Included here are figures and other relevant data from the paper "Characterizing Interconnects to 325 GHz". Abstract: We developed an interconnect characterization procedure that first embeds the interconnect into the error boxes of a multiline thru-reflect-line calibration and subsequently de-embeds the interconnect with a multi-tiered calibration. We experimentally validated our method with distributed contactless interconnects in the form of broadside coupled coplanar waveguides as a test case. We find excellent agreement between experiment, full-wave simulations, and a distributed model of contactless interconnects. This work provides a rigorous method to accurately characterize interconnects when conventional approaches are not applicable.

We present a method for accurately determining the permittivity of dielectric materials in 3D integrated structures at broadband RF frequencies. With applications of microwave and millimeter-wave electronics on the rise, reliable methods for measuring the electrical properties of dielectrics used in integrated circuits are critical. We outline an on-wafer method for extracting the permittivity of a 3D multilayer glass structure from 100 MHz to 30 GHz using S-parameter measurements of different calibration chips. Our method can be used to inform better design of metrology for dielectric materials for 3D integrated circuit technologies.This is data associated with the manuscript "Characterizing the broadband RF permittivity of 3D-integrated layers in a glass wafer stack from 100 MHz to 30 GHz" for the 2024 International Microwave Symposium (IMS) in Washington, D.C. The manuscript is currently under review by ERB in the NPS system under PUB ID 957051 / N2024-0193

This dataset is for the publication entitled "LABORATORY-BASED REFERENCE CHANNELS FOR MILLIMETER-WAVE WIRELESS DEVICE MEASUREMENTS." The dataset includes results from experiments in a simulated industrial wireless channel operated at 28 GHz. Results include synthetic-aperture beamforming data, and error-vector-magnitude information calculated from synthetic-aperture and directional-antenna measurements.

Figures and relevant data from the paper "Broadband Electromagnetic Properties of Engineered Flexible Absorber Materials" are found here . The paper was published on Advanced Materials Technologies in 2023. ABSTRACT: Flexible and stretchable materials have attracted significant interest for applications in wearable electronics and bioengineering fields. Recent developments also incorporate mounted and embedded microwave circuits, components, and systems with engineered flexible materials that operate over a broadband frequency range (~1 to 100 GHz). Here we demonstrate a simple, low-cost, flip-chip technique where flexible materials are placed on top of coplanar waveguide (CPW) transmission lines for material property measurement. We apply on-wafer error correction and de-embedding techniques to determine broadband electromagnetic properties of the material-loaded transmission line segments. Finite-element simulations of material-loaded devices were employed along with the broadband measurements to estimate the electromagnetic material properties. To demonstrate this technique, we fabricated flexible polydimethylsiloxane (PDMS) composites with varying concentrations of Barium Hexaferrite (BaM) nanoparticles for potential applications in electromagnetic shielding and quantified the complex permittivity and permeability of the composites up to 110 GHz using our broadband scattering-parameter measurements. We fit the frequency-dependent permeability to models describing the ferromagnetic resonance of barium hexaferrite (BaM) nanoparticles in PDMS and estimated the constituent nanoparticle properties using the Maxwell-Garnett mixing model. This study paves way to exploit a wide range of engineered materials in flexible, wearable, and biomedical electronics applications and presents a convenient methodology to extract important broadband electromagnetic properties for applications such as electromagnetic shielding.

This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).

This data is provided as a supplement to NIST Technical Note 2159 Laboratory Method for Recording AWS-3 LTE Waveforms available at https://doi.org/10.6028/NIST.TN.2159. In particular, the data provided here are a compressed version of all the IQ recordings discussed in the report, with diagnostic information. The data is structured as a compressed archive, Data.zip, for each experimental configuration and capture repeat, resulting in 112 compressed archives organized by directory structure. The compressed archive, Data.zip, contains six files: IQ.csv, configuration.csv, diagnostic.csv, UE_diagnostic_spectrogram.csv, IQ_spectrogram.csv and IQ_spectrogram.png. - IQ.csv Description: Measured IQ at a sampling rate of 61.44 MS / s Contents: I and Q as signed integers, relative units- configuration.csv Description: The experimental configuration under which the IQ recording was made. Corresponds to a row in Table 4.1: Test Configurations of the technote. Contents: Scheduler_Awareness, Scheduler_Allocation, PowerControl_PUSCH, PowerControl_PUCCH,Scheduler_RBMask, P0_PUSCH, P0_PUCCH, alpha, UTG_NumUEs, UTG_ULRate, UTG_TrafficType, UTG_UERSRP, DUT_UE_ULRate, DUT_UE_TrafficType, UT_UE2_ULRate, DUT_UE2_TrafficType - diagnostic.csv Description: UE Diagnostic information with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), Total Tx Power (dBm), Resource Block Start, Number of Resource Blocks, MCS Index - UE_diagnostic_spectrogram.csv Description: The UE diagnostic report of power and resource block allocation organized into physical resource blocks with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), 0 ? max physical resource blocks in shared channel with a power in mw. - IQ_spectrogram.csv Description: IQ data organized into physical resource blocks with a time axis aligned to the diagnostic information. Contents: Elapsed time (ms), 0 ? 199 resource blocks with a relative power. - IQ_spectrogram.png Description: Plot of 140 ms of spectrogram, max relative power versus frequency and mean relative power, corresponds to appendix B of technote. Contents: Single image

