2019/3
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Recent Submissions
- ItemA Modified Compressed Sensing-Based Recovery Algorithm for Wireless Sensor Networks(Společnost pro radioelektronické inženýrství, 2019-09) Jahanshahi, Javad Afshar; Danyali, Habibollah; Helfroush, Mohammad SadeghIn this paper, a novel compressed sensing (CS) acquisition and joint recovery of spatiotemporal correlated signals algorithm is presented for effective data collection and precise sensors data streams reconstruction in wireless sensor networks. The CS-based proposed method utilizes~an iterative re-weighted l1-minimization and a l2 regularization to increase the reconstruction accuracy with a small number of required data transmission. Moreover, we develop~an alternating direction method of multipliers based algorithm to efficiently solve the resulting optimization problem. Numerical experiments are conducted on several test signals with~a variety of sampling ratios. The experimental results verify the effectiveness of the proposed scheme in terms of reconstruction accuracy and consumption time compared with the state of the art algorithms.
- ItemDim Target Detection in Infrared Images Using Saliency Algorithms(Společnost pro radioelektronické inženýrství, 2019-09) Tunc, Seyit; Ilgin, Hakki AlparslanInfrared (IR) target detection and tracking are commonly used in modern defense systems. Target detection is the first and very important step for several surveillance applications. Long distance between imager and targets or bad weather conditions mostly cause dim target appearance with low signal-to-noise ratio (SNR) in IR images. In this study, dim targets in IR images are enhanced and detected using saliency detection algorithms, which have not been used in IR wavelength before. Performances of the algorithms are evaluated on common IR datasets. Algorithms are compared in terms of SNR, receiver operating characteristic (ROC) and area under curve (AUC) score. Effects of parameter selection are also considered for automatic target detection. Furthermore, feasibility of the methods for real-time applications are discussed.
- ItemConvolutional Neural Networks for Profiled Side-channel Analysis(Společnost pro radioelektronické inženýrství, 2019-09) Hou, Shourong; Zhou, Yujie; Liu, HongmingRecent studies have shown that deep learning algorithms are very effective for evaluating the security of embedded systems. The deep learning technique represented by Convolutional Neural Networks (CNNs) has proven to be a promising paradigm in the profiled side-channel analysis attacks. In this paper, we first proposed a novel CNNs architecture called DeepSCA. Considering that this work may be reproduced by other researchers, we conduct all experiments on the public ASCAD dataset, which provides electromagnetic traces of a masked 128-bit AES implementation. Our work confirms that DeepSCA significantly reduces the number of side-channel traces required to perform successful attacks on highly desynchronized datasets, which even outperforms the published optimized CNNs model. Additionally, we find that DeepSCA pre-trained from the synchronous traces works well in presence of desynchronization or jittering after a slight fine-tuning.
- ItemDesign and Implementation of Fractional-Order Microwave Integrator(Společnost pro radioelektronické inženýrství, 2019-09) Gupta, Mridul; Upadhyay, Dharmendra KumarA novel design of fractional-order microwave integrator using shunt connected open-stubs with transmission line sections in cascade is proposed. Design is obtained by optimizing the L1-norm based error function in Z-domain having not more than absolute magnitude error value of 0.01. Optimization is done using nature inspired cuckoo search algorithm. Superiority of the design in terms of magnitude error performance is identified by comparing it with the results obtained from some widely used benchmark optimization algorithms. The obtained design is implemented on a RT/Duroid 5880 substrate having 20 mil thickness, and results for the measured magnitude response are found to be in good agreement with ideal one over the frequency range of 1.0 GHz to 6.8 GHz.
- ItemThe Detection Performance of the Dual-Sequence-Frequency-Hopping Signal via Stochastic Resonance Processing under Color Noise(Společnost pro radioelektronické inženýrství, 2019-09) Liu, Guangkai; Kang, Yanmei; Quan, Houde; Sun, Huixian; Cui, Peizhang; Guo, ChaoCan the Dual-Sequence-Frequency-Hopping (DSFH) as a military emergency communication mode work under strong color noise? And is there any detection improvement of the DSFH signal via stochastic resonance (SR) processing under color noise? To deal with this problem, we analyze the physical feature of the DSFH signal. Firstly, the signal models of transmission, reception and the intermediate frequency (IF) are constructed. And the scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-markovian Langevin Equation (LE) is transformed into a markovian one by expand the 1-D LE to~a 2-D one. Thirdly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous one by assuming that the SR transition of magnetic particles is instantaneous and introducing the decision time. Therefore, the analytical periodic steady solution of the probability density function (PDF) with the parameter of the correlation time of the color noise is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, under the criterion of the maximum~a posterior probability (MAP). Theoretical and simulation results show as below: 1) whether the DSFH can work under strong color noise is decided by the correlation time of the color noise; 2) when the power intensity of the color noise is constant, the smaller the correlation time with the bigger local SNR, the greater PDF difference of the SR output under two hypothesis, leading to better detection performance.