Brazil

In this contribution a design of a full polarimetric multi-channel Ground Penetrating Radar (GPR) with optimized antenna switching is presented. Firstly, a single-polarized system was used to obtain multiple measurements of a static scene with different antenna orientation and providing a full polarimetric dataset. Laboratory experiments have been performed on metal test landmines and where promising results have been obtained. The single-polarized radar system was modified to a multichannel system with focus on decreasing the scanning time. The selected frequency range covers the band of 550 MHz to 2.7 GHz, which corresponds in soil to a range resolution of approximately 7 cm. There was no need to use a Power Amplifier (PA) at the transmitter stage, since a highly sensitive receiver is implemented. This greatly decrease the power consumption and makes the device suitable for battery powered platforms. Physical contact between the ground and antennas was avoided with using the air-launched measurement technique, where additionally the level of safety is higher.

IWSSIP 2020

Design of a Full Polarimetric GPR System for Landmine Detection

landmine detection

uwb gpr

radar design

full polarimetric gpr

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

The presentation focus signal processing techiniques appliend on the ATLAS experiment, present on the LHC machine. It compares two energy estimation methods in high pile-up scenario using LHC collision data.

Energy Reconstruction Performance in the ATLAS Tile Calorimeter Operating at High Event Rate Conditions Using LHC Collision Data

Blur and other undesirable visual artifacts are often produced from simple interpolation methods when applied to magnification from low-resolution into higher resolution images. This work evaluates the effect of existing sharpening filters when applied to images obtained from interpolation in order to determine whether image quality can be improved. Additionally, a novel sharpening filter is proposed which uses information from the interpolation process to prevent the occurrence of blur. The novel filter is shown to achieve better results when compared to other sharpening filters from the literature.

An Empirical Evaluation of Sharpening Filters Applied to Magnified Images

In the new context of smart grids (SGs), new types of electric disturbances may occur in the electrical power systems (EPSs) current and voltage waveforms. The detection and the study of such disturbances can be useful in the operation and control of the EPSs. In order to identify any waveform abnormality or variation, novelty detectors might be used. This work presents a novelty detection technique for power quality signals based on surrogates and distances calculated in the time-frequency domain. Surrogates are signals created with the same magnitude of the Fourier spectrum from the original signal, but with the phase spectrum generated from a random uniform distribution. Tests using several cases of the most common power quality disturbances using synthetic signals were conducted. A comparison with an energy-based novelty detector currently present in the literature is performed, showing better performance results in terms of the receiver operating characteristic (ROC) curve parameters. Also, a real-life waveform signal is used to show the presented technique in action. The surrogate technique presented in this work can be useful in the definition of the novelties ground-truth for real power signal waveforms.

Novelty Detection in Power Quality Signals with Surrogates: a Time-Frequency Technique

One of the goals of the music information retrieval (MIR) community is to research new methods and create new systems that can efficiently and effectively retrieve and recommend songs from large databases of music content. Despite the volume of research in the area, there is a lack of music databases to support these works, i.e. databases that comply with some quite desirable requirements for the development of researches, such as: a huge amount of music pieces, the audio signal availability and a great diversity of audio attributes. In order to contribute to the MIR community, we present Music4All, a new music database which contains metadata, tags, genre information, 30-seconds audio clips, lyrics, and so on. Additionally, we also exemplify some MIR tasks that may benefit from our database and compare it with other databases proposed in the literature. To request the Music4All database, send an e-mail to contact4music4all@gmail.com.

Music4All: A New Music Database and its Applications

In this paper, we propose a method for face recognition
by applying data augmentation and transfer learning in pretrained Convolutional Neural Networks (CNNs). Our main focus
is to analyze the power of data augmentation for face recognition
systems with CNN transfer learning. We extracted features from
the images and trained a KNN classifier using the VGG-Face
CNN.

