Sound Effect (SFX) generation, primarily aims to automatically produce sound waves for sounding visual objects in images or videos. Rather than learning an automatic solution to this task, we aim to propose a much broader system, AutoSFX, designed to automate sound design for videos in a more efficient and applicable manner. AutoSFX capitalizes on this concept by aggregating multimodal representations by cross-attention and leverages a diffusion model to generate sound with visual information embedded. AutoSFX also optimizes the generated sounds to render the entire soundtrack for the input video, leading to a more immersive and engaging multimedia experience by performing seamless transitions between sound clips and harmoniously mixing sounds playing simultaneously. We have developed a user-friendly interface for AutoSFX enabling users to interactively engage in the SFX generation for their videos with particular needs. To validate the capability of our vision-to-sound generation, we conducted comprehensive experiments and analyses using the widely recognized VEGAS and VGGSound test sets, yielding promising results. We also conducted a user study to evaluate the performance of the optimized soundtrack and the usability of the interface. Overall, the results revealed that our AutoSFX provides a viable sound landscape solution for making attractive videos.

In this paper, three polytypes of p-(6H, 4H, 3C)SiC and n-GaN heterojunction single-drift (SDR), and double-drift (DDR) impact-ionization-avalanche-transit-time (IMPATT) diodes are studied. We conduct the numerical simulations of the direct current (DC), radio frequency (RF), and noise characteristics of IMPATT diodes The results show that (6H)SiC/GaN IMPATT diodes have the optimum RF characteristics. Among them, the RF power density of (6H)SiC/GaN DDR IMPATT diode can reach 2.23 MW/cm2. The DC-RF conversion efficiency of (6H)SiC/GaN SDR IMPATT diode can reach 23.4%. Their noise performances are being examined in the meantime. The (3C)SiC/GaN IMPATT has better noise characteristics in the SDR structure, while (4H)SiC/GaN IMPATT has better noise characteristics in the DDR structure. This study provides a reference for the design and fabrication of IMPATT diodes.

Accelerated passive cycling test is a typical and effective way to evaluate the substrate solder layer degradation of power modules due to thermal stresses. The main failure mechanism is the initiation and penetration of cracks inside the solder layer and contributes to the overall thermal resistance rise of the module. Based on passive cycling test results of eight modules with 48 substrates in total under two testing conditions, this paper models the behaviour of the solder crack growth and its impact on the thermal resistance of power module on device-level and module-level. By dividing substrate solders into several groups, results show that solders in the middle of the power module under test withstand the severest thermo-mechanical stress and have higher crack length growth rate in a Logarithmic law based crack model. It indicates that diodes on top of the highly stressed solder areas have much lower lifetime and suffer from high reliability concern under passive cycles.

A novel switching power loss estimation method for modular multilevel converters (MMCs) with the nearest level modulation (NLM) is introduced in this paper. It focuses on the switching actions caused only by change of the inserted submodule (SM) number per arm, named as the essential switching transitions. Every essential switching action can be identified under the assumption of ideal sinusoidal arm voltage reference. The corresponding arm current value determining the switching energy can be obtained with the exclusive use of the exact time of the switching action. Effects of the modulation index and the power factor on the power loss are also taken into account. Simulations have been performed on a 30-MVA three-phase grid-connected MMC system with 20 SMs per arm. The results have confirmed the effectiveness of the proposed method, where high estimation accuracy can be achieved under a range of operating conditions.

The reliability aspect study of Modular Multilevel Converter (MMC) is of great interest in industry applications, such as offshore wind. Lifetime prediction of key components is an important tool to design MMC with fulfilled reliability specifications. While many efforts have been made to the lifetime prediction of IGBT modules in renewable energy applications by considering long-term varying operation conditions (i.e., mission profile), the justifications of using the associated mission profiles are still missed. This paper investigates the impact of mission profile data resolutions and electrical power modeling methods on the estimated lifetime of IGBT modules in an MMC for offshore wind power application. In a 30 MW MMC case study, an annual wind speed profile with a resolution of 1 s/data, 10 minute/data, and 1 hour/data are considered, respectively. A method to re-generate higher resolution wind speed data from lower resolution data is introduced as well. Based on the wind speed data, IEC 61400-12-1 power curve model and a wind speed-power stochastic model are compared as well. Five mission profile modeling scenarios are compared in terms of the predicted lifetime of the IGBT modules used in the MMC, resulting in significant differences. The study serves as a first step to quantify the impact of mission profile modeling on lifetime prediction, and to provide a guideline on mission profile collection for the presented application.

For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.