Study of nonlinear phase error correction technique for synthetic aperture ladar

The success of target reconstruction in the SAR(Synthetic Aperture Radar) imaging system is greatly dependent on coherent detection. Incoherent detection appears as a multiplicative phase error to echoed signal, which consequently causes fatal degradations such as fading or dislocation of target image. In this paper, we propose a motion error correction scheme using an in-scene target to compensate for relative distance error between the radar and the target. We start by modeling from a wave equation for one point target, and then derive the complex motion error from extended overall echoed data. The proposed algorithm is also good for the correction of relatively large motion error because it can be applied repeatedly, and it is converged after each iteration. We use the spatial Doppler characteristics of the strong in-scene target to retrieve motion error, thus, we only used the partial spectral echo data corresponding to the strong in-scene target. By doing this, we can reduce computational loads and the number of iterations for the large motion error. We verify the performance of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.

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Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks

Sensors ◽

10.3390/s21062009 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2009

Author(s):

Fatemeh Najafi ◽

Masoud Kaveh ◽

Diego Martín ◽

Mohammad Reza Mosavi

Keyword(s):

Error Correction ◽

Random Access ◽

Error Correction Codes ◽

Access Memory ◽

Deep Convolutional Neural Networks ◽

Device Identification ◽

Physical Unclonable Functions ◽

Server Side ◽

Correction Technique ◽

Reduction Methods

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

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