The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Automatic recognition of human demographical attributes has implications in a variety of domains, such as surveillance systems, human computer interaction, marketing etc. In this paper, we present an automatic gender recognition method from facial images based on convolutional neural networks. In order to train the network, we merged together several face databases and also gathered and annotated...
The importance of face anti-spoofing algorithms in biometric authentication systems is becoming indispensable. Recently, the success of Convolution Neural Networks (CNN) in key application areas of computer vision has encouraged its use in face biometrics for face anti-spoofing and verification applications. However, small training data has restricted the use of deep CNN architectures for face anti-spoofing...
Support Vector Machine (SVM) is a linear binary classifier that requires a kernel function to handle non-linear problems. Most previous SVM implementations for embedded systems in literature were built targeting a certain application; where analyses were done through comparison with software implementations only. The impact of different application datasets towards SVM hardware performance were not...
An energy efficient machine learning requires an effective construction of neural network during training. This paper introduces a tensorized formulation of neural network during training such that weight matrix can be significantly compressed. The tensorized neural network can be further naturally mapped to a 3D CMOS-RRAM based accelerator with significant bandwidth boosting from vertical I/O connections...
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-reduced variants obtained by eliminating combinations of the input signal, bias,...
Automated medical assistance system is in high demand with the advances in research in the machine learning area. In many such applications, availability of labeled medical dataset is a primary challenge and dataset of dental diseases is not an exception. An attempt towards accurate classification of dental diseases is addressed in this paper. Labeled dataset consisting of 251 Radio Visiography (RVG)...
We present and compare two approaches to detect the presence of bird calls in audio recordings using convolutional neural networks on mel spectrograms. In a signal processing challenge using environmental recordings from three very different sources, only two of them available for supervised training, we obtained an Area Under Curve (AUC) measure of 89% on the hidden test set, higher than any other...
Provides an abstract for each of the tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real...
Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNNs) by retaining the structure and systematically reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense...
Monaural source separation is an important research area which can help to improve the performance of several real-world applications, such as speech recognition and assisted living systems. Huang et al. proposed deep recurrent neural networks (DRNNs) with discriminative criterion objective function to improve the performance of source separation. However, the penalty factor in the objective function...
This paper presents the design and implementation of an Internet of Thing (IoT)-based system for indoor localization using Bluetooth Low Energy (BLE) technology. Our solution consists of two main systems: an acquisition system and a central server, under the Client-Server paradigm and the IoT philosophy. We report the development of different modules: measurement (Bluetooth beacons), data aggregation...
Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem,...
We present ongoing work on a tool that consists of two parts: (i) A raw micro-level abstract world simulator with an interface to (ii) a 3D game engine, translator of raw abstract simulator data to photorealistic graphics. Part (i) implements a dedicated cellular automata (CA) on reconfigurable hardware (FPGA) and part (ii) interfaces with a deep learning framework for training neural networks. The...
The need for effective and reliable surveillance techniques is getting nowadays more and more of primary importance, especially in the actual scenario in which safety and security have become a priority. While classical techniques rely on video-based surveillance systems, such as Close-Circuit television, many studies show that also the audio signal can be effectively used for these purposes. There...
The increasing amount of data to be processed coming from multiple sources, as in the case of sensor networks, and the need to cope with constraints of security and privacy, make necessary the use of computationally efficient techniques on simple and cheap hardware architectures often distributed in pervasive scenarios. Random Vector Functional-Link is a neural network model usually adopted for processing...
Machine learning can be the key to saving the world from losing football field-sized forest areas each second. As deforestation in the Amazon basin causes devastating effects both on the ecosystem and the environment, there is urgent need to better understand and manage its changing landscape. A competition was recently conducted to develop algorithms to analyze satellite images of the Amazon. Successful...
In this paper, artificial neural networks are modeled to predict complete band-gaps of bi-dimensional photonic crystals. The available data-set has been generated by an integrated artificial immune network and MPB (MIT Photonic Bands) optimization procedure. Two case studies were carried out, considering square lattice photonic crystals composed of two and three silicon round rods embedded in air...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.