22 research outputs found
Protection and Retrieval of Encrypted Multimedia Content: When Cryptography Meets Signal Processing
No full textThe processing and encryption of multimedia content are generally considered sequential and independent operations. In certain multimedia content processing scenarios, it is, however, desirable to carry out processing directly on encrypted signals. The field of secure signal processing poses significant challenges for both signal processing and cryptography research; only few ready-to-go fully integrated solutions are available. This study first concisely summarizes cryptographic primitives used in existing solutions to processing of encrypted signals, and discusses implications of the security requirements on these solutions. The study then continues to describe two domains in which secure signal processing has been taken up as a challenge, namely, analysis and retrieval of multimedia content, as well as multimedia content protection. In each domain, state-of-the-art algorithms are described. Finally, the study discusses the challenges and open issues in the field of secure signal processing.Electrical Engineering, Mathematics and Computer Scienc
Occupancy information and people counting in smart buildings using noninvasive sensors and machine learning
No full textOccupancy information is a fundamental resource in smart building management, as it provides insight into how spaces are used and allows building systems to respond dynamically. It can capture different levels of detail, ranging from simple presence detection to more advanced forms such as activity recognition. Within this hierarchy, people counting represents a critical level of occupancy information, focusing on estimating the number of individuals in a given space. Accurate people counting enables a wide range of applications, including demand-driven Heating, Ventilation, and Air Conditioning (HVAC) control, optimization of space utilization, compliance with safety and security regulations, and the enhancement of occupant comfort and experience. Despite its importance, people counting with non-privacy-invasive sensors remains a challenging task.Vision-based and device-tracking approaches often achieve high accuracy but raise concerns over privacy, cost, and scalability, limiting their practical use. While non-privacy-invasive methods have been widely explored, they still lack low-cost and scalable solutions that leverage sensors already embedded in smart buildings. Binary PIR devices, for example, are common in building automation systems for presence detection, yet their potential for multi-person counting remains underexplored. No comprehensive review has mapped the role of binary and signal-based PIR sensors across different levels of occupancy information or examined their integration with machine learning in real-world settings. Previous studies have typically focused on controlled lab setups or dense multi-sensor deployments, which limit generalizability. In addition, contextual booking data has rarely been incorporated directly into occupancy estimation models. Finally, advanced temporal deep learning models such as Transformers, along with systematic analyses of historical window size and feature importance based on environmental data, are still underexplored, leaving open opportunities to optimize both modeling strategies and sensor deployment in real-world smart buildings. This thesis addresses these gaps through four studies. First, a systematic literature review synthesizes the role of binary and signal-based PIR sensors across different levels of occupancy information, identifying research gaps and motivating new approaches. Second, an event-based framework demonstrates that reliable occupancy estimates can be achieved using only two binary PIR sensors combined with machine learning. Third, a context-aware extension integrates PIR data with booking records, improving estimation accuracy and exposing patterns of underutilization, overbooking, and mismatches between planned and actual usage. Finally, a fixed-interval time-series framework evaluates advanced deep learning architectures, including RNNs, LSTMs, GRUs, CNN-LSTMs, and Transformers, showing how temporal context length and multimodal feature sets influence performance. Taken together, these studies advance the state of the art by demonstrating that accurate, scalable, and privacy-preserving people counting does not require invasive or costly infrastructures. By leveraging PIR sensors already present in many buildings, enriched with contextual and environmental data and analyzed through modern machine learning techniques, smart buildings can achieve reliable occupancy estimation. Beyond technical performance, the findings highlight the broader operational value of occupancy information for efficient space management, energy savings, and improved occupant experience, while laying the foundation for next-generation intelligent building systems.Paper IV in dissertation as manuscript and not included in the fulltext online</p
Determination of Magnesium, Calcium and Sulphate Ion Impurities in Commercial Edible Salt
No full textNatural elemental impurities are recognized as a threat for safety and quality of edible salt and have adverse effects on public health. In the current study, fiftysamples of packages containing 1 kg of salt from 25 different brands were collected from retailers in Semnan city (Iran). The concentrations of main impurities of edible salt including magnesium (Mg), calcium (Ca), and sulphate (SO4-2) ions were quantified by the aid of an Ion Chromatography with conductivity detector. According to findings, the maximum concentrations of Mg, Ca, and SO4-2 ions in salt samples were 0.067, 0.226, and 0.888 % w/w (dry matter basis), respectively. In addition, the concentration of Mg in 16%, Ca in 4%, and SO4-2 in 28% of samples suppressed the acceptable limit proposed by the Institute of Standards and Industrial Research of Iran (ISIRI) (0.15% for Ca, 0.03% for Mg, and 0.46% for SO4-2). Moreover, the maximum and minimum levels of purity in the salt samples were recorded as 99.940 and 97.730%, respectively. Moreover, the purity in 12% of the investigated samples was lower than that of the minimum acceptable limit suggested by ISIRI, while the purity of 97% samples met the acceptable Codex Alimentarius limit (97% Min). Based on results of the current investigation, the routine purification processes used in some factories of Iran did not reduce impurities. Hence, purification process bedsides constant monitoring and safety management should be improved to promote the health quality of edible salt
Structure–properties relationships in dental adhesives: Effect of initiator, matrix monomer structure, and nano-filler incorporation
No full textVisual Similarity for Object Detection and Alignment
No full textOver recent years, the use of digital image and video content in science and industry has increased dramatically. Some of its various applications include 3-D reconstruction, skin cancer segmentation, autonomous driving, and robotics. These applications require vision systems capable of interpreting image or video content accurately. For instance, in autonomous driving, the object needs to be correctly detected and localized prior to taking any action. Depending on the type of object (pedestrian, car, traffic signs, etc.) and the distance, the decision made in a similar situation can be different. The fundamental problems in visual object detection and classification are: learning discriminative features and modeling the variation of visual appearance of objects within the same class (e.g., cat). Appearance variation, object viewpoints, and camera viewpoints make these problems even more challenging.