This data is provided as a supplement to NIST Technical Note 2159 Laboratory Method for Recording AWS-3 LTE Waveforms available at https://doi.org/10.6028/NIST.TN.2159. In particular, the data provided here are a compressed version of all the IQ recordings discussed in the report, with diagnostic information. The data is structured as a compressed archive, Data.zip, for each experimental configuration and capture repeat, resulting in 112 compressed archives organized by directory structure. The compressed archive, Data.zip, contains six files: IQ.csv, configuration.csv, diagnostic.csv, UE_diagnostic_spectrogram.csv, IQ_spectrogram.csv and IQ_spectrogram.png. - IQ.csv Description: Measured IQ at a sampling rate of 61.44 MS / s Contents: I and Q as signed integers, relative units- configuration.csv Description: The experimental configuration under which the IQ recording was made. Corresponds to a row in Table 4.1: Test Configurations of the technote. Contents: Scheduler_Awareness, Scheduler_Allocation, PowerControl_PUSCH, PowerControl_PUCCH,Scheduler_RBMask, P0_PUSCH, P0_PUCCH, alpha, UTG_NumUEs, UTG_ULRate, UTG_TrafficType, UTG_UERSRP, DUT_UE_ULRate, DUT_UE_TrafficType, UT_UE2_ULRate, DUT_UE2_TrafficType - diagnostic.csv Description: UE Diagnostic information with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), Total Tx Power (dBm), Resource Block Start, Number of Resource Blocks, MCS Index - UE_diagnostic_spectrogram.csv Description: The UE diagnostic report of power and resource block allocation organized into physical resource blocks with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), 0 ? max physical resource blocks in shared channel with a power in mw. - IQ_spectrogram.csv Description: IQ data organized into physical resource blocks with a time axis aligned to the diagnostic information. Contents: Elapsed time (ms), 0 ? 199 resource blocks with a relative power. - IQ_spectrogram.png Description: Plot of 140 ms of spectrogram, max relative power versus frequency and mean relative power, corresponds to appendix B of technote. Contents: Single image

This repository contains results for experiments on generative modeling of synthetic Orthogonal-Frequency Division Multiplexing (OFDM) communication signals. (This record supersedes Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks, formerly at https://doi.org/10.18434/mds2-2428)

An electrical connector with improved crosstalk compensation is disclosed. By including at least one coupling with a different frequency dependency than other couplings in the connector, crosstalk compensation performance is improved over a greater frequency range. The different frequency dependency may, for example, be used to compensate for phase shifts caused by distances between compensation circuitry and the plug/jack interface. Embodiments for decreasing these distances are also enclosed.

< 자율주행 시나리오 데이터셋: 멀티센서퓨전 > - 데이터셋 유형 - 멀티센서(카메라-LiDAR) 퓨전 Bounding Box-Panoptic Segmentation-Cuboid/Track ID 데이터셋 - 10초 동안 주행하며 수집한 자율주행 데이터를 5fps로 추출해 구축한 데이터셋 - 데이터셋 구성 1. 원천데이터_카메라 센서 데이터 1) 전방 FHD 카메라 이미지 데이터(.png): 50frames 2) 후방 FHD 카메라 이미지 데이터(.png): 50frames 3) 좌측방 FHD 카메라 이미지 데이터(.png): 50frames 4) 우측방 FHD 카메라 이미지 데이터(.png): 50frames 2. 원천데이터_LiDAR 센서 데이터 1) 전방위 128ch LiDAR 포인트 클라우드 데이터(.pcd): 50frames 2) 전방 장거리 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 3) 전방 중거리 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 4) 후방 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 5) 후방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 6) 좌측방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 7) 우측방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 8) 전방위 + 후방 사각지대 + 좌측방 사각지대 + 우측방 사각지대 데이터를 융합한 포인트 클라우드 데이터(.pcd): 50frames 3. 원천데이터_메타데이터(파일 속성 정보, 센서 파라미터 정보, 차량 절대위치 및 자세 정보, 차량 상태 정보, 주행 환경 및 상황 정보) 1) 전방 카메라 이미지 메타데이터(.json): 50frames 2) 후방 카메라 이미지 메타데이터(.json): 50frames 3) 좌측방 카메라 이미지 메타데이터(.json): 50frames 4) 우측방 카메라 이미지 메타데이터(.json): 50frames 5) 융합된 전방위 LiDAR 포인트 클라우드 메타데이터(.json): 50frames 4. 2D Bounding Box 어노테이션 데이터 1) 전방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 2) 후방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 3) 좌측방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 4) 우측방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 5. Panoptic Segmentation 어노테이션 데이터 1) 전방 카메라 이미지 Panoptic Segmentation 데이터(.json): 50frames 2) 전방 카메라 이미지 Panoptic Segmentation 마스크 이미지(.png): 50frames 3) 전방 카메라 이미지 Semantic Segmentation 마스크 이미지(.png): 50frames 4) 전방 카메라 이미지 Instance Segmentation 마스크 이미지(.png): 50frames 6. Cuboid(3D Bounding Box)/Track ID 어노테이션 데이터 1) 융합된 전방위 LiDAR 포인트 클라우드 Cuboid/Track ID 데이터(.json): 50frames 7. 데이터셋 정의서