Pre-Chirp-Domain Index Modulation for Affine Frequency Division Multiplexing

Affine frequency division multiplexing (AFDM), tailored as a novel multicarrier technique utilizing chirp signals for high-mobility communications, exhibits marked advantages compared to traditional orthogonal frequency division multiplexing (OFDM). AFDM is based on the discrete affine Fourier transform (DAFT) with two modifiable parameters of the chirp signals, termed as the pre-chirp parameter and post-chirp parameter, respectively. These parameters can be fine-tuned to avoid overlapping channel paths with different delays or Doppler shifts, leading to performance enhancement especially for doubly dispersive channel. In this paper, we propose a novel AFDM structure with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can embed additional information bits into the pre-chirp parameter design for both spectral and energy efficiency enhancement. Specifically, we first demonstrate that the application of distinct pre-chirp parameters to various subcarriers in the AFDM modulation process maintains the orthogonality among these subcarriers. Then, different pre-chirp parameters are flexibly assigned to each AFDM subcarrier according to the incoming bits. By such arrangement, aside from classical phase/amplitude modulation, extra binary bits can be implicitly conveyed by the indices of selected pre-chirping parameters realizations without additional energy consumption. At the receiver, both a maximum likelihood (ML) detector and a reduced-complexity ML-minimum mean square error (ML-MMSE) detector are employed to recover the information bits. It has been shown via simulations that the proposed AFDM-PIM exhibits superior bit error rate (BER) performance compared to classical AFDM, OFDM and IM-aided OFDM algorithms

Mid-frequency prediction of transmission loss using a novel hybrid deterministic and statistical method

A novel hybrid deterministic-statistical approach named ES-FE-SEA method specially used to predict the sound Transmission loss of panels in mid-frequency is proposed in this paper. The proposed hybrid methods takes the best advantages of edged-based smoothing FEM (ES-FEM) and statistical energy analysis (SEA) to further improve the accuracy of mid-frequency transmission loss predictions. The application of ES-FEM will “soften” the well-known “overly-stiff” behavior in the standard FEM solution and reduce the inherent numerical dispersion error. While the SEA approach will deal with the physical uncertainty in the relatively higher frequency range. Two different types of subsystems will be coupled based on “reciprocity relationship” theorem. The proposed was firstly applied to a standard simple numerical example, and excellent agreement with reference results was achieved. Thus the method is then applied to a more complicated model-a 2D dash panel in a car. The proposed ES-FE-SEA is verified by various numerical examples

Hierarchical control strategy for unbalanced voltage in an islanded microgrid

When the microgrid is running in an islanded mode, unbalanced loads result in microgrid voltage unbalance. The voltage unbalance factor at the Point of Common Coupling (PCC) is a key parameter in measurement of microgrid power quality. To improve microgrid power quality, many documents utilize micro-source voltage measurement results to help adjust the unbalance factor of microgrid voltage. However, due to line impedance presence, there are differences between micro-source output voltage and PCC voltage. Therefore, it is impossible for a micro-source to control the unbalance factor of PCC voltage with high precision by measuring its own output voltage. Based on equivalent circuit, the present paper analyzes the negative sequence component relationship among micro-source output voltage, line impedance voltage drop, and PCC voltage. It further proposes a hierarchical-control-based method to control the unbalance factor of PCC voltage with high accuracy, and analyzes the impact of secondary control delay on system stability by root locus calculating. Finally, the control strategy is validated in an islanded microgrid system with two micro-sources. The experimental results show the effectiveness and feasibility of the proposed control strategy.Під час роботи мікроенергосистеми (МЕ) в ізольованому режимі незбалансовані навантаження призводять до дисбалансу напруги у ній. Фактор дисбалансу напруги у точці спільного приєднання (ТСП) є основним параметром при вимірюванні якості електроенергії МЕ. Для підвищення якості електроенергії МЕ використовують результати вимірювань напруги мікроджерел для врегулювання фактора дисбалансу напруги МЕ. Проте через наявність повного вхідного опору лінії існують відмінності між вихідною напругою мікрождерела та напругою ТСП. Тому мікроджерело не може контролювати фактор дисбалансу напруги ТСП з високою точністю шляхом вимірювання власної вихідної напруги. На базі еквівалентної схеми у даній статті аналізуються відношення складової оберненої послідовності між вихідною напругою мікроджерела, падінням напруги повного вхідного опору лінії та напругою у ТСП. Також для контролю фактора дисбалансу напруги ТСП із високою точністю пропонується метод на основі ієрархічного контролю, аналізується вплив затримки вторинного контролю на стабільність системи. Стратегія контролю перевірялася в ізольованій мікроенергосистемі з двома мікроджерелами. Дослідні дані показують ефективність та доцільність запропонованої стратегії контролю.При работе микроэнергосистемы (МЭ) в изолированном режиме несбалансированые нагрузки приводят к дисбалансу напряжения в ней. Фактор дисбаланса напряжения в точке общего присоединения (ТОП) является основным параметром при измерении качества электроэнергии МЭ. Для улучшения качества электроэнергии МЭ используют результаты измерений напряжения микроисточников для урегулирования дисбаланса напряжения МЭ. Однако из-за наличия полного входного сопротивления линии существуют различия между выходным напряжением микроисточника и напряжением ТОП. Поэтому микроисточник не может контролировать дисбаланс напряжения ТОП с высокой точностью путем измерения собственного выходного напряжения. На основании эквивалентной схемы в данной статье анализируется отношение составляющей обратной последовательности между выходным напряжением микроисточника, падением напряжения полного входного сопротивления линии и напряжением в ТОП. Также для контроля фактора дисбаланса напряжения ТОП с высокой точностью предлагается метод на основе иерархического контроля, анализируется влияние задержки вторичного контроля на стабильность системы. Стратегия контроля проверялась в изолированной микроэнергосистеме с двумя микроисточниками. Опытные данные показывают эффективность и целесообразность предлагаемой стратегии контроля

