Search Results (60)

To address the sorting challenge of multiple stations and multiple networking modes (networks with inconsistent features and networks with similar features but asynchrony) in complex electromagnetic environments, this paper proposes a full-network station sorting algorithm that integrates Hop Describe Word (HDW), hierarchical clustering, and temporal sorting. First, from the perspectives of hardware differences, channel interference, and networking strategies, it is demonstrated that “there theoretically exist no multiple frequency-hopping networks that are completely synchronized and have consistent features”, thereby defining the sorting boundary of the algorithm. Second, “preliminary clustering sorting” is used to separate networks with significant differences in HDW static features, and then the “temporal sorting algorithm” designed in this paper is applied to overcome the sorting bottleneck of networks with similar features but asynchrony. Finally, based on the feature rules of the sorted networks, ARIMA temporal prediction and K-nearest neighbor (KNN) feature completion are adopted to achieve accurate recovery of missing signals. Experimental results show that in scenarios with a signal-to-noise ratio (SNR) of −8 dB to 5 dB and a signal loss rate of 0% to 15%, the proposed algorithm achieves an average sorting accuracy of 96.7%, a sorting completeness of 94.3%, and a robustness fluctuation range of only ±4.2% for 10 mixed networks. It significantly outperforms the traditional K-means algorithm and the single HDW clustering algorithm, and can effectively meet the needs of military and civilian spectrum reconnaissance and station sorting. Full article

Large language models (LLMs) have been increasingly applied in Automatic Speech Recognition (ASR), achieving significant advancements. However, the performance of LLM-based ASR (LLM-ASR) models remains unsatisfactory when applied across domains due to domain shifts between acoustic and linguistic conditions. To address this challenge, we propose a decoupled two-stage domain adaptation framework that separates the adaptation process into text-only and audio-only stages. In the first stage, we leverage abundant text data from the target domain to refine the LLM component, thereby improving its contextual and linguistic alignment with the target domain. In the second stage, we employ a pseudo-labeling method with unlabeled audio data in the target domain and introduce two key enhancements: (1) incorporating decoupled auxiliary Connectionist Temporal Classification (CTC) loss to improve the robustness of the speech encoder under different acoustic conditions; (2) adopting a synchronous LLM tuning strategy, allowing the LLM to continuously learn linguistic alignment from pseudo-labeled transcriptions enriched with domain textual knowledge. The experimental results demonstrate that our proposed methods significantly improve the performance of LLM-ASR in the target domain, achieving a relative word error rate reduction of 19.2%. Full article

In the context of post-pandemic remote work normalization and the emergence of the metaverse, virtual workspaces have attracted significant attention as critical digital infrastructure with promising application prospects. While virtual workspaces enable efficient task performance, compared with traditional ones, the lack of emotional connection between humans and machines adversely affects participants’ mental health. The emergence of affective computing has made it possible to endow virtual workspaces with “affective intelligence”. Therefore, this study aims to clarify the relationship between color and participants’ emotions in virtual workspaces through an experiment involving 48 participants, and eight virtual workspaces were constructed, incorporating four color conditions (red, blue, yellow, and green) and two workspace types (shared and single). Data were synchronously collected using the Positive and Negative Affect Schedule (PANAS), a questionnaire item on arousal, electrodermal activity (EDA), and heart rate variability (HRV). The results successfully established specific associations between colors and emotions: red with “anxious”, yellow with “happy”, and blue with “calm”. Although no specific emotion word was identified for green, this study successfully achieved the emotion classification of virtual workspaces and constructed a corresponding dataset. These findings provide a theoretical foundation for the development of affective computing models. Full article

