Salvatore Distefano

• Professor
• Professor (Full) at University of Messina

This book and its offshoots were prepared to provide comprehensive information about the Internet of Things on the engineering level. Its goal is to introduce IoT to bachelor students, master students, technology enthusiasts and engineers willing to extend their current knowledge with the latest hardware and software achievements in the scope of th...

Hyperspectral image (HSI) classification remains a challenging task due to the intricate spatial-spectral correlations. Existing transformer models excel in capturing long-range dependencies but often suffer from information redundancy and attention inefficiencies, limiting their ability to model fine-grained relationships crucial for HSI classific...

Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination in applications such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability of HSI data pose significant challenges for feature extracti...

Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges due to the high-dimensional nature of the data and the complex spectral-spatial relationships inherent in HSI. Traditional methods, including conventional machine learni...

Hyperspectral image classification (HSIC) presents significant challenges due to spectral redundancy and spatial discontinuity, both of which can negatively impact classification performance. To mitigate these issues, this work proposes the Differential Spatial-Spectral Transformer ( DiffFormer ), a novel framework designed to enhance feature discr...

Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. In this work, we propose the Differential Spatial-Spectral Transformer (DiffFormer), a novel framework designed to address the inherent challenges of HSIC, such as spectra...

The classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data typically available for training. In this study, we propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for effi...

Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This paper...

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new exam...

The 3D Swin Transformer (3DST) and Spatial-Spectral Transformer (SST) each excel in capturing distinct aspects of image information: 3DST with hierarchical attention and window-based processing, and SST with self-attention mechanisms for long-range dependencies. However, applying them independently reveals limitations: 3DST struggles with spectral...

The Transformer model encounters challenges with variable-length input sequences, leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical Spatial-Spectral Transformer (PyFormer). This innovative approach organizes input data hierarchically into pyramid segments, each representing distinct abstractio...

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention an...

In recent years, Transformers have garnered significant attention for Hyperspectral Image Classification (HSIC) due to their self-attention mechanism, which provides strong classification performance. However, these models face major challenges in computational efficiency, as their complexity increases quadratically with the sequence length. The Ma...

In Transformer-based Hyperspectral Image Classification (HSIC), predefined positional encodings (PEs) are crucial for capturing the order of each input token. However, their typical representation as fixed-dimension learnable vectors makes it challenging to adapt to variable-length input sequences, thereby limiting the broader application of Transf...

Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely int...

In the pandemic scenario, characterized by lockdowns and service interruptions, Distance Learning allowed educational pathways remotely, proving to be a reliable solution, resilient to the emergency. However, issues related to its effectiveness as well as social and psychological implications, remain unresolved. From many sides, there is a desire t...

Nestled at the confluence of nature grandeur and human civilization, beaches command an influential presence that resonates throughout the environment, society, and culture. However, climate change and pollution overhang the beach health and need to be properly dealt with. Proactive measures involve education, responsible waste management, sustaina...

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to...

This research proposes a smart parking system that is geared towards business entities. The system focuses on privacy, scalability, and performance for an enterprise system on a decentralized network(Blockchain). In a business setup, performance, scalability, and privacy should be at the centre stage. This research aims to improve on already existi...

Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant inf...

A Smart Factory exploits information and communication technologies (ICT) to improve the production process and the working environment, usually addressing safety concerns. To this concern, factory-grade fire protection systems are governed by several procedures and standards whose application often becomes definitely challenging when the factory p...

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification c...

Hyperspectral Image Classification (HSIC) is a challenging task due to the spectral mixing effect which induces high intra-class variability and inter-class similarity. To overcome these limitations, Convolutional Neural Networks (CNNs) are utilized for feature extraction and classification. However, 3D CNNs are computationally expensive and 2D CNN...

Wastewater treatment is a critical process in urban and industrial settlements aiming to clean and protect the water as well as the overall environment. Wastewater management systems are conceived explicitly for purifying wastewater, providing clean water efficiently, but this is a hard task due to frequent and quite unpredictable fluctuations of i...

The primary goal of a wastewater treatment system is to take care of the environment as well as of people health by purifying sewage water. In urban and industrial environments, wastewater management is non-trivial since it has to deal with abnormal fluctuations in incoming water flows (due to rainwater or human and industrial sewage) that may caus...

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Therefore, this paper p...

