scholarly journals A review of state-of-the-art and proposal for high frequency inductive step-down DC–DC converter in advanced CMOS

2016 ◽  
Vol 87 (2) ◽  
pp. 201-211 ◽  
Author(s):  
Florian Neveu ◽  
Bruno Allard ◽  
Christian Martin
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Inductive Step ◽  
Step Down

scholarly journals Sub-THz and THz SiGe HBT Electrical Compact Modeling

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1397
Author(s):  
Bishwadeep Saha ◽  
Sebastien Fregonese ◽  
Anjan Chakravorty ◽  
Soumya Ranjan Panda ◽  
Thomas Zimmer
Keyword(s):  
Heterojunction Bipolar Transistors ◽  
Silicon Germanium ◽  
High Frequency ◽  
State Of The Art ◽  
Sensitivity Study ◽  
Compact Modeling ◽  
Process Technology ◽  
Very High Frequency ◽  
Sige Hbt ◽  
Frequency Behavior

From the perspectives of characterized data, calibrated TCAD simulations and compact modeling, we present a deeper investigation of the very high frequency behavior of state-of-the-art sub-THz silicon germanium heterojunction bipolar transistors (SiGe HBTs) fabricated with 55-nm BiCMOS process technology from STMicroelectronics. The TCAD simulation platform is appropriately calibrated with the measurements in order to aid the extraction of a few selected high-frequency (HF) parameters of the state-of-the-art compact model HICUM, which are otherwise difficult to extract from traditionally prepared test-structures. Physics-based strategies of extracting the HF parameters are elaborately presented followed by a sensitivity study to see the effects of the variations of HF parameters on certain frequency-dependent characteristics until 500 GHz. Finally, the deployed HICUM model is evaluated against the measured s-parameters of the investigated SiGe HBT until 500 GHz.

scholarly journals Treatment of infant leukemias: challenge and promise

Hematology ◽  
2013 ◽  
Vol 2013 (1) ◽  
pp. 596-600 ◽  
Author(s):  
Patrick Brown
Keyword(s):  
High Frequency ◽  
Clinical Presentation ◽  
State Of The Art ◽  
Poor Response ◽  
Cooperative Groups ◽  
Therapeutic Approaches ◽  
Unique Biology ◽  
Current Therapies ◽  
Epidemiologic Features ◽  
Infant Leukemia

Abstract Leukemia in infants is rare but generates tremendous interest due to its aggressive clinical presentation in a uniquely vulnerable host, its poor response to current therapies, and its unique biology that is increasingly pointing the way toward novel therapeutic approaches. This review highlights the key clinical, pathologic, and epidemiologic features of infant leukemia, including the high frequency of mixed lineage leukemia (MLL) gene rearrangements. The state of the art with regard to current approaches to risk stratified treatment of infant leukemia in the major international cooperative groups is discussed. Finally, exciting recent discoveries elucidating the molecular biology of infant leukemia are reviewed and novel targeted therapeutic strategies, including FLT3 inhibition and modulation of aberrant epigenetic programs, are suggested.

ACOUSTIC DEWATERING AND DRYING (LOW AND HIGH FREQUENCY): STATE OF THE ART REVIEW

1985 ◽  
Vol 3 (4) ◽  
pp. 529-566 ◽  
Author(s):  
H. S. Muralidhara, ◽  
D. Ensminger ◽  
A. Putnam
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Frequency State

scholarly journals Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs

2019 ◽  
Author(s):  
Madhu Vankadari ◽  
Swagat Kumar ◽  
Anima Majumder ◽  
Kaushik Das
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Image Sequence ◽  
Input Image ◽  
Performance Comparison ◽  
Structural Defects ◽  
Learning Framework ◽  
Current State ◽  
Input And Output ◽  
Monocular Depth

This paper presents a new GAN-based deep learning framework for estimating absolute scale awaredepth and ego motion from monocular images using a completely unsupervised mode of learning.The proposed architecture uses two separate generators to learn the distribution of depth and posedata for a given input image sequence. The depth and pose data, thus generated, are then evaluated bya patch-based discriminator using the reconstructed image and its corresponding actual image. Thepatch-based GAN (or PatchGAN) is shown to detect high frequency local structural defects in thereconstructed image, thereby improving the accuracy of overall depth and pose estimation. Unlikeconventional GANs, the proposed architecture uses a conditioned version of input and output of thegenerator for training the whole network. The resulting framework is shown to outperform all existing deep networks in this field and beating the current state-of-the-art method by 8.7% in absoluteerror and 5.2% in RMSE metric. To the best of our knowledge, this is first deep network based modelto estimate both depth and pose simultaneously using a conditional patch-based GAN paradigm.The efficacy of the proposed approach is demonstrated through rigorous ablation studies and exhaustive performance comparison on the popular KITTI outdoor driving dataset.

