Brazil

Breast cancer is one of the most severe types ofcancer and responsible for about 15% of all women death related to cancer worldwide. Mammography is the most reliable and recommended approach to help early detection of breast cancer, since this technique can detect the disease in its asymptomatic phase. Different Computer-Aided Diagnosis (CAD) systems have been used to aid in the diagnosis serving as second opinion to the physicians. These systems extract features from masses found in mammograms, which compose inputs to the classifiers used in CAD systems. The main contribution of this study is the evaluation of different polygonal representations on the phase of feature extraction. In this context, two different polygonal models were tested with different parameters to represent boundaries of masses. Artificial neural networks, support vector machines and k-nearest neighbors were used to discriminate masses as benign and malignant achieving accuracy of 85%, 84% and 85%, respectively.

IWSSIP 2020

Evaluating the impact of polygonal representations on mass classification

**About**
 The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

 PETRA (which means “stone” in Greek) addresses important human living needs. From a technological perspective, PETRA motivates and inspires new scientific thrusts, innovations and discoveries to improve living environments, a particularly important issue, in an increasingly crowded and complex world where the need for inclusiveness and connectivity with the rest of the world is key. For the elderly in particular, PETRA addresses the fact that, as people grow older; they will increasingly rely on technology to be able to prolong staying in their homes and enjoy an independent life.

 The PETRA 2021 proceedings will be published in the ACM Digital Library as part of the ACM ICPS program. The ISBN number assigned to PETRA '21 is 978-1-4503-8792-7

PETRA 2021

The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

technical paper

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

Brazilian Electricity Regulatory Agency (ANEEL) defines as non-technical losses the problems that happen during the energy consumption measurement process. A feasible alternative to reduce those losses is the energy consumption reading process performed by customers, called self-reading. This process might be executed across digital platforms such as mobile applications and the customers would register and send consumption information. In this context, the consumption reading using voice is a simple way for the public that has lower affinity for technology to perform self-reading. Therefore, this work proposes an end-to-end speech recognition method applied to the energy consumption measurement that uses a neural network architecture based on Recurrent Neural Network Transducer (RNN-T). The proposed method uses a modified architecture called Residual Recurrent Neural Network Transducer (RRNN-T) which contains a Residual Skip that make its behavior similar to a network shallower than RNN-T by skipping some layers in the training process. That strategy saves computational cost of the inference process and disk space. A spelled sequence of digits is the input of the proposed method that recognizes those digits and outputs the electrical energy consumption reading measured in kilowatts per hour. The dataset used in this work contains 111,224 samples of spelled sequences divided into 70\% for training and 30\% for test. The proposed method obtains 0.05 word error rate (WER) and inference time less than 3 seconds on mobile devices.

Self-Reading by Speech: An End-to-end Speech Recognition Method Applied to the Energy Consumption Measurement on Mobile Devices

A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its integrity. Typically, the same watermark is added consistently to mark movie collections. Consequently, media with a visible watermark will be eventually susceptible to the necessity of watermark remission, operation that could be an issue. This paper presents two new and very
fast algorithms for watermark detection and removal. The here considered watermarks could be blended, overlaid or added in a continuous form to the digital video on remission treatment. The first of such algorithms use a template matching approach to pre-process the blended watermarks frame-to-frame in the Hue-Saturation-Value (HSV) color space, and then improve the detection using a post-processing step. The second algorithm presents two steps as well. Initially, it estimates the structure of a translucent watermark observing the constancy of its gradient magnitude in the area corresponding to the tenth part of each video. Secondly, it uses a sliding window to detect the watermarked in the frames. The results show that watermark detection by both algorithms has high sensitivity. Moreover, they are very fast when compared with other methods especially those based on deep learning

Watermark Detection and Clearance in Video Using Simple Signal and Image Processing Techniques

In this presentation we evaluate an approach of transfer learning to automatic semantic segmentation of lungs in a pediatric chest x-ray image based on
knowledge acquired in other datasets. The results are promising and
reach 0.917 of Dice using U-Net in a chest x-ray dataset without lung annotation

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Self-Reading by Speech: An End-to-end Speech Recognition Method Applied to the Energy Consumption Measurement on Mobile Devices

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Next from IWSSIP 2020

Self-Reading by Speech: An End-to-end Speech Recognition Method Applied to the Energy Consumption Measurement on Mobile Devices

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Automated ROP Diagnostic System based on Comparisons and U-Net Segmentation

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