In the finite field of integers, the number theoretic transform (NTT) is a specific variant of the discrete fourier transform (DFT). NTT is the essential method that permits efficient computing. therefore, NTT could b...
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An analog-to-digital converter (ADC) is a critical part of most computing systems as it converts analog signals into quantifiable digital values. Since most digital devices operate only on digital values, the ADC acts...
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ISBN:
(纸本)9783031411809;9783031411816
An analog-to-digital converter (ADC) is a critical part of most computing systems as it converts analog signals into quantifiable digital values. Since most digital devices operate only on digital values, the ADC acts as an interface between the digital and analog worlds. Hence, ADCs are commonly used in a wide-range of application areas, such as internet of things (IoT), industrial controlsystems (ICS), cyberphysical systems (CPS), audio/video devices, medical imaging, digital oscilloscopes, and cell phones, among others. For example, programmable logic controllers (PLCs) in ICS/CPS often make control decisions based on digital values that are converted from analog signals by ADCs. Due to its crucial role in various applications, ADCs are often targeted by a wide-range of physical and cyber attacks. Attackers may exploit vulnerabilities that could be found in the software/hardware of ADCs. In this work, we first conduct a deeper study on the ADC conversion logic to scrutinize relevant vulnerabilities that were not well explored by prior works. Hence, we manage to identify exploitable vulnerabilities on certain ADC registers that are used in the ADC conversion process. these vulnerabilities can allow attackers to launch dangerous attacks that can disrupt the behaviour of the targeted system (e.g., an IoT or control system) in a stealthy way. As a proof of concept, we design three such attacks by exploiting the vulnerabilities identified. Finally, we test the attacks on a mini-CPS testbed we designed using IoT devices, analog sensors and actuators. Our experimental results reveal high effectiveness of the proposed attack techniques in misleading PLCs to make incorrect control decisions in CPS. We also analyze the impact of such attacks when launched in realistic CPS testbeds.
Trust, reliance, and robustness have been identified as key elements for team fluency between teams. they are also crucial elements for successful collaboration between humans and robots (HRC). Robot arms have become ...
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ISBN:
(纸本)9789811912801;9789811912795
Trust, reliance, and robustness have been identified as key elements for team fluency between teams. they are also crucial elements for successful collaboration between humans and robots (HRC). Robot arms have become integral to numerous digital design and fabrication processes allowing new material forms, more efficient use of materials and novel geometries. It will not be long before close proximity HRC design becomes standard. However, little research has been directed at understanding team fluency development between industrial robots and humans (industrial HRC). Even less to understand the evolution of HRC in creative tasks and factors that influence elements like trust to be established between industrial robot arms and designers. Team fluency is a multidimensional construct, heavily dependent on the context. It is crucial to understand how team fluency develops when designers interact with industrial robots. To this end, in this study, a team fluency measurement scale suitable for industrial HRC in design activities was developed in two stages. In the first stage, HRC literature was reviewed to establish a measurement scale for the different team fluency constructs and identify team fluency-related themes relevant to the design context. A corresponding pool of questionnaire items was generated. In the second stage, an exploratory HRC design exercise was designed and conducted to collect participant's opinions qualitatively and quantitative. Questionnaire items were applied to participants. the results were statistically analyzed to identify the key factors impacting team fluency. A set of curriculum recommendations is made, and a team fluency scale is proposed to measure HRC in design activities.
Reading improves the reader's vocabulary and knowledge of the world. It can open minds to different ideas which may challenge the reader to view things in a different light. Reading books benefits both physical an...
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this paper proposes the design and implementation of a comprehensive Smart Bus Ticketing System leveraging Radio-Frequency Identification (RFID), Global System for Mobile Communications (GSM), Global Positioning Syste...
