Analysis and application of digital spectral warping in analog and mixed-signal testing

Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation

On the use of fuzzy logic in dependable cloud management

The effective and efficient use of dependable cloud infrastructures requires the agreement between users and cloud providers on resources, services, operating conditions, and features as well as the mapping of users' requirements onto the cloud architecture. In this paper, we identify the different ways in which fuzzy logic can be profitably adopted in performing these tasks, providing flexibility in capturing users' needs and dealing with complex architectures and conflicting or hardly-satisfiable requirements. We specifically put forward the idea of using fuzzy logic at the user-side, to enable the specification of users' needs in crisp or fuzzy ways and their homogenous processing

Towards explainable face aging with Generative Adversarial Networks

Generative Adversarial Networks (GAN) are being increasingly used to perform face aging due to their capabilities of automatically generating highly-realistic synthetic images by using an adversarial model often based on Convolutional Neural Networks (CNN). However, GANs currently represent black box models since it is not known how the CNNs store and process the information learned from data. In this paper, we propose the \ufb01rst method that deals with explaining GANs, by introducing a novel qualitative and quantitative analysis of the inner structure of the model. Similarly to analyzing the common genes in two DNA sequences, we analyze the common \ufb01lters in two CNNs. We show that the GANs for face aging partially share their parameters with GANs trained for heterogeneous applications and that the aging transformation can be learned using general purpose image databases and a \ufb01ne-tuning step. Results on public databases con\ufb01rm the validity of our approach, also enabling future studies on similar models

A Web-Based Distributed Virtual Educational Laboratory

Evolution and cost of measurement equipment, continuous training, and distance learning make it difficult to provide a complete set of updated workbenches to every student. For a preliminary familiarization and experimentation with instrumentation and measurement procedures, the use of virtual equipment is often considered more than sufficient from the didactic point of view, while the hands-on approach with real instrumentation and measurement systems still remains necessary to complete and refine the student's practical expertise. Creation and distribution of workbenches in networked computer laboratories therefore becomes attractive and convenient. This paper describes specification and design of a geographically distributed system based on commercially standard components

Towards the prediction of renewable energy unbalance in smart grids

The production of renewable energy is increasing worldwide. To integrate renewable sources in electrical smart grids able to adapt to changes in power usage in heterogeneous local zones, it is necessary to accurately predict the power production that can be achieved from renewable energy sources. By using such predictions, it is possible to plan the power production from non-renewable energy plants to properly allocate the produced power and compensate possible unbalances. In particular, it is important to predict the unbalance between the power produced and the actual power intake at a local level (zones). In this paper, we propose a novel method for predicting the sign of the unbalance between the power produced by renewable sources and the power intake at the local level, considering zones composed of multiple power plants and with heterogeneous characteristics. The method uses a set of historical features and is based on Computational Intelligence techniques able to learn the relationship between historical data and the power unbalance in heterogeneous geographical regions. As a case study, we evaluated the proposed method using data collected by a player in the energy market over a period of seven months. In this preliminary study, we evaluated different configurations of the proposed method, achieving results considered as satisfactory by a player in the energy market

A Tensor-Based Forensics Framework for Virtualized Network Functions in the Internet of Things: Utilizing Tensor Algebra in Facilitating More Efficient Network Forensic Investigations

With the ever-increasing network traffic and Internet connectivity of smart devices, more attack events are being reported. As a result, network forensics remains a topic of ongoing research interest in the Internet of Things (IoT). In this article, we present a novel tensor-based forensics approach for virtualized network functions (VNFs). An event tensor model is proposed to formalize the network events, and then, it is used for effectively updating the core event tensor. We then introduce a similarity tensor model to integrate the core event tensors on the orchestration and management layer in the network function virtualization (NFV) framework. Finally, we present an evidence tensor model for network forensics, where we demonstrate how evidence tensors can be merged

A query unit for the IPSec databases

IPSec is a suite of protocols that adds security to communications at the IP level. Protocols within IPSec make extensive use of two databases, namely the Security Policy Database (SPD) and the Security Association Database (SAD). The ability to query the SPD quickly is fundamental as this operation needs to be done for each incoming or outgoing IP packet, even if no IPSec processing needs to be applied on it. This may easily result in millions of query per second in gigabit networks. Since the databases may be of several thousands of records on large secure gateways, a dedicated hardware solution is needed to support high throughput. In this paper we discuss an architecture for these query units, we propose different query methods for the two databases, and we compare them through simulation. Two different versions of the architecture are presented: the basic version is modified to support multithreading. As shown by the simulations, this technique is very effective in this case. The architecture that supports multithreading allows for 11 million queries per second in the best case

Finger vein verification algorithm based on fully convolutional neural network and conditional random field

Owing to the complexity of finger vein patterns in shape and spatial dependence, the existing methods suffer from an inability to obtain accurate and stable finger vein features. This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). Firstly, to reduce missing pixels during ROI extraction, the method of sliding window summation is employed to filter and adjusted with self-built tools. In addition, the traditional baselines are endowed with different weights to automatically assign labels. Secondly, the deformable convolution network, through replacing the plain counterparts in the standard U-Net mode, can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins' scales and shapes. Moreover, the above features can be further mined and accumulated by combining the recurrent neural network (RNN) and the residual network (ResNet). With the steps mentioned above, the fully convolutional neural network is constructed. Finally, the CRF with Gaussian pairwise potential conducts mean-field approximate inference as the RNN, and then is embedded as a part of the FCN, so that the model can fully integrate CRF with FCNs, which provides the possibility to involve the usual back-propagation algorithm in training the whole deep network end-to-end. The proposed models in this paper were tested on three public finger vein datasets SDUMLA, MMCBNU and HKPU with experimental results to certify their superior performance on finger-vein verification tasks compared with other equivalent models including U-Net

A neural-based minutiae pair identification method for touch-less fingerprint images

Contact-based sensors are the traditional devices used to capture fingerprint images in commercial and homeland security applications. Contact-less systems achieve the fingerprint capture by vision systems avoiding that users touch any parts of the biometric device. Typically, the finger is placed in the working area of an optics system coupled with a CCD module. The captured light pattern on the finger is related to the real ridges and valleys of the user fingertip, but the obtained images present important differences from the traditional fingerprint images. These differences are related to multiple factors such as light, focus, blur, and the color of the skin. Unfortunately, the identity comparison methods designed for fingerprint images captured with touch-based sensors do not obtain sufficient accuracy when are directly applied to touch-less images. Recent works show that multiple views analysis and 3D reconstruction can enhance the final biometric accuracy of such systems. In this paper we propose a new method for the identification of the minutiae pairs between two views of the same finger, an important step in the 3D reconstruction of the fingerprint template. The method is divisible in the sequent tasks: first, an image preprocessing step is performed; second, a set of candidate minutiae pairs is selected in the two images, then a list of candidate pairs is created; last, a set of local features centered around the two minutiae is produced and processed by a classifier based on a trained neural network. The output of the system is the list of the minutiae pairs present in the input images. Experiments show that the method is feasible and accurate in different light conditions and setup configurations

ALL-IDB : the acute lymphoblastic leukemia image database for image processing

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research