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identifica...

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Several regularization...

The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an...

Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner’s personal information and services against the stored passwords. As a result of this po...

Modern transportation systems, such as computer networks, have become increasingly faster, aiming to "shorten" distances and travel time. This trend allows thinking about new services and induces to reconsider existing ones starting from new technologies, as for Intelligent Transportation Systems. Thereby, an all-encompassing scenario laying at the...

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information wh...

Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI classification (HSIC) is a challenging task due to high interclass similarity, high intraclass variability, overlapping, and nested regions. The 2-D convolutional neural network (CNN) is a viable classification approach since HSIC depends on both spectral-spatial...

In this paper, we envision to illustrate the process to be used in developing a mobile application on the smart contract. We start by looking at what other researchers have accomplished and how we can improve on what already exists. The paper highlights the requirement gathering process, the methodology for data collection to streamline and validat...

Driving in modern cities is getting harder and harder due to the continuously changing road and traffic conditions, binding to navigation systems for successful route planning. Despite navigators are becoming more and more powerful and precise in estimating road and traffic conditions, there is still room for improving their effectiveness in route...

In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the...

In this paper, we envision to illustrate the process to be used in developing a mobile application on the smart contract. We start by looking at what other researchers have accomplished and how we can improve on what already exists. The paper highlights the requirement gathering process, the methodology for data collection to streamline and validat...

Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored passwords. As a result of this po...

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the p...

Global warming and climate changes are due to several factors, not least vehicle and transportation emissions. Smart City technologies can provide mechanisms for emission(greenhouse gases, particles) containment that may significantly impact on the environment. This paper proposes a solution, based on an intelligent cruise control system, allowing...

Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried out on joint spectral–spatial information. Thirdly, a minor but still worth mentioning factor is the s...

This paper discusses an open-source solution for smart-parking in highly urbanized areas. We have conducted interviews with domain experts, defined user stories and proposed a system architecture with a case study. Our solution allows integration of independent owners of parking space into one unified system, that facilitates parking in a smart cit...

Vehicular ad hoc networks (VANETs) present an intriguing platform for several applications on e.g., intelligent transportation system (ITS) and infotainment applications aspire to be the main pattern of communication among vehicles while travelling. This can significantly impact on the amount of data exchanged by vehicles, increasing the contention...

The Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time del...

The Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time del...

This paper discusses an open-source solution for smart-parking in highly urbanised areas. We have conducted interviews with domain experts, defined user stories and proposed a system architecture with a case study. Our solution allows integration of independent owners of parking space into one unified system, that facilitates parking in a smart cit...

This paper discusses an open source solution to smart-parking in highly urbanized areas. Interviews have been conducted with domain experts, user stories defined and a system architecture has been proposed with a case study. Our solution allows independent owners of parking space to be integrated into one unified system, that facilitates the parkin...

DevOps is a quite effective approach for managing software development and operation, as confirmed by plenty of success stories in real applications and case studies. DevOps is now becoming the main-stream solution adopted by the software industry in development, able to reduce the time to market and costs while improving quality and ensuring evolv...

The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the “lines of code added since the l...

Hyperspectral imaging has been extensively utilized in several fields, and it benefits from detailed spectral information contained in each pixel, generating a thematic map for classification to assign a unique label to each sample. However, the acquisition of labeled data for classification is expensive in terms of time and cost. Moreover, manual...

Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a la...

This book is a collection of outstanding papers presented at the 1st International Conference on Advances in Computational Intelligence and Informatics (ICACII 2019), organized by the Department of Computer Science & Engineering, Anurag Group of Institutions (AGI), Hyderabad, on 20–21 December 2019. It includes innovative ideas and new research fin...

DevOps is a quite effective approach for managing software development and operation, as confirmed by plenty of success stories in real applications and case studies. DevOps is now becoming the main-stream solution adopted by the software industry in development, able to reduce the time to market and costs while improving quality and ensuring evolv...

The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the l...

DevOps is a quite effective approach for managing software development and operation, as confirmed by plenty of success stories in real applications and case studies. DevOps is now becoming the main-stream solution adopted by the software industry in development, able to reduce the time to market and costs while improving quality and ensuring evolv...

Technological development has brought about a profound transformation of modern society. New technologies and media have completely redefined the way we communicate, inform, study, work, create and disseminate knowledge, weaving social relationships, with significant benefits in our daily lives. However, from an educational point of view, the avail...

