| 1. |
- Betta, Giovanni, et al.
(författare)
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Metrological characterization of 3D biometric face recognition systems in actual operating conditions
- 2017
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Ingår i: Acta IMEKO. - : IMEKO International Measurement Confederation. - 2221-870X .- 2221-870X. ; 6:1, s. 33-42
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.
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| 2. |
- Carratù, Marco, et al.
(författare)
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Wireless Sensor Network Calibration for PM10 Measurement
- 2020
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Ingår i: 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). - : IEEE. - 9781728144337
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Konferensbidrag (refereegranskat)abstract
- The proposal of an Advanced Metering Infrastructure based on short-range communication is suggested for the continuous monitoring of Particulate Matter. A prototype of Automatic Measurement System (AMS), including a low-cost off-the-shelf PM sensor, has been developed as a remote node to be adopted in the radio Local Area Network. The results of the system calibration and comparison with the data quality requirements of the PM measurement according to European regulations, as well as the simulation of a typical Smart City scenario in terms of communication performance, confirm the feasibility of the proposed distributed AMS for an effective adoption within an urban area.
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| 3. |
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| 4. |
- Di Leo, Giuseppe, et al.
(författare)
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A vision system for the online quality monitoring of industrial manufacturing
- 2017
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Ingår i: Optics and lasers in engineering. - : Elsevier BV. - 0143-8166 .- 1873-0302. ; 89, s. 162-168
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Tidskriftsartikel (refereegranskat)abstract
- The design of an image based measurement system for the online inspection of electromechanical parts is described. A two-camera architecture is introduced in order to highlight all the required details involved in the measurements. The design takes into account both the interfacing and the real-time issues that assure an effective online operation. The description of the measurement system and the corresponding installation on the production line points out a methodological approach to the design of these kinds of measurement systems. The paper provides details about the algorithms for the localization and the measurement of the required quantities, as well as the calibration procedure and the error correction. Experimental tests for the performance evaluation are presented and discussed in terms of timing and accuracy.
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| 5. |
- Di Leo, Giuseppe, et al.
(författare)
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I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies
- 2017
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Ingår i: Proceedings of the 50th Hawaii International Conference on System Sciences 2017. - 9780998133102 ; , s. 3587-3596
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Konferensbidrag (refereegranskat)abstract
- The paper introduces I3DermoscopyApp, a new declination of the Internet of Things (IoT) paradigm, designed to allow the early detection of melanoma. Even though artificial intelligence programs cannot outperform the diagnostic accuracy of expert dermatologists yet, they reveal to be very useful in providing second opinions to physicians with short clinical experience, thus improving significantly their diagnostic performance. Following this trend, an original integration of mobile app technology and well-known image processing algorithms allows the automatic analysis of pigmented skin lesions to help physicians apply a diagnostic method (Seven Point Check List) based on dermoscopy. The web-based platform makes the physician able to: i) store digital images captured by smartphones featured with a dermatoscope; ii) measure morphological and chromatic parameters of the skin lesion; iii) make a diagnostic decision according to the Seven Point Checklist method. A detailed description of the adopted techniques, together with the first validation results are reported.
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| 6. |
- Di Leo, Giuseppe, et al.
(författare)
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Smart Meters in Smart Cities : An Application of DLMS-COSEM on 169 MHz WM-Bus
- 2016
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Ingår i: Advanced Computer and Communication Engineering Technology. - Cham : Springer. - 9783319245829 ; , s. 735-746
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Konferensbidrag (refereegranskat)abstract
- Advanced Metering Infrastructures (AMI) are going to represent the backbone of all the Smart City projects where metering and other public services are supposed to be smart. The scenario proposed by OPEN METER project is taking shape in some urban areas where smart meters, concentrators and central access systems have been experiencing. In this paper some topics concerning the use of DLMS-COSEM (Device Language Message specification-Companion Specification for Energy Metering) protocol on a 169 MHz gateway network based on WM-bus for collecting and manage data coming from smart meters and other electronic devices are discussed by the authors. In particular, an innovative solution for water metering and the architecture of a data Central Access System, are described in detail.
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| 7. |
- Liguori, Consolatina, et al.