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the integration of artificial intelligence in mechanical fault detection and diagnosis (FDD) helps to increase reliability, reduce costs, and improve the overall performance of mechanical systems in Industry 4.0 appli...
the integration of artificial intelligence in mechanical fault detection and diagnosis (FDD) helps to increase reliability, reduce costs, and improve the overall performance of mechanical systems in Industry 4.0 applications. Most interesting industrial applications nowadays come from dynamic environments where data are generated continuously over time and where the labeled data are scarce and expensive. therefore, semi-supervised learning (SSL) can be particularly useful in FDD because faults may be rare or difficult to identify, and may not be fully represented in the labeled data. By using a combination of labeled and unlabeled data, SSL can help to identify these rare or difficult-to-detect faults, leading to more effective FDD. In this paper, graph-based SSL relying on label propagation is combined with conventional classification algorithms to detect potential failures in complex mechanical systems. Experimental results on realistic pneumatic and hydraulic systems from the related literature show that the proposed method can effectively enlarge the labeled datasets and interestingly identify different types of non-nominal conditions with higher accuracy compared to baseline methodologies.
Flipped Classroom (FC) is an active learning design requiring the students to engage in pre-class learning activities to prepare for face-to-face sessions. Identifying FC learning behaviors that lead to academic succe...
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the proceedings contain 26 papers. the special focus in this conference is on Simulation of Adaptive Behavior. the topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Mode...
ISBN:
(纸本)9783031715327
the proceedings contain 26 papers. the special focus in this conference is on Simulation of Adaptive Behavior. the topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent systems: A Computational Modeling Approach;transient Milling Dynamics in Collective Motion with Visual Occlusions;extended Swarming with Embodied Neural Computation for Human control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for Data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive Exploration control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of Exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy Analysis in Education: A Multimodal Computational Model.
Many Internet of things (IoT) applications are considered critical systems, and it is important to guarantee that such deployments are resilient to attacks. An attacker may use radio interference selectively to disrup...
Many Internet of things (IoT) applications are considered critical systems, and it is important to guarantee that such deployments are resilient to attacks. An attacker may use radio interference selectively to disrupt communication while minimising the risk of their detection. It is essential to identify such attacks in order to remove the threat. In this work, we propose a novel method of detecting targeted interference in a Narrowband-Internet of things (NB-IoT) network at the User Equipment (UE). NB-IoT is a recent Low Power Wide Area Network (LPWAN) radio technology used to deploy IoT infrastructures at scale. Network performance data collected at the UE is used to reason about the current interference situation. Subframe loss rates within the downlink channel are monitored and used as input for a statistical anomaly detector. Our evaluation shows that the detector is able to distinguish targeted interference attacks from the impact of naturally occurring interference in cellular networks. this is important as naturally occurring interference requires a different response than targeted interference attacks.
In this paper, we study the "decoding" problem for discrete-time, stochastic hybrid systems with linear dynamics in each mode. Given an output trace of the system, the decoding problem seeks to construct a s...
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ISBN:
(纸本)9781450391962
In this paper, we study the "decoding" problem for discrete-time, stochastic hybrid systems with linear dynamics in each mode. Given an output trace of the system, the decoding problem seeks to construct a sequence of modes and states that yield a trace "as close as possible" to the original output trace. the decoding problem generalizes the state estimation problem, and is applicable to hybrid systems with non-determinism. the decoding problem is NP-complete, and can be reduced to solving a mixed-integer linear program (MILP). In this paper, we decompose the decoding problem into two parts: (a) finding a sequence of discrete modes and transitions;and (b) finding corresponding continuous states for the mode/transition sequence. In particular, once a sequence of modes/transitions is fixed, the problem of "filling in" the continuous states is performed by a linear programming problem. In order to support the decomposition, we "cover" the set of all possible mode/transition sequences by a finite subset. We use well-known probabilistic arguments to justify a choice of cover with high confidence and design randomized algorithms for finding such covers. Our approach is demonstrated on a series of benchmarks, wherein we observe that relatively tiny fraction of the possible mode/transition sequences can be used as a cover. Furthermore, we show that the resulting linear programs can be solved rapidly by exploiting the tree structure of the set cover.
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