The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only in particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the l...

This book and its offshoots were prepared to provide comprehensive information about the Internet of Things. Its goal is to introduce IoT to the bachelor students, master students, technology enthusiasts and engineers that are willing to extend their current knowledge. This book is also designated for teachers and educators willing to extend their...

A laboratory guide for VREL projects

The Internet of Things (IoT) and Robotics cannot be considered two separate domains these days. The Internet of Robotics Things (IoRT) is a concept that has been recently introduced to describe the integration of robotics technologies in IoT scenarios. As a consequence, these two research fields have started interacting, and thus linking research c...

The vision encompassing Smart and Software-defined Buildings (SSDB) is becoming more and more popular and its implementation is now more accessible due to the widespread adoption of the IoT infrastructure. Some of the most important applications sustaining this vision are energy management , environmental comfort, safety and surveillance. This pape...

Enabling data processing at the network edge, as close to the actual source of data as possible, is a challenging, yet realistic goal to be achieved by the Internet of Things (IoT), which still primarily relies on the Cloud for data processing. By further extending the Fog and Edge computing principles, recent research advancements enabled aggregat...

The vision encompassing Smart and Software-defined Buildings (SSDB) is becoming more popular and its implementation is now more accessible due to the widespread adoption of the Internet of Things (IoT) infrastructure. Some of the most important applications sustaining this vision are energy management, environmental comfort, safety and surveillance...

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to...

Smartphones have ubiquitously integrated into our home andwork environments, however, the user normally relies on explicit but inefficient identification processes in a controlled environment. Therefore, when the device is stolen, the attacker can have access to the user’s personal information and services against the stored password/ s. As a resul...

Presentation at DevOps 19 about Software Release anomaly detection in DevOps environment - project in cooperation between Innopolis University, University of Messina and Khwaja Fareed University of Engineering and Information Technology.

Physical activity recognition (PAR) using wearable devices can provide valued information regarding an individual's degree of functional ability and lifestyle. In this regards, smartphone-based physical activity recognition is a well-studied area. Research on smartwatch-based PAR, on the other hand, is still in its infancy. Through a large-scale ex...

Smartphones have pervasively integrated into our home and work environments managing confidential information but their owners still rely on as explicit as inefficient and insecure identification processes. Therefore, if a device is stolen, a thief can have access to the owner's personal information and services though the stored password/s. To avo...

To enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they represent a stacked architecture, in which raw datasets are first pre-processed locally at the E...

Abstract The Internet of Things (IoT) facilitates creation of smart spaces by converting existing environments into sensor-rich data-centric cyber-physical systems with an increasing degree of automation, giving rise to Industry 4.0. When adopted in commercial/industrial contexts, this trend is revolutionising many aspects of our everyday life, inc...

Hyperspectral imaging (HSI) has attracted the formidable interest of the scientific community and has been applied to an increasing number of real-life applications to automatically extract the meaningful information from the corresponding high dimensional datasets. However, traditional autoencoders (AE) and restricted Boltzmann machines are comput...

Integration of the Internet of Things (IoT) with the Cloud may lead to a range of different architectures and solutions. Our efforts in this domain are mainly geared toward making IoT systems available as service-oriented infrastructure. Under Infrastructure-as-a-Service (IaaS) scenarios, network virtualization is a core building block of any solut...

The Internet of Things is underpinned by the global penetration of network‐connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures – on the one hand, and Big Data processing techniques – on the other, existing approaches, however, primarily adopt...

Big Data solutions aim to cope with the overwhelming amount of data generated by various domains, such as social networks and the Internet of Things, thereby enabling a new generation of data-intensive applications (DIAs) and services. At the same time, to facilitate DIA design and development processes and address (Big) data management requirement...

It has been observed how the technological progress of our civilization mimics the extraordinarily smart design choices of the nature surrounding us. As scientists have been inspired by the cosmos to observe and understand its mechanics from the deepest to the smallest particle, so engineers have found inspiration in biological systems to design t...

#SmartME has been one of the first initiatives in Italy to realize a Smart City through the use of open technologies. Thanks to the use of low cost sensor-powered devices scattered over the city area, different ``smart'' services have been deployed having the Stack4Things framework as the common underlying middleware. In this paper, we present the...