A 23 GHz Active Mixer with Integrated Diode Linearizer in SiGe BiCMOS Technology

Active mixers operating at 23 GHz are designed and fabricated in SiGe technology.An integrated diode linearizer is used to improve the linearity of the mixer.Measurement and simulation show excellent agreement.Typically,10 dB double-sideband noise figure, 10 dBm IIP3 and 2 dB conversion gain are measured, featuring low noise and high linearity in a same design

High-Accuracy Multicommodity Flows via Iterative Refinement

The multicommodity flow problem is a classic problem in network flow and combinatorial optimization, with applications in transportation, communication, logistics, and supply chain management, etc. Existing algorithms often focus on low-accuracy approximate solutions, while high-accuracy algorithms typically rely on general linear program solvers. In this paper, we present efficient high-accuracy algorithms for a broad family of multicommodity flow problems on undirected graphs, demonstrating improved running times compared to general linear program solvers. Our main result shows that we can solve the _{q, p}-norm multicommodity flow problem to a (1 + ε) approximation in time O_{q, p}(m^{1+o(1)} k² log(1/ε)), where k is the number of commodities, and O_{q, p}(⋅) hides constants depending only on q or p. As q and p approach to 1 and ∞ respectively, _{q, p}-norm flow tends to maximum concurrent flow. We introduce the first iterative refinement framework for _{q, p}-norm minimization problems, which reduces the problem to solving a series of decomposable residual problems. In the case of k-commodity flow, each residual problem can be decomposed into k single commodity convex flow problems, each of which can be solved in almost-linear time. As many classical variants of multicommodity flows were shown to be complete for linear programs in the high-accuracy regime [Ding-Kyng-Zhang, ICALP'22], our result provides new directions for studying more efficient high-accuracy multicommodity flow algorithms

Automatic 3D Model Annotation by a Two-Dimensional Hidden Markov Model

In this paper, a new method of 3D model automatic annotation is proposed based on a two-dimensional Hidden Markov Model(2-D HMM). Growing importance in the last years Hidden Markov Models are a widely used methodology for sequential data modeling. Recent years, HMMs are applied to research of automatic annotation, such as images and models annotation. The three basic problems with HMM-liked model are also solved in our model. Our modeling process has two steps, those are training and testing. In the proposed approach, each object is separated into several bins by a spiderweb model and a shape function D2 is computed for each bin. These feature vectors are then arranged in a sequential fashion to compose a sequence vector, which is used to train HMMs. In 2-D HMM, we assume that feature vectors are statistically dependent on an underlying state process which has transition probabilities conditioning the states of two neighboring bins. Thus the dependency of two dimensions is reflected simultaneously. To classify an object, the maximized posteriori probability is calculated by a given model and the observed sequence of an unknown object. Comparing with the general HMM, 2-D HMM gets more information from the neighboring bins. So the system of 2-D HMM performs well on images and model annotation. Analysis and experimental results show that the proposed approach performs better than existing ones in database. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.494

Lei Zu and Can Cong Weaved the Splendid Brocade and the Belt and Road Is Writing a Brilliant Chapter——The Chengdu Consensus of the Academic Seminar on the Land of Abundance and the Silk Road

“Dawn sees saturated reds; the town’s heavy with blooms.” We, more than 90 experts and scholars from home and abroad, attended The Academic Seminar on the Land of Abundance and the Silk Road held by the Association of Chinese Historians, the Guangming Daily Press, the Society for Chinese Archaeology, the Chinese Society on Ancient Capitals, the School of Archaeology and Museology of Peking University, and Sichuan Academy of Social Sciences, on April 8, 2017 in Chengdu. We are to conduct in-depth exploration of the history between Sichuan and the Silk Road, the development context, as well as jointly discuss the strategies for the development of Sichuan and the Belt and Road. We reached the following consensus.

Visual-UWB Navigation System for Unknown Environments

Navigation applications relying on the Global Navigation Satellite System (GNSS) are limited in indoor environments and GNSS-denied outdoor terrains such as dense urban or forests. In this paper, we present a novel accurate, robust and low-cost GNSS-independent navigation system, which is composed of a monocular camera and Ultra-wideband (UWB) transceivers. Visual techniques have gained excellent results when computing the incremental motion of the sensor, and UWB methods have proved to provide promising localization accuracy due to the high time resolution of the UWB ranging signals. However, the monocular visual techniques with scale ambiguity are not suitable for applications requiring metric results, and UWB methods assume that the positions of the UWB transceiver anchor are pre-calibrated and known, thus precluding their application in unknown and challenging environments. To this end, we advocate leveraging the monocular camera and UWB to create a map of visual features and UWB anchors. We propose a visual-UWB Simultaneous Localization and Mapping (SLAM) algorithm which tightly combines visual and UWB measurements to form a joint non-linear optimization problem on Lie-Manifold. The 6 Degrees of Freedom (DoF) state of the vehicles and the map are estimated by minimizing the UWB ranging errors and landmark reprojection errors. Our navigation system starts with an exploratory task which performs the real-time visual-UWB SLAM to obtain the global map, then the navigation task by reusing this global map. The tasks can be performed by different vehicles in terms of equipped sensors and payload capability in a heterogeneous team. We validate our system on the public datasets, achieving typical centimeter accuracy and 0.1% scale error.Comment: Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018

Probing critical spin fluctuations with a composite magnetoelectric method: A case study on a Kitaev spin liquid candidate Na3_3Co2_2SbO6_6

In correlated quantum materials, divergent critical fluctuations near the quantum critical point are often closely associated with exotic quantum phases of matter, such as unconventional superconductivity and quantum spin liquids. Here we present a simple yet highly sensitive composite magnetoelectric (ME) method for detecting the critical spin fluctuations in quantum magnets. The ME signal is proportional the magnetostriction coefficient, which directly probes the product of magnetization and spin-spin correlation. As a demonstration, the composite ME method is applied to a Kitaev quantum spin liquid candidate Na$_3$Co$_2$SbO$_6$, which shows signs of magnetic field-induced quantum criticality. Notably, the ME signal prominently diverges at the magnetic field-induced tricritical points, particularly at a tricritical point that lies in close proximity to a zero-temperature quantum critical point. A crucial aspect of these tricritical points is their tunability through the modification of the in-plane magnetic field's direction. The direction of magnetic field can thus serve as a handful yet important tuning parameter, alongside pressure and chemical doping, for searching quantum critical points in quantum magnets with pronounced magnetic anisotropy.Comment: 6 pages, 4 figure

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data

Systematic Absences of Optical Phonon Modes in Phonon Dispersion Measured by Electron Microscopy

Phonon dispersion is widely used to elucidate the vibrational properties of materials. As an emerging technique, momentum-resolved vibrational spectroscopy in scanning transmission electron microscopy (STEM) offers an unparalleled approach to explore q-dependent phonon behavior at local structures. In this study, we systematically investigate the phonon dispersion of monolayer graphene across several Brillouin zones (BZs) using momentum-resolved vibrational spectroscopy and find that the optical phonon signals vanish at the {\Gamma} points with indices (hk0) satisfying h+2k=3n (n denoted integers). Theoretical analysis reveals that the observed phenomena arise from the complete destructive interference of the scattered waves from different basis atoms. This observation, corroborated by the study of diamond, should be a general characteristic of materials composed of symmetrically equivalent pairs of the same elements. Moreover, our results emphasize the importance of multiple scattering in interpreting the vibrational signals in bulk materials. We demonstrate that the systematic absences and dynamic effects, which have not been much appreciated before, offer new insights into the experimental assessment of local vibrational properties of materials