There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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Table Of Integrals Series And Products

1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra. 4. Parametric Methods for Line Spectra. 5. Filter Bank Methods. 6. Spatial Methods. Appendix A: Linear Algebra and Matrix Analysis Tools. Appendix B: Cramer-Rao Bound Tools. Appendix C: Model Order Selection Tools. Appendix D: Answers to Selected Exercises. Bibliography. References Grouped by Subject. Subject Index.

/pdf/spectral-analysis-of-signals-4dbupf9m4n.pdf

Spectral analysis of signals

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

/pdf/deep-learning-approach-for-network-intrusion-detection-in-3w6qu79h2o.pdf

Deep learning approach for Network Intrusion Detection in Software Defined Networking

Software Defined Networking (SDN) has emerged as a key enabler for future agile Internet architecture. Nevertheless, the flexibility provided by SDN architecture manifests several new design issues in terms of network security. These issues must be addressed in a unified way to strengthen overall network security for future SDN deployments. Consequently, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection systems for SDNs. The proposed approach is tested using the NSL-KDD dataset, and we achieve an accuracy of 89% with only six raw features. Our experiment results also show that the proposed GRU-RNN does not deteriorate the network performance. Through extensive experiments, we conclude that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments.

/pdf/deep-recurrent-neural-network-for-intrusion-detection-in-sdn-336ybdgfbh.pdf

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

/pdf/channel-estimation-and-symbol-detection-for-block-38shjtgah4.pdf

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

A novel approach for ensuring confidential wireless communication is proposed and analyzed from an information-theoretic standpoint. In this approach, the legitimate receiver generates artificial noise (AN) to impair the intruder's channel. This method is robust because it does not need the feedback of channel state information (CSI) to the transmitter and does not assume that the number of Eve's antennas should be smaller than that of Bob. Furthermore, we propose a new concept of outage secrecy region to evaluate the secrecy performance from a geometrical perspective. This should be useful if we need to know what zone should be protected (or militarized). Analysis and simulation results in practical environments show that the proposed method has a good performance.

Secure Communication via Sending Artificial Noise by the Receiver: Outage Secrecy Capacity/Region Analysis

In this letter, we address physical layer security in MISO communications in the presence of passive eavesdroppers, i.e., the eavesdroppers' channels are unknown to the transmitter. Spatial beamforming and artificial noise broadcasting are chosen as the strategy for secure transmission. With the aim of guaranteeing a given probability of secrecy, defined by quality of service constraints at the intended receiver and at the eavesdroppers, an optimum power allocation strategy between transmitted information and artificial noise is proposed. Both power constrained and power unconstrained systems are considered. Our proposed outage probability-based approach compares favourably to an existing second-order-statistic-based approach.

Mounir Ghogho

Papers

Deep learning approach for Network Intrusion Detection in Software Defined Networking

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

Secure Communication via Sending Artificial Noise by the Receiver: Outage Secrecy Capacity/Region Analysis

Outage Probability Based Power Distribution Between Data and Artificial Noise for Physical Layer Security