Overview of Comnonent and Device Characterization

1996 ◽  
Vol 430 ◽  
Author(s):  
Doug Rytting
Keyword(s):  
Integrated Circuit ◽  
State Of The Art ◽  
Ease Of Use ◽  
Network Analyzer ◽  
Time To Market ◽  
Phase Measurements ◽  
New Methods ◽  
Rapid Changes ◽  
Improved Accuracy ◽  
Error Correction Procedures

AbstractThe electronics market is constantly pushing the state of the art in design to reduce cost, size, weight, and power consumption. New designs are emerging causing rapid changes in technology driving high design turnover. This drives the need for component and material measurements that will reduced design cycles and time to market. In the design and measurement of linear devices, error corrected S-parameters are traditionally measured with a network analyzer. the network analyzer combines magnitude with phase measurements for improved accuracy. Time domain techniques are used to get a better physical understanding of the device characteristics. Error correction procedures have been improved to provide high accuracy and ease of use. New methods to measure impedance results in high precision capacitor and inductor models necessary for both surface mount and integrated circuit applications. Note: In the following paper the relevant text follows each slide.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals Durable Continuous-Flow Mechanical Circulatory Support: State of the Art

Hearts ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 127-138
Author(s):  
Antonio Loforte ◽  
Luca Botta ◽  
Silvia Boschi ◽  
Gregorio Gliozzi ◽  
Giulio Giovanni Cavalli ◽  
...  
Keyword(s):  
Heart Transplantation ◽  
Continuous Flow ◽  
Mechanical Circulatory Support ◽  
State Of The Art ◽  
Ease Of Use ◽  
Circulatory Support ◽  
Pulse Modulation ◽  
Ventricular Assist ◽  
Old Patients ◽  
Number Of Patients

Implantable mechanical circulatory support (MCS) systems for ventricular assist device (VAD) therapy have emerged as an important strategy due to a shortage of donor organs for heart transplantation. A growing number of patients are receiving permanent assist devices, while fewer are undergoing heart transplantation (Htx). Continuous-flow (CF) pumps, as devices that can be permanently implanted, show promise for the treatment of both young and old patients with heart failure (HF). Further improvement of these devices will decrease adverse events, enable pulse modulation of continuous blood flow, and improve automatic remote monitoring. Ease of use for patients could also be improved. We herein report on the current state of the art regarding implantable CF pumps for use as MCS systems in the treatment of advanced refractory HF.

New Trends in Asymptotic Approaches: Summation and Interpolation Methods

2001 ◽  
Vol 54 (1) ◽  
pp. 69-92 ◽  
Author(s):  
Igor V. Andrianov ◽  
Jan Awrejcewicz
Keyword(s):  
Applied Sciences ◽  
Solid Mechanics ◽  
State Of The Art ◽  
Asymptotic Methods ◽  
Perturbation Series ◽  
Review Article ◽  
Point Of View ◽  
Advantages And Disadvantages ◽  
New Methods ◽  
Local Character

In this review article, we present in some detail new trends in application of asymptotic techniques to mechanical problems. First we consider the various methods which allows for the possibility of extending the perturbation series application space and hence omiting their local character. While applying the asymptotic methods very often the following situation appears: an existence of the asymptotics ε → 0 implies an existence of the asymptotics ε → ∞ (or, in a more general sense, ε → a and ε → b). Therefore, an idea of constructing a single solution valid for a whole interval of parameter ε changes is very attractive. In other words, we discuss a problem of asymptotically equivalent function constructions possessing for ε → a and ε → b a known asymptotic behavior. The defined problems are very important from the point of view of both theoretical and applied sciences. In this work, we review the state-of-the-art, by presenting the existing methods and by pointing out their advantages and disadvantages, as well as the fields of their applications. In addition, some new methods are also proposed. The methods are demonstrated on a wide variety of static and dynamic solid mechanics problems and some others involving fluid mechanics. This review article contains 340 references.

ERROR BOUNDED VARIABLE DISTANCE OFFSET OPERATOR FOR FREE FORM CURVES AND SURFACES

1991 ◽  
Vol 01 (01) ◽  
pp. 67-78 ◽  
Author(s):  
GERSHON ELBER ◽  
ELAINE COHEN
Keyword(s):  
Approximation Algorithms ◽  
Error Bound ◽  
Free Form ◽  
Global Error Bound ◽  
New Methods ◽  
Curves And Surfaces ◽  
Polygon Mesh ◽  
Finite Set ◽  
Variable Distance ◽  
Improved Accuracy

Most offset approximation algorithms for freeform curves and surfaces may be classified into two main groups. The first approximates the curve using simple primitives such as piecewise arcs and lines and then calculates the (exact) offset operator to this approximation. The second offsets the control polygon/mesh and then attempts to estimate the error of the approximated offset over a region. Most of the current offset algorithms estimate the error using a finite set of samples taken from the region and therefore can not guarantee the offset approximation is within a given tolerance over the whole curve or surface. This paper presents new methods to globally bound the error of the approximated offset of freeform curves and surfaces and then automatically derive new approximations with improved accuracy. These tools can also be used to develop a global error bound for a variable distance offset operation and to detect and trim out loops in the offset.