The literature for brown lemming (Lemmus trimucronatus) and collared lemming (Dicrostonyx groenlandicus) population cycles was revisited from Barrow, Alaska. This review covered observations and research primarily from 1946 to 1974. Much of what we know about brown lemming cycles from North America was derived from these early studies. The data for collared lemming, however, are far less extensive and only a minor part of the historical research at Barrow. Nonetheless, important information was discovered. Collectively, the historical literature is confusing and sometimes contradictory. The time intervals, amplitude, and density of lemming populations from Barrow varied greatly from year to year. For example, in most papers, 1956 was considered a lemming population high, but in a major research paper in 1993, the 1956 data was sometimes included and sometimes deleted because it did not meet an arbitrary mathematical definition of a population high. Qualitative explanations were often used to support the lemming population cycle concept when it was apparently in flux or did not exist. Other investigations suggested synchronous lemming population fluctuations over wide geographic areas did not occur, but rather were localized most of the time. Even within a specific local area, lemming densities varied with habitat. Presumably, higher densities were in higher quality habitats, but this could vary somewhat with season. It is unlikely that lemming migrations occurred; however, local movements of large numbers of lemmings were witnessed. Although many studies suggested a specific event influenced lemming population fluctuations, overall, the data suggest multiple factors acting synergistically drove the x-fold increases and x-fold decreases in lemming populations at Barrow. Other qualitative observations, and quantitative studies suggest lemming population fluctuations affect and have an effect on the survival and reproduction of other species of birds and mammals at Barrow. Brown lemmings should be considered an indicator of the health of the Arctic environment at Barrow. Clearly, population fluctuations of lemmings at Barrow existed with an average interval for peak populations of about 3.8 years, ranging from 2 to 6 years, depending on what data was included and how it was analyzed. The conundrum, however, is defining a peak. Furthermore, one must ask if an average interval between peak lemming populations is really a cycle. The data support population fluctuations; however, the four annual phases of the cycle (e.g., increase, peak, decline, low) did not repeat themselves in sequence. Overall, perhaps cycles did not exist in the strictest definition of the word at Barrow. Perhaps it is time to redefine Barrow lemming “cycles” as annual population fluctuations that exhibit patterns over time. Full article

Digitization and green low-carbon are the main directions of China’s economic development in the future. This paper aims to explore the relationship between improvements in the digital level of manufacturing industry segments and carbon emissions. It is found that the digitization level of China’s manufacturing industry segments is still at a low level, which needs to be further improved, and the digitization level of technology-intensive industries is higher than that of capital-intensive and labor-intensive industries. There is a serious misallocation of production factors and R&D factors among manufacturing industries, which is mainly caused by capital factors. Improvement in the digital level of manufacturing industry segmentation can significantly improve the double-layer factor allocation efficiency of the manufacturing industry, and can synchronously realize carbon emissions reduction through improvements in the double-layer factor allocation efficiency of the manufacturing industry; in other words, the improvement in the digital level of China’s manufacturing industry has the dual effects of improving factor allocation efficiency and carbon emissions reduction. Further analysis shows that this effect has significant heterogeneity of ownership. Therefore, China should focus on accelerating the digital transformation of the manufacturing industry, improve the allocation efficiency of traditional and R&D factors in the manufacturing industry through this digital transformation, and accelerate the realization of green and low-carbon development. Full article

In this paper, we consider the use of high-definition maps for autonomous vehicle (AV) localization. An autonomous vehicle may have a variety of sensors, including cameras, lidars, and Global Positioning System(GPS) sensors. Each sensor technology has its own pros and cons; for example, GPS may not be very effective in a city environment with high-rise buildings; cameras may not be very effective in poorly illuminated environments; and lidars simply generate a relatively dense local point cloud. In a typical autonomous vehicle system, all of these sensors are present and sensor fusion algorithms are used to extract the most accurate information. Using our AV research vehicle, we drove on our university campus and recorded Real Time Kinematic-GPS(RTK-GPS) (ZED-F9P) and Velodyne Lidar (VLP-16) data in a time-synchronized fashion. In other words, for every GPS location on our campus, we have lidar-generated point cloud data, resulting in a simple high-definition map of the campus. The main challenge that we look to overcome in this paper is thus: given a high-definition map of the environment and local point cloud data generated by a single lidar scan, determine the AV research vehicle’s location by using point cloud “similarity” metrics. We first propose a computationally simple similarity metric and then describe a recursive Kalman filter-like approach for localization. The effectiveness of the proposed similarity metric has been demonstrated using the experimental data. Full article

Sync word-based frame synchronization is an established method of frame synchronization that is employed in many low-resource applications because of its simplicity, and it involves the attachment of sync words (digital binary sequences) to the frames, which are then used for detection. Despite its simplicity, the method is underutilized today because there is no available hardware scheme that can perform correlation-based sync word frame synchronization with long sync words (>80 bits) efficiently. For this reason, the sync word methods that are used typically employ sync words of shorter lengths (<50 bits) with limited accuracy. This article introduces a highly modifiable, minimalist hardware architecture that performs correlation-based sync word frame synchronization using long sync words with evident accuracy gains over existing methods. As a bonus, the architecture not only detects the frame but also captures its payload regardless of size. Its low complexity allows for its deployment at a low cost in terms of hardware resources and power while providing high bit rates. Its flexibility is demonstrated by using a low-cost FPGA for implementation. Full article

