Filtering Network Traffic Based on Protocol Encapsulation Rules

Packet filtering is a technology at the foundation of many traffic analysis tasks. While languages and tools for packet filtering have been available for many years, none of them supports filters operating on the encapsulation relationships found in each packet. This represents a problem as the number of possible encapsulations used to transport traffic is steadily increasing and we cannot define exactly which packets have to be captured. This paper presents our early work on an algorithm that models protocol filtering patterns (including encapsulation constraints) as Finite State Automata and supports the composition of multiple expressions within the same filter. The resulting, optimized filter is then translated into executable code. The above filtering algorithms are available in the NetBee open source library, which provides some basic tools for handling network packets (e.g., a tcpdump-like program) and APIs to build more advanced tool

Printed Electrodermal Activity Sensor with Optimized Filter for Stress Detection

This paper presents a tiny, flexible, and low-cost all-analog approach for measuring electrodermal activity, based on the conductance of the skin. We propose a tiny, fully-printed system on flexible substrates, which guarantees flexibility and simplifies attachment to the body, and allows for detection of high stress values in form of a binary classification. A major contribution of this paper is the design of the printed hardware, including a novel way to optimize the hardware parameters, which is done via an evolutionary algorithm

Harmonic Resonance Analysis for Wind Integrated Power System and Optimized Filter Design

As the contribution of renewable energy sources is increasing year over year, the effect of harmonics on power system becomes important, and it requires special attention. In conventional power sources, the harmonics is not generated at the source side; only load side is contributing in the harmonics. But renewable energy sources, particularly wind and solar, are based on power electronic devices, so it generates harmonics. This harmonics may have an adverse effect on the system. Harmonic resonance is one of the phenomena, due to which the harmonics are amplified and give rise to several trivial issues. Various methods are used to control the harmonics in the system. Harmonic filter is one of the simple ways to absorb the harmonics generated at load and generation side. Various filter designs have been found in literature as well as in the field. The filters are classified according to their design, construction and operation method. There are two main categories, active filters and passive filters. The passive filters are widely used due to its simplicity and lesser cost. However, to achieve the better performance, it is also used with active filters, and this combination is known as hybrid filter. The response of filters is modified as per the system requirement using various techniques. In this work, the impedance characteristics of various filters are discussed and analysed. Also, how the control structure of power electronic devices affects or modifies the output impedance of converter is also discussed

Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm

On the spectral factor ambiguity of FIR energy compaction filter banks

This paper focuses on the design of signal-adapted finite-impulse response (FIR) paraunitary (PU) filter banks optimized for energy compaction (EC). The design of such filter banks has been shown in the literature to consist of the design of an optimal FIR compaction filter followed by an appropriate Karhunen-Loe/spl grave/ve transform (KLT). Despite this elegant construction, EC optimal filter banks have been shown to perform worse than common nonadapted filter banks for coding gain, contrary to intuition. Here, it is shown that this phenomenon is most likely due to the nonuniqueness of the compaction filter in terms of its spectral factors. This nonuniqueness results in a finite set of EC optimal filter banks. By choosing the spectral factor yielding the largest coding gain, it is shown that the resulting filter bank behaves more and more like the infinite-order principal components filter bank (PCFB) in terms of numerous objectives such as coding gain, multiresolution, noise reduction with zeroth-order Wiener filters in the subbands, and power minimization for discrete multitone (DMT)-type nonredundant transmultiplexers