scholarly journals Liquid-Based Reconfigurable Antenna Technology: Recent Developments, Challenges and Future

2020 ◽  
Author(s):  
Habshah Abu Bakar ◽  
Rosemizi Abd Rahim ◽  
Ping Jack Soh ◽  
Prayoot Akkaraekthalin
Keyword(s):  
High Frequency ◽  
Communication Systems ◽  
Room Temperature ◽  
State Of The Art ◽  
Reconfigurable Antennas ◽  
Wireless Communication Systems ◽  
Dielectric Resonators ◽  
Different Types ◽  
Recent Developments ◽  
Basic Approaches

Advances in reconfigurable liquid-based reconfigurable antennas are enabling new possibilities to fulfil the requirements of more advanced wireless communication systems. In this review, a comparative analysis of various state-of-the-art concepts and techniques for designing reconfigurable antennas using liquid is presented. First, the electrical properties of different liquids at room temperature commonly used in reconfigurable antennas are identified. This is followed by a discussion of various liquid actuation techniques in enabling high frequency reconfigurability. Next, liquid-based reconfigurable antennas in literature used to achieve the different types of reconfiguration will be critically reviewed. These include frequency-, polarization-, radiation pattern- and compound reconfigurability. The current concepts of liquid-based reconfigurable antennas can be classified broadly into three basic approaches: altering the physical (and electrical) dimensions of antennas using liquid, applying liquid-based sections as reactive loads; and implementation of liquids as dielectric resonators. Each concept and their design approaches will be examined, outlining their benefits, limitations, and possible future improvements.

scholarly journals On the Effectiveness of Low Frequency Perturbations

2019 ◽  
Author(s):  
Yash Sharma ◽  
Gavin Weiguang Ding ◽  
Marcus A. Brubaker
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Low Frequency ◽  
Human Perception ◽  
Search Space ◽  
Frequency Space ◽  
Performance Improvements ◽  
Frequency Regime ◽  
Frequency Components ◽  
Norm Constraint

Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains. In addition, recent work has shown that constraining the attack space to a low frequency regime is particularly effective. Yet, it remains unclear whether this is due to generally constraining the attack search space or specifically removing high frequency components from consideration. By systematically controlling the frequency components of the perturbation, evaluating against the top-placing defense submissions in the NeurIPS 2017 competition, we empirically show that performance improvements in both the white-box and black-box transfer settings are yielded only when low frequency components are preserved. In fact, the defended models based on adversarial training are roughly as vulnerable to low frequency perturbations as undefended models, suggesting that the purported robustness of state-of-the-art ImageNet defenses is reliant upon adversarial perturbations being high frequency in nature. We do find that under L-inf-norm constraint 16/255, the competition distortion bound, low frequency perturbations are indeed perceptible. This questions the use of the L-inf-norm, in particular, as a distortion metric, and, in turn, suggests that explicitly considering the frequency space is promising for learning robust models which better align with human perception.

scholarly journals Direction-aware Feature-level Frequency Decomposition for Single Image Deraining

2021 ◽  
Author(s):  
Sen Deng ◽  
Yidan Feng ◽  
Mingqiang Wei ◽  
Haoran Xie ◽  
Yiping Chen ◽  
...  
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Low Frequency ◽  
Communication Channels ◽  
Input Image ◽  
Experimental Results ◽  
Training Procedure ◽  
Single Image ◽  
Frequency Decomposition

We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compelling characteristics. First, unlike previous algorithms, we propose to perform frequency decomposition at feature-level instead of image-level, allowing both low-frequency maps containing structures and high-frequency maps containing details to be continuously refined during the training procedure. Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image. Third, different from existing algorithms using convolutional filters consistent in all directions, we propose a direction-aware filter to capture the direction of rain streaks in order to more effectively and thoroughly purge the input images of rain streaks. We extensively evaluate the proposed approach in three representative datasets and experimental results corroborate our approach consistently outperforms state-of-the-art deraining algorithms.

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