(författare)
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Estimation of the minimum measurement time interval in acoustic noise
- 2017
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Ingår i: Applied Acoustics. - : Elsevier BV. - 0003-682X .- 1872-910X. ; 127, s. 126-132
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Tidskriftsartikel (refereegranskat)abstract
- The appropriate choice of the minimum measurement time interval is introduced for an accurate estimation of environmental noise indicators. The proposal is based on a bootstrap approach for the continuous estimation of measurement uncertainty in order to determine the statistical variability of the acquired sound pressure levels. Experimental results concerning the adoption of the proposed method regarding environmental noise from three different sources (road traffic, outdoor air conditioner fan motor and construction site) confirm the reliability of the proposal and its feasibility in evaluating the equivalent sound pressure level of an acoustic phenomenon using short-term indicators.
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| 8. |
- Liguori, Consolatina, et al.
(författare)
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Innovative bootstrap approach for the estimation of minimum measurement time interval in road traffic noise evaluation
- 2017
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Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 98, s. 237-242
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Tidskriftsartikel (refereegranskat)abstract
- It is observed that in order to characterize the environmental noise in a site, during diurnal reference time (6–22h) or nocturnal reference time (22–6h), relatively at preset time window, observation period, a single value of the equivalent continuous A-weighted sound pressure level LA,eq is used. This value is determined by integrating and averaging the squared A-weighted sound pressure of fluctuating noise during the measurement time interval, in which there are representative values of acoustic event pressure levels: so it is very important accurately to select the suitable integration time. Such matter are highly relevant to the area of measuring environmental noise and this paper aims to present a statistical method, for determining the minimum measurement time interval for an accurate estimation of LAeq. The proposed algorithm, based on CPER bootstrap method, has been experimentally verified with real data obtained from road traffic noise measurement and it showed a very good stability. The methodology is suitable for upgrading the level meter firmware in order to have the real time information on the measurand uncertainty estimation and on the minimum measurement time interval.
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| 9. |
- Liguori, Consolatina, et al.
(författare)
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Proposal for the automatic evaluation of workers' exposure to acoustic noise following task-based approach
- 2021
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Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 173
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Tidskriftsartikel (refereegranskat)abstract
- Accurate measurement of exposure to noise in the workplace is important for employee health prevention as well as cost implications for employers. Standard ISO 9612 employs an engineering methodology for estimating noise exposure levels including levels of uncertainty. In this procedure some aspects are left to the discretion of the operator. Beginning with preliminary studies on the determination of the measurement intervals for evaluating workers' exposure to noise, this paper proposes an innovative approach to estimating work conditions for bus drivers. Measurement results are analysed and compared to the estimations based on both the ISO 9612 and the continuous acquisition analysis showing the feasibility of the proposal for accurately measuring the exposure to acoustic noise in a typical work situation.
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| 10. |
- Nie, Yali, et al.
(författare)
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A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss
- 2023
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Ingår i: Diagnostics. - : MDPI AG. - 2075-4418. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Skin cancers are the most cancers diagnosed worldwide, with an estimated > 1.5 million new cases in 2020. Use of computer-aided diagnosis (CAD) systems for early detection and classification of skin lesions helps reduce skin cancer mortality rates. Inspired by the success of the transformer network in natural language processing (NLP) and the deep convolutional neural network (DCNN) in computer vision, we propose an end-to-end CNN transformer hybrid model with a focal loss (FL) function to classify skin lesion images. First, the CNN extracts low-level, local feature maps from the dermoscopic images. In the second stage, the vision transformer (ViT) globally models these features, then extracts abstract and high-level semantic information, and finally sends this to the multi-layer perceptron (MLP) head for classification. Based on an evaluation of three different loss functions, the FL-based algorithm is aimed to improve the extreme class imbalance that exists in the International Skin Imaging Collaboration (ISIC) 2018 dataset. The experimental analysis demonstrates that impressive results of skin lesion classification are achieved by employing the hybrid model and FL strategy, which shows significantly high performance and outperforms the existing work.
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| 11. |
- Nie, Yali, et al.
(författare)
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Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks
- 2019
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Ingår i: 2019 E-Health and Bioengineering Conference (EHB). - : IEEE. - 9781728126036
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Konferensbidrag (refereegranskat)abstract
- In the past three years, deep convolutional neural networks (DCNNs) have achieved promising performance in detecting skin cancer. However, improving the accuracy and efficiency of the automatic detection of melanoma is still urgent due to the visual similarity of benign and malignant dermoscopy. There is also a need for fast and computationally effective systems for mobile applications targeting caregivers and homes. This paper presents the You Only Look Once (Yolo) algorithms, which are based on DCNNs applied to the detection of melanoma. The Yolo algorithms comprise YoloV1, YoloV2, and YoloV3, whose methodology first resets the input image size and then divides the image into several cells. According to the position of the detected object in the cell, the network will try to predict the bounding box of the object and the class confidence score. Our test results indicate that the mean average precision (mAP) of Yolo can exceed 0.82 with a training set of only 200 images, proving that this method has great advantages for detecting melanoma in lightweight system applications.
