This paper proposes an embedded implementation able to evaluate the pavement quality of road infrastructure by using a low-cost microcontroller board, an analog microphone placed inside the tyre cavity and a Convolutional Neural Network for real-time classification. To train the neural network, tracks audio were collected employing a vehicle moving at different cruise speeds (30, 40, 50 km/h) in the area of Pisa. The raw audio signals were split, labelled and converted into images by calculating the MFCC spectrogram. Finally, the author designed a tiny CNN with a size of 18KB able to classify five different classes: good quality road, bad quality road, pothole-bad road, silence and unknown. The CNN model achieved an accuracy equal to 93.8 % on the original model and about 90 % on the quantized model. The finale embedded system is equipped with BLE communication for the transmission of information to a smartphone equipped with GPS and obtain real-time maps of road quality.
Fingerprinting is one form of biometrics, a science that can be used for personal identification. It is one of the important techniques and security measures for human authentication across the globe due to its uniqueness and individualistic characteristics. Fingerprints are made up of an arrangement of ridges, called friction ridges. Each ridge consists of pores, that are attached to the glands under the skin. Several algorithms proposed different approaches to recreate fingerprint images. However, these works encountered problems with poor quality and presence of structured noise on these images. In this paper, we present a novel fingerprint system that provides more unique and robust algorithms which are capable to distinguish between individuals effectively. A sparse autoencoder (SAE) algorithm is used to reconstruct fingerprint images. It is an unsupervised deep learning model that replicates its input at the output. The architecture is designed and trained with datasets of fingerprint images that are pre-processed to be able to fit them in the model. Three datasets of fingerprint images have been utilized to validate the robustness of the model. This dataset has been split into 70% for training and 30% for testing the model. SAE is fine-tuned and optimized with L2 and sparsity regularization, thus it increased the efficiency of learning representation for the architecture. The sparse autoencoder is a suitable deep learning model to improve the recreation of fingerprint images significantly. The proposed approach showed promising results, and it can enhance the quality of reproduced fingerprint images with a clear ridge structure and eliminating various overlapping patterns.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.