylene derivative, Super Yellow. The receiver is an organic light-emitting diode (OLSD) with a single active layer consisting of a mixture of the polymer donor PTB7-Th and the low-molecular-weight acceptor ITIC; the receiver operates without an applied reverse voltage. OLED and OLSD have the same geometry and simple structure without any interlayers: glass/ITO/PEDOT:PSS/(active layer)/Ca/Al; the OPC is formed by OLED and OLSD which adhere tightly to each other. Despite its simple structure, the OPC showed a current transfer ratio of 0.13%, good linearity, and good dynamic performance: a three-decibel cutoff frequency of 170 kHz and response times to a step change in current at the OPC input of 2 μs. Compared to most organic OPC devices with similar performance parameters, where the transmitter and receiver have complex structures with additional interlayers between the active layers and electrodes and the need to apply a reverse voltage to the receiver, the simple design of our OPC reduces the number of fabrication steps and greatly simplifies the device fabrication process. [ABSTRACT FROM AUTHOR]

The editorial discusses the development and applications of Microelectromechanical systems (MEMS) technology, focusing on micromachined acoustic transducers for the audio-frequency range. It highlights the advancements in MEMS technology, particularly in MEMS microphones and speakers, and their impact on various industries. The editorial also explores future research areas, such as improving signal-to-noise ratio and reducing noise in MEMS devices, and discusses measurement methods and applications of MEMS acoustic devices, including acoustic source localization, MEMS microphone arrays for acoustic imaging, and electrical tuning of MEMS acoustic transducer resonant frequency. [Extracted from the article]

The modulation techniques for satellite navigation signals is important for performance such as signal ranging accuracy, spectral concentration, and multipath suppression. The signal design of the next-generation Global Navigation Satellite System (GNSS) should comprehensively consider these performance indicators and provide the optimal choice under specific engineering constraints. In order to achieve a trade-off between spectral concentration and ranging accuracy which are two key performance metrics, in this paper, a dual-channel orthogonal modulation technique based on continuous phase modulation (CPM) is proposed, namely phase compression-holding modulation (PCHM). The signal model, power spectrum characteristics and performance are investigated. The results show that when given a baseband phase pulse, this modulation technique can continuously control the out-of-band (OOB) power radiation and Gabor bandwidth of the signal by adjusting the holding length while preserving the beneficial properties of CPM such as phase continuity and constant envelope. Therefore, we can achieve a trade-off between the two indicators mentioned above and provide more flexible choices for specific problems in GNSS. [ABSTRACT FROM AUTHOR]

To address the challenges posed by chromatic dispersion (CD)-induced power fading and nonlinear signal distortions in double-sideband (DSB) intensity modulation and direct detection (IM/DD) transmission systems, a combination of a Volterra feed-forward equalizer (VFFE) and a Volterra decision-feedback equalizer (VDFE) is widely employed. However, the conventional VFFE-VDFE exhibits significant computational complexity, particularly for longer memory lengths. In this Letter, a low-complexity cluster-assisting look-up table-based VDFE (CLUT-VDFE) is proposed to effectively reduce the computational complexity associated with compensating for CD and nonlinear distortions. By utilizing CLUTs, all multiplication operations required for the implementation of the VDFE are completely eliminated. To validate the effectiveness of the proposed CLUT-VDFE, experiments on a C-band 100-Gb/s PAM-4 transmission system over a 60-km standard single-mode fiber (SSMF) are conducted. The experimental results show that the CLUT-VDFE not only achieves comparable equalization performance to the conventional VDFE but also effectively eliminates multiplication operations and significantly saves 48.8% real-valued additions.