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| 12. |
- Nie, Yali, et al.
(författare)
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Deep Melanoma classification with K-Fold Cross-Validation for Process optimization
- 2020
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Ingår i: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA). - : IEEE. - 9781728153865
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Konferensbidrag (refereegranskat)abstract
- Deep convolution neural networks (DCNNs) enable effective methods to predict the melanoma classes otherwise found with ultrasonic extraction. However, gathering large datasets in local hospitals in Sweden can take years. Small datasets will result in models with poor accuracy and insufficient generalization ability, which has a great impact on the result. This paper proposes to use a K-Fold cross validation approach based on a DCNN algorithm working on a small sample dataset. The performance of the model is verified via a Vgg16 extracting the features. The experimental results reveal that the model built by the approach proposed in this paper can effectively achieve a better prediction and enhance the accuracy of the model, which proves that K-Fold can achieve better performance on a small skin cancer dataset.
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| 13. |
- Nie, Yali, et al.
(författare)
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Ensembling CNNs for dermoscopic analysis of suspicious skin lesions
- 2021
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Ingår i: 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA). - : IEEE. - 9781665419147
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Konferensbidrag (refereegranskat)abstract
- Deep Convolution Neural Networks (CNN) enable advanced methods to predict the skin cancer classes through the automatic analysis of digital dermoscopic images. However, small datasets' availability often allows the models to be characterized by low prediction accuracy and poor generalization ability, which significantly influences clinical decisions. This paper proposes to use an original ensembling of multiple CNNs as feature extractors able to detect and measure skin lesions atypical criteria according to the well-known diagnostic method 7-Point Check List. The experimental results show that the Artificial Intelligence-based model can suitably manage the classification uncertainty of the single CNNs and finally distinguish melanomas from benignant nevi. Diagnostic performance is promising in terms of sensitivity and specificity towards a decision-supporting system used by a dermatologist with low experience during clinical practice.
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| 14. |
- Nie, Yali, et al.
(författare)
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Recent Advances in Diagnosis of Skin Lesions using Dermoscopic Images based on Deep Learning
- 2022
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Ingår i: IEEE Access. - 2169-3536. ; 10, s. 95716-95747
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Tidskriftsartikel (refereegranskat)abstract
- Skin cancer is one of the most threatening cancers, which spreads to the other parts of the body if not caught and treated early. During the last few years, the integration of deep learning into skin cancer has been a milestone in health care, and dermoscopic images are right at the center of this revolution. This review study focuses on the state-of-the-art automatic diagnosis of skin cancer from dermoscopic images based on deep learning. This work thoroughly explores the existing deep learning and its application in diagnosing dermoscopic images. This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this. We discuss advancements in deep learning-based solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems to support dermatologists and enhance their ability to diagnose skin cancer. Author
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| 15. |
- Nie, Yali, et al.
(författare)
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Skin Cancer Classification based on Cosine Cyclical Learning Rate with Deep Learning
- 2022
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Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE. - 9781665483605
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Konferensbidrag (refereegranskat)abstract
- Since early-stage skin cancer identification can improve melanoma prognosis and significantly reduce treatment costs, AI-based diagnosis systems might greatly benefit patients suffering from suspicious skin lesions. The study proposes a cosine cyclical learning rate with a skin cancer classification model to improve melanoma prediction. The contributions of models involve three critical CNNs, which are standard deep feature extraction modules for the skin cancer classification in this study (Vgg19, ResNet101 and InceptionV3). Each CNN model applies three different learning rates: fixed learning rate(LR), Cosine Annealing LR, and Cosine Annealing with WarmRestarts. HAM10000 is a large collection of publicly available dermoscopic images dataset used for our experiments. The performance of the proposed approach was appraised through comparative experiments. The outcome has indicated that the proposed method has high efficiency in diagnosing skin lesions with a cosine cyclical learning rate.
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