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Mario Bijelic - IEEE Xplore Author Profile

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Excessive alcohol consumption was responsible for 6% of global deaths in 2023. To encourage healthier drinking habits and enhance user awareness of their current condition, just-in-time interventions prove to be a suitable approach for informing users about their current state of intoxication. Current methods for determining blood alcohol content are intrusive and many also invasive, requiring use...Show More
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a pivotal role. An important application within this domain inv...Show More
Alcohol consumption has a significant impact on individuals’ health, with even more pronounced consequences when consumption becomes excessive. One approach to promoting healthier drinking habits is implementing just-in-time interventions, where timely notifications indicating intoxication are sent during heavy drinking episodes. However, the complexity or invasiveness of an intervention mechanism...Show More
Hyperdimensional computing (HDC) is a computing framework that has gained significant attention due to its high efficiency and rapid training and inference of machine learning algorithms [1]. With its fast learning and inference capabilities, HDC shows excellent potential for IoT/Embedded systems. However, while HDC allows for fast single-pass learning [2], it suffers from weak classification accu...Show More
Hyperdimensional computing (HDC) is a novel computing framework that has gained significant attention for its ability to accelerate machine learning algorithms. Its fast learning and inference capabilities make it an ideal technique for various fields, including machine learning. HDC utilizes high-dimensional holographic vectors, which are vectors with independent and identically distributed dimen...Show More
Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments. The encoding of information to the hyperspace is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing atomic information. We present a detailed study on basis-hypervectors, leading to ...Show More
Diabetes impacts around 8% of the world’s population, with Type 2 diabetes comprising up to 90% of cases. This chronic disease is characterized by a metabolic resistance to insulin which results in a high blood sugar level and increased potential for serious health complications. Preventative medicine and the detection of genetic predisposition play a key part in successful treatment. Although sev...Show More
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness. HDC models have already been proven to be useful in diffe...Show More
Pneumonia is a common complication associated with COVID-19 infections. Unlike common versions of pneumonia that spread quickly through large lung regions, COVID-19 related pneumonia starts in small localized pockets before spreading over the course of several days. This makes the infection more resilient and with a high probability of developing acute respiratory distress syndrome. Because of the...Show More
Sepsis arises when a patient’s immune system has an extreme reaction to an infection. This is followed by septic shock if damage to organ tissue is so extensive that it causes a total systemic failure. Early detection of septic shock among septic patients could save critical time for preparation and prevention treatment. Due to the high variance in symptoms and patient state before shock, it is ch...Show More
This paper introduces the Gravity compiler. Gravity is an open source optimizing Artificial Neural Network (ANN) to ANSI C compiler with two unique design features that make it ideal for use in resource constrained embedded systems: (1) the generated ANSI C code is self-contained and void of any library or platform dependencies and (2) the generated ANSI C code is optimized for maximum performance...Show More
In the micro-tolling paradigm, a centralized system manager sets different toll values for each link in a given traffic network with the objective of optimizing the system's performance. A recently proposed micro-tolling scheme, denoted Δ-tolling, was shown to yield up to 32% reduction in total travel time when compared to a no-toll scheme. Δ-tolling, computes a toll value for each link in a given...Show More
Advances in sensing, computation, storage, and actuation technologies have entered cyber-physical systems (CPSs) into the smart era where complex control applications requiring high performance are supported. Neural networks (NNs) models are proposed as a predictive model to be used in model predictive control (MPC) applications. However, the ability to efficiently exploit resource hungry NNs in e...Show More
Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we le...Show More
Cyber-Physical Systems (CPS) are composed of computing devices interacting with physical systems. Model-based design is a powerful methodology in CPS design in the implementation of control systems. For instance, Model Predictive Control (MPC) is typically implemented in CPS applications, e.g., in path tracking of autonomous vehicles. MPC deploys a model to estimate the behavior of the physical sy...Show More
Cyber-Physical Systems (CPS) are composed of computation, networking, and physical processes. Model-based design is a powerful technique to apply mathematical modeling in CPS design. A model of a physical system is the description of variations in some aspects and properties of the system such as motion, velocity, and pressure. The variations of physical quantities such as motion, velocity, and pr...Show More
Artificial Intelligence methods to solve continuous-control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real-world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed t...Show More
Variabilities in the execution time of integrated circuits are frequently exploited as a side channel attack to expose secret information of deployed systems. Standard countermeasures analyze and change the explicit timing behavior in lower level hardware description languages, but their application is time consuming and error-prone. In this paper we investigate the integration of timing attack re...Show More
Cyber Physical Systems (CPSs) integrate networked embedded computation systems with real-world physical installations. Programming of CPSs is not trivial, since CPSs combine traditional programming challenges and real-world timing, concurrency, and communication. This paper shows how a programming framework that allows students to implement and test CPS control programs in their Internet browsers,...Show More
The demand for compute cycles needed by embedded systems is rapidly increasing. In this paper, we introduce the XGRID embedded many-core system-on-chip architecture. XGRID makes use of a novel, FPGA-like, programmable interconnect infrastructure, offering scalability and deterministic communication using hardware supported message passing among cores. Our experiments with XGRID are very encouragin...Show More
A good cyber-physical-systems (CPS) design methodology must conduct trade-off analysis of both the physical characteristics of the CPS as well as its cyber sub-system in a holistic manner. This paper presents a design space exploration (DSE) approach for CPSs that emphasizes the variabilities of the physical subsystem and control aspects of the system. We propose the application of parameterizable...Show More
In this paper we outline a novel approach for accessing mutually exclusive resources in hierarchically scheduled real-time systems. Our method known as the Resource Access Control Protocol with Preemption (RACPwP) is an improved resource allocation protocol which utilizes preemptive critical sections to provide guaranteed determinism for hard real-time tasks and comparable response times for soft ...Show More
This paper presents an analysis framework for correct system operation (i.e. system success) of Cyber-Physical Systems (CPS) that deploy binary sensors with possible faults. We discuss potential faults in the interface part of such systems and address solutions for those faults in order to build dependable and reliable CPS applications. As a practical tool, we present a set of models in SIMULINK t...Show More
Within the design of Cyber Physical Systems, model-based approaches are powerful means to describe and test the behavior of the system. Still, a good methodology is needed to go from the idealized model environment to an implementable system architecture that is capable of dealing with uncertainties in both the physical and the cyber subsystem. This paper presents a concept that explicitly utilize...Show More