scholarly journals Data-Efficient Sensor Upgrade Path Using Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6523
Author(s):  
Pieter Van Van Molle ◽  
Cedric De De Boom ◽  
Tim Verbelen ◽  
Bert Vankeirsbilck ◽  
Jonas De De Vylder ◽  
...  
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Original Data ◽  
Radar Data ◽  
Teacher Supervision ◽  
Multispectral Images ◽  
Test Set ◽  
Time To Market ◽  
Speed Up ◽  
Knowledge Distillation

Deep neural networks have achieved state-of-the-art performance in image classification. Due to this success, deep learning is now also being applied to other data modalities such as multispectral images, lidar and radar data. However, successfully training a deep neural network requires a large reddataset. Therefore, transitioning to a new sensor modality (e.g., from regular camera images to multispectral camera images) might result in a drop in performance, due to the limited availability of data in the new modality. This might hinder the adoption rate and time to market for new sensor technologies. In this paper, we present an approach to leverage the knowledge of a teacher network, that was trained using the original data modality, to improve the performance of a student network on a new data modality: a technique known in literature as knowledge distillation. By applying knowledge distillation to the problem of sensor transition, we can greatly speed up this process. We validate this approach using a multimodal version of the MNIST dataset. Especially when little data is available in the new modality (i.e., 10 images), training with additional teacher supervision results in increased performance, with the student network scoring a test set accuracy of 0.77, compared to an accuracy of 0.37 for the baseline. We also explore two extensions to the default method of knowledge distillation, which we evaluate on a multimodal version of the CIFAR-10 dataset: an annealing scheme for the hyperparameter α and selective knowledge distillation. Of these two, the first yields the best results. Choosing the optimal annealing scheme results in an increase in test set accuracy of 6%. Finally, we apply our method to the real-world use case of skin lesion classification.

Application of a Bayesian Network to Integrated Circuit Tester Diagnosis

1994 ◽  
Author(s):  
Daniel Mittelstadt ◽  
Robert Paasch ◽  
Bruce D'Ambrosio
Keyword(s):  
Integrated Circuit ◽  
Procedural Knowledge ◽  
Knowledge Engineering ◽  
Probability Distributions ◽  
Belief Networks ◽  
Diagnostic Problem ◽  
Complete Model ◽  
New Methods ◽  
Ic Testing ◽  
Time Diagnosis

Abstract We describe research efforts to implement a Bayesian belief network based expert system to solve a real-world diagnostic problem — the diagnosis of integrated circuit (IC) testing machines. We describe the development of several models of the IC tester diagnostic problem in belief networks, the implementation of one of these models using Symbolic Probabilistic Inference (SPI), and discuss the difficulties and advantages encountered in the process. We observe that modelling with interdependencies in belief networks simplified the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modelling, contact resistance failures, because evaluation of the probability distributions could not be made fast enough to expand to a complete model with real-time diagnosis. Further research is recommended to create new methods, or refine existing methods, to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

scholarly journals Chip-to-Chip Authentication Method Based on SRAM PUF and Public Key Cryptography

2019 ◽  
Vol 3 (4) ◽  
pp. 382-396 ◽  
Author(s):  
Ioannis Karageorgos ◽  
Mehmet M. Isgenc ◽  
Samuel Pagliarini ◽  
Larry Pileggi
Keyword(s):  
Integrated Circuit ◽  
Hardware Implementation ◽  
State Of The Art ◽  
Public Key Cryptography ◽  
Cmos Technology ◽  
Performance Level ◽  
The Other ◽  
Public Key ◽  
Critical Systems ◽  
Software Vulnerabilities

AbstractIn today’s globalized integrated circuit (IC) ecosystem, untrusted foundries are often procured to build critical systems since they offer state-of-the-art silicon with the best performance available. On the other hand, ICs that originate from trusted fabrication cannot match the same performance level since trusted fabrication is often available on legacy nodes. Split-Chip is a dual-IC approach that leverages the performance of an untrusted IC and combines it with the guaranties of a trusted IC. In this paper, we provide a framework for chip-to-chip authentication that can further improve a Split-Chip system by protecting it from attacks that are unique to Split-Chip. A hardware implementation that utilizes an SRAM-based PUF as an identifier and public key cryptography for handshake is discussed. Circuit characteristics are provided, where the trusted IC is designed in a 28-nm CMOS technology and the untrusted IC is designed in an also commercial 16-nm CMOS technology. Most importantly, our solution does not require a processor for performing any of the handshake or cryptography tasks, thus being not susceptible to software vulnerabilities and exploits.

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