Data Augmentation for Face Recognition with CNN Transfer Learning

Around the world, there are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving a total of 285 million visually impaired people. The use of information and communication technologies can help disabled people to achieve greater independence, quality of life and inclusion in social activities by increasing, maintaining or improving their functional capacities. In this context, this paper presents an automatic methodology for identifying banknotes that can be widely used by people with visual impairment. For this, we evaluated a set of four point of interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies, which can be used as a basis for the development of applications for the identification of banknotes. Experiments performed on US Dollar (USD), Euro (EUR) and Brazilian Real Banknotes (BRL) obtained rates of accuracy of 99.78%, 99.12%, and 96.92%, respectively.

Banknote Identification Methodology for Visually Impaired People

Glaucoma is identified as one of the main global
causes of visual impairment or blindness. There is no cure or possible corrections in case of visual impairment, so the early diagnosis is essential to delay or prevent its progression. However, the disease is asymptomatic in its early stages. The disease can be detected in routine eye exams like anterior segment optical coherence tomography, which is analyzed by a specialist, but the analysis of many patients demands much time. There are two main types of the disease, open angle and angle closure glaucoma. In this paper, we propose a method for automatic detection of angle closure glaucoma in anterior segment optical coherence tomography images, based on transfer learning and multilevel convolutional neural networks to extract visual features. In the proposed method, the multilevel architecture models achieve as the best result an AUC of 0.972."

Multilevel CNN for Angle Closure Glaucoma Detection using AS-OCT Images

One of the most used statistical approaches in the
literature for computing texture information is the Gray Level Cooccurrence Matrix (GLCM). The use of GLCM and textures descriptors calculated from there, allows, among other applications, the detection of the main direction of the elements of texture. This is possible essentially because the GLCM uses different angles to compute the relationship between the gray
tones in the image. An alternative to computing this relationship is the Sum of Difference Histograms (SDH). In contrast to GLCM, which is a bidimensional structure, SDHs are onedimensional. Once there is a relationship between the data stored in both structures, the behavior of the descriptors is similar, and the SDH have a lower computational cost being, for this, more efficient in a number of applications. In this sense, this work leads to a series of investigations regarding the correspondence between
these two approaches. Moreover, it investigates which texture descriptors, calculated from the SDH, can collaborate in the process of identifying the predominant direction of a texture.

Determining the Direction of Images Using Sum and Difference Histograms and Texture Descriptors

This work proposes the use of Genetic Algorithms (GA) to identify the area of the breast from the background in thermographic breast images. The proposed method uses color information, a fitness function based on cardioids, and GA. This is the first work in the literature to propose a Region of Interest (ROI) extraction based on GA and cariods. ROI extraction can improve the accuracy of cancer detection and assist with the standardization of acquisition protocols. The method is able to successfully separate the breast region in 52 out of 58 images, while being fully automatic, and not requiring manual selection of seed points.

ROI Extraction in Thermographic Breast Images Using Genetic Algorithms

Hybrid Capsule Network Architecture Estimation for Melanoma Detection. As uncommon as its incidence is, melanoma is considered to be one of the most aggressive and deadly skin cancers today. Risk factors for the development of the disease are related to exposure to ultraviolet rays and family history. Currently, the diagnosis is made through dermatoscopy, a noninvasive technique that aims to observe the structures of the skin. The diagnosis of melanoma is linked to the professional's experience in identifying the disease. With the advancement of computing, solutions based on deep learning were suggested to help diagnose the disease. This work has as main objective the adaptation of a capsule neural network for the task of detecting melanoma in medical images. The proposed architecture consists of the VGG16 neural network combined with a capsule neural network. The results obtained by the proposed model fed with the ISIC Archive database proved to be promising when compared to the literature, obtaining an accuracy of 92.6, sensitivity of 90.9 and specificity of 92.8.

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Design of a Full Polarimetric GPR System for Landmine Detection

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Energy Reconstruction Performance in the ATLAS Tile Calorimeter Operating at High Event Rate Conditions Using LHC Collision Data

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