Prompting Segmentation with Sound is Generalizable Audio-Visual Source Localizer

Never having seen an object and heard its sound simultaneously, can the model still accurately localize its visual position from the input audio? In this work, we concentrate on the Audio-Visual Localization and Segmentation tasks but under the demanding zero-shot and few-shot scenarios. To achieve this goal, different from existing approaches that mostly employ the encoder-fusion-decoder paradigm to decode localization information from the fused audio-visual feature, we introduce the encoder-prompt-decoder paradigm, aiming to better fit the data scarcity and varying data distribution dilemmas with the help of abundant knowledge from pre-trained models. Specifically, we first propose to construct Semantic-aware Audio Prompt (SAP) to help the visual foundation model focus on sounding objects, meanwhile, the semantic gap between the visual and audio modalities is also encouraged to shrink. Then, we develop a Correlation Adapter (ColA) to keep minimal training efforts as well as maintain adequate knowledge of the visual foundation model. By equipping with these means, extensive experiments demonstrate that this new paradigm outperforms other fusion-based methods in both the unseen class and cross-dataset settings. We hope that our work can further promote the generalization study of Audio-Visual Localization and Segmentation in practical application scenarios.Comment: 11 pages, 7 figures, modified the additional material

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning

Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influence of specific data samples on a given model without the need to retrain it from scratch. Accordingly, existing methods focus on maximizing user privacy protection. However, there are different degrees of privacy regulations for each real-world web-based application. Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios. Furthermore, designing the MU algorithm with simple control of the aforementioned trade-off is desirable but challenging due to the inherent complex interaction. To address the challenges, we present Controllable Machine Unlearning (ConMU), a novel framework designed to facilitate the calibration of MU. The ConMU framework contains three integral modules: an important data selection module that reconciles the runtime efficiency and model generalization, a progressive Gaussian mechanism module that balances privacy and model generalization, and an unlearning proxy that controls the trade-offs between privacy and runtime efficiency. Comprehensive experiments on various benchmark datasets have demonstrated the robust adaptability of our control mechanism and its superiority over established unlearning methods. ConMU explores the full spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners to account for different real-world regulations. Source code available at: https://github.com/guangyaodou/ConMU

Surface roughness of thin wood veneers sliced from laminated green wood lumber

Freshly-felled Chinese fir (Cunninghamia lanceolate), Masson Pine (Pinus massoniana) and Camphor Tree (Cinnamomum camphora) logs were reconstituted to form laminated lumber with moisture content above fiber saturation point by slicing, finger-jointing, gluing, and cold-pressing processes. The laminated lumber was then sliced into wood veneers, which were air-dried to about 15% moisture content. The surface roughness of the veneer was tested in comparison with two commercial engineered wood veneers using a stylus tracing method. The influence of  the wood surface roughness was relatively  small  for  the  wood  species chosen due  to  their  similar densities. All roughness parameter values were consistently larger along the transverse direction compared with these along longitudinal direction. The values of surface roughness at the finger-joint region were higher than these that at the non-finger-joint region along both longitudinal direction and transverse direction. The two engineered wood veneers had surface roughness values noticeably smaller in the longitudinal direction, but their values in transverse direction were comparable and even larger compared with these of the prepared wood veneers including both non-finger-joint and finger-joint regions.  Overall, the process of laminating finger-jointed green wood planks and subsequently slicing can be used to yield acceptable wood veneers with sufficient surface quality

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features