We explore an oscillator with nonlinear functions and equilibrium lines that displays chaos. The equilibrium stability and complexity of the oscillator have been analysed and investigated. The presence of multiple equilibrium lines sets it apart from previously reported oscillators. The synchronization of the oscillator is considered as an application for secure communications. An observer is designed by considering a transmitted signal as a state, in other words, by injecting a linear function satisfying Lipschitz’s condition to the proposed oscillator. Moreover, the adaptive control of the new oscillator is obtained. Full article

The aim of this paper was to explore the application of multi-channel synchronized dynamic strain gauges in monitoring the neutral axis (N.A.) position of prestressed concrete box girders. The N.A. position has recently been proposed as an indicator for monitoring the health of bridge structures. Laboratory experiments were conducted on a prestressed T-beam under different prestress level conditions to investigate the correlation between the prestress magnitude and the N.A. position. In the development of the multi-channel synchronized dynamic strain gauges, edge computing was employed to significantly reduce the amount of data transmitted from the sensor nodes on-site. In edge computing, only the dynamic strain response caused by the maximum vehicle load in each minute is transmitted. This approach greatly enhances the monitoring efficiency and enables the realization of on-site non-computer-based monitoring systems. The laboratory test results of the prestressed T-beam showed that the N.A. position tends to move slightly downward as the prestress force increases. In other words, when the prestress force decreases due to loss, the N.A. position exhibits a slight upward movement. This study selected a newly constructed prestressed box girder as the subject for on-site measurement of the N.A. position using multi-channel synchronized dynamic strain gauges shortly after the prestress was applied. The on-site monitoring data indeed revealed a gradual upward movement of the N.A. position. This phenomenon confirmed that soon after the completion of prestressed concrete bridges, there is a gradual loss of prestress due to the significant shrinkage and creep effects of the early-age concrete. The on-site monitoring result aligned with the findings from the laboratory experiments, where the N.A. position was observed to move upward as the prestress decreased. Full article

Using an optimality theoretic analysis, this study offers a conception of the problem of plural realization in Eastern Andalusian Spanish (EAS) where plural suffix /s/ was deleted diachronically that differs from other accounts that assign the EAS plural an underlying suffixal /s/ synchronically. Using alignment constraints, we argue that plural /s/ does not appear in the underlying form synchronically in EAS, but that instead the plural morpheme is represented by a floating [–ATR]PL feature that aligns to the right edge of the word and spreads left. The [–ATR] feature, represented phonetically as a laxing or opening of vowels, applies to all mid vowels, low vowels in word final position, and combines with vowel epenthesis to explain Eastern Andalusian pluralization tendencies in words with final consonants. We discuss the behavior of high vowels, which can be transparent to harmony, and focus in particular on the plural of words that end in a final stressed vowel that have been rarely discussed in the EAS literature. We develop an optimality-theoretic analysis on the Granada variety and extend that analysis to other varieties with somewhat different patterns. Full article

Many studies suggest a significant association between individual essential trace elements (ETEs) and cognitive impairment in older adults, but evidence of the synchronized effect of multiple ETEs on cognitive function is lacking. We investigated the association between multiple ETEs, cognitive impairment with no dementia (CIND), and executive function in older Korean adults, using the Bayesian kernel machine regression (BKMR) model. Three hundred and thirty-six older adults were included as the study population and classified as the CIND and control groups. Blood manganese (Mn), copper (Cu), zinc (Zn), selenium (Se), and molybdenum (Mo) were measured as relevant ETEs. The frontal/executive tests included digit symbol coding (DSC), the Korean color word Stroop test (K-CWST), a controlled oral word association test (COWAT), and a trial-making test (TMT). Overall, the BKMR showed a negative association between multiple ETEs and the odds of CIND. Mn was designated as the most dominant element associated with the CIND (PIP = 0.6184), with a U-shaped relationship. Cu and Se levels were positively associated with the K-CWST percentiles (β = 31.78; 95% CI: 13.51, 50.06) and DSC percentiles (β = 25.10; 95% CI: 7.66, 42.53), respectively. Our results suggest that exposure to multiple ETEs may be linked to a protective mechanism against cognitive impairment in older adults. Full article

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm. Full article

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas, owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages. Full article

Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS. Full article

This paper focuses on the equilibrium problem of an urban public transportation system with time delay. Time delay, multi-weights, and stochastic disturbances are considered in the urban public transportation system. Hence, one can regard the urban public transportation system as a stochastic multi-weighted delayed complex network. By combining graph theory and the Lyapunov method, the global Lyapunov function is constructed indirectly. Moreover, the response system can realize synchronization with the drive system under the adaptive controller. In other words, the urban public transportation system is balanced in the actual running traffic network. Finally, numerical examples about the Chua system and small-world network are presented to confirm the accuracy and validity of the theoretical results. Full article