Performance Comparison of State-of-the-Art 300A/1700V Si IGBT and SiC MOSFET Power Modules

2020 ◽  
Vol 7 (3) ◽  
pp. 44-51
Author(s):  
Michael J. Rogers ◽  
Eric R. Motto ◽  
Mark Steiner
Keyword(s):  
State Of The Art ◽  
Performance Comparison ◽  
Power Modules

Performance comparison of a state-of-the-art neuro-SPET scanner and a dedicated neuro-PET scanner

1994 ◽  
Vol 21 (5) ◽  
Author(s):  
DaleL. Bailey ◽  
Felicia Zito ◽  
Maria-Carla Gilardi ◽  
AnnaRita Savi ◽  
Ferruccio Fazio ◽  
...  
Keyword(s):  
State Of The Art ◽  
Performance Comparison

scholarly journals A Novel Fuzzy Encryption Technique Based on Multiple Right Translated AES Gray S-Boxes and Phase Embedding

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Naveed Ahmed Azam
Keyword(s):  
Image Encryption ◽  
State Of The Art ◽  
Performance Comparison ◽  
Security System ◽  
Security Systems ◽  
Secret Image ◽  
Host Image ◽  
Embedding Technique ◽  
Frequency Domains ◽  
Selection Of

This paper presents a novel image encryption technique based on multiple right translated AES Gray S-boxes (RTSs) and phase embedding technique. First of all, a secret image is diffused with a fuzzily selected RTS. The fuzzy selection of RTS is variable and depends upon pixels of the secret image. Then two random masks are used to enhance confusion in the spatial and frequency domains of the diffused secret image. These random masks are generated by applying two different RTSs on a host image. The decryption process of the proposed cryptosystem needs the host image for generation of masks. It is therefore, necessary, to secure the host image from unauthorized users. This task is achieved by diffusing the host image with another RTS and embedding the diffused secret image into the phase terms of the diffused host image. The cryptographic strength of the proposed security system is measured by implementing it on several images and applying rigorous analyses. Performance comparison of the proposed security technique with some of the state-of-the-art security systems, including S-box cryptosystem and steganocryptosystems, is also performed. Results and comparison show that the newly developed cryptosystem is more secure.

Performance and trends in recent opinion retrieval techniques

2013 ◽  
Vol 30 (1) ◽  
pp. 76-105 ◽  
Author(s):  
Sylvester O. Orimaye ◽  
Saadat M. Alhashmi ◽  
Eu-Gene Siew
Keyword(s):  
State Of The Art ◽  
Performance Comparison ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Opinion Retrieval ◽  
Future Research Directions ◽  
And Performance ◽  
Comparison Table ◽  
Art Techniques

AbstractThis paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

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.

Post Extension Ion Implant Photo Resist Strip for 32 nm Technology and beyond

2009 ◽  
Vol 145-146 ◽  
pp. 253-256 ◽  
Author(s):  
G. Mannaert ◽  
L. Witters ◽  
Denis Shamiryan ◽  
Werner Boullart ◽  
K. Han ◽  
...  
Keyword(s):  
Sheet Resistance ◽  
State Of The Art ◽  
Substrate Oxidation ◽  
Performance Comparison ◽  
Low Energy ◽  
Advanced Technology ◽  
Process Conditions ◽  
Threshold Voltages ◽  
Dopant Loss ◽  
Technology Nodes

The most advanced technology nodes require ultra shallow extension implants (low energy) which are very vulnerable to ash related substrate oxidation, silicon and dopant loss, which can result in a dramatic increase of the source/drain resistance and shifted transistor threshold voltages. A robust post extension ion implant ash process is required in order to meet cleanliness, near zero Si loss and dopant loss specifications. This paper discusses a performance comparison between fluorine-free, reducing and oxidizing, ash chemistries and “as implanted – no strip” process conditions, for both state-of-the-art nMOS and pMOS implanted fin resistors. Fluorine-free processes were chosen since earlier experiments with fluorine containing plasma strips exhibited almost a 10x increase in sheet resistance in the worse case.

scholarly journals Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2202 ◽  
Author(s):  
MinJi Park ◽  
Byoung Chul Ko
Keyword(s):  
Urban Areas ◽  
State Of The Art ◽  
Fire Detection ◽  
Classification Performance ◽  
Performance Comparison ◽  
Detection Methods ◽  
Night Time ◽  
Detection Systems ◽  
Ambient Lighting ◽  
Fast Classification

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

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