In this study, the author aims to define the similarity between image objects by employing graph theory. This thesis introduces an unsupervised framework for constructing a visual similarity network (VSN) of images. This VSN automatically discovers sub-classes and continues latent attributes. The constructed VSN has experimentally demonstrated improvement in the accuracy of image alignment and object detection
Discretization of integrals driven by multifractional Brownian motions with discontinuous integrands
No full textWe establish the rate of convergence in the L1 -norm for equidistant approximations of stochastic integrals with discontinuous integrands driven by multifractional Brownian motion. Our findings extend the known results for the case when the driver is a fractional Brownian motion.© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed
Long-range dependent completely correlated mixed fractional Brownian motion
No full textIn this paper we introduce the long-range dependent completely correlated mixed fractional Brownian motion (ccmfBm). This is a process that is driven by a mixture of Brownian motion (Bm) and a long-range dependent completely correlated fractional Brownian motion (fBm, ccfBm) that is constructed from the Brownian motion via the Molchan–Golosov representation. Thus, there is a single Bm driving the mixed process. In the short time-scales the ccmfBm behaves like the Bm (it has Brownian Hölder index and quadratic variation). However, in the long time-scales it behaves like the fBm (it has long-range dependence governed by the fBms Hurst index). We provide a transfer principle for the ccmfBm and use it to construct the Cameron–Martin–Girsanov–Hitsuda theorem and prediction formulas. Finally, we illustrate the ccmfBm by simulations.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings : A Review of Methodologies and Machine Learning Approaches
No full textBuildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains
Non-Invasive People Counting in Smart Buildings : Employing Machine Learning with Binary PIR Sensors
No full textPeople counting in smart buildings is crucial for the efficient management of building systems such as energy, space allocation, efficiency, and occupant comfort. This study investigates the use of two non-invasive binary Passive Infrared (PIR) sensors for estimating the number of people in seven office rooms with different people counting intervals. Previous studies often relied on sensor fusion or more complex signal-based PIR sensors, which increased hardware costs, raised privacy concerns, and added installation complexity. Our approach addresses these limitations by utilizing fewer sensors, reducing hardware costs, and simplifying installation, making it scalable and flexible for different room configurations, while also ensuring high consideration of privacy. Additionally, binary PIR sensors are typically part of smart building systems, eliminating the need for additional sensors. We employed several machine learning methods to analyze motion detected by binary PIR sensors, imp roving the accuracy of people counting estimates. We analyzed important features by extracting event count, duration, and density from sensor data, along with features from the room’s shape, to estimate the number of people. We used different machine learning models for estimating the number of people. Models like Gradient Boosting, XGBoost, MLP, and LGBM demonstrated superior performance for their strong ability to handle complex, non-linear relationships in sensor data, high-dimensional datasets, and imbalanced data, which are common challenges in people counting tasks using PIR sensors. These models were evaluated using performance metrics such as accuracy and F1-score. Additionally, the results show that features such as passage events and the number of detected events, combined with machine learning algorithms, can achieve good accuracy and reliability in people counting
Non-invasive occupancy estimation and space utilization in smart buildings : Leveraging machine learning with PIR sensors and booking data
No full textOccupancy estimation in smart buildings is essential for optimizing resource usage and enhancing operational efficiency. Existing estimation methods predominantly rely on cameras or advanced sensor fusion techniques, which, while accurate, are often expensive, invasive, and raise privacy concerns. Additionally, these approaches frequently require extra hardware, increasing installation complexity and operational costs. A significant gap in the literature lies in the limited use of existing smart building infrastructure, such as detection systems and booking data, for people counting. This study addresses these limitations by exclusively utilizing two binary PIR sensors (in-door and in-room) and booking data. Since PIR sensors and booking systems are already integrated into most smart building infrastructures, leveraging these existing resources helps reduce costs and simplifies implementation. The primary goal is to estimate the number of people between each in-door sensor trigger using machine learning models by incorporating people counting levels and time thresholds. Among the evaluated machine learning algorithms, the Extra Trees Classifier delivered strong performance, achieving 68.5% accuracy when the estimated occupancy differed from the actual count by at most one person, and 81.56% with a tolerance of two. These results are based on periods when the room was occupied. When both occupied and unoccupied periods were included, the accuracy was 96.10% for ±1 tolerance. Moreover, incorporating booking data enhanced people counting accuracy by 4%. The study also explores the method's ability to identify underutilization and overutilization by comparing estimated occupancy with booking records and seating capacity, thereby supporting enhanced space management in smart buildings
