In this work, we address the analog transmission of correlated information over fading Multiple Access Channels (MACs) using analog Joint Source Channel Coding (JSCC). We consider module-like mappings to encode the so...
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Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and subsymbolic AI. Symbolic AI is based on the idea that intelligence can be represented using semanti...
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Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and subsymbolic AI. Symbolic AI is based on the idea that intelligence can be represented using semantically meaningful symbolic rules and representations, while deep learning (DL), or sometimes called subsymbolic AI, is based on the idea that intelligence emerges from the collective behavior of artificial neurons that are connected to each other. A major drawback of DL is that it acts as a 'black box,' meaning that predictions are difficult to explain, making the testing & evaluation (T&E) and validation & verification (V&V) processes of a system that uses subsymbolic AI a challenge. Since neurosymbolic AI combines the advantages of both symbolic and subsymbolic AI, this survey explores how neurosymbolic applications can ease the V&V process. This survey considers two taxonomies of neurosymbolic AI, evaluates them, and analyzes which algorithms are commonly used as the symbolic and subsymbolic components in current applications. Additionally, an overview of current techniques for the T&E and V&V processes of these components is provided. Furthermore, it is investigated how the symbolic part is used for T&E and V&V purposes in current neurosymbolic applications. Our research shows that neurosymbolic AI has great potential to ease the T&E and V&V processes of subsymbolic AI by leveraging the possibilities of symbolic AI. Additionally, the applicability of current T&E and V&V methods to neurosymbolic AI is assessed, and how different neurosymbolic architectures can impact these methods is explored. It is found that current T&E and V&V techniques are partly sufficient to test, evaluate, verify, or validate the symbolic and subsymbolic part of neurosymbolic applications independently, while some of them use approaches where current T&E and V&V methods are not applicable by default, and adjustments or even new approaches are needed. Our research shows that th
As mobile devices with dual WiFi and cellular interfaces become widespread, network protocols have been developed that utilize the availability of multiple paths. To evaluate the performance of these protocols on a te...
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In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using ...
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In this work we address the transmission of correlated Gaussian sources over Multiple Input Multiple Output fading channels using analog Joint Source Channel Coding (JSCC). The source symbols are first compressed usin...
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The acoustic wave propagation from a two-dimensional subwavelength slit surrounded by metal plates decorated with Helmholtz resonators （HRs） is investigated both numerically and experimentally in this work. Owing to...
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The acoustic wave propagation from a two-dimensional subwavelength slit surrounded by metal plates decorated with Helmholtz resonators （HRs） is investigated both numerically and experimentally in this work. Owing to the presence of HRs, the effective impedance of metal surface boundary can be manipulated. By optimizing the distribution of HRs, the asymmetric effective impedance boundary will be obtained, which contributes to generating tunable acoustic radiation pattern such as directional acoustic beaming. These dipole-like radiation patterns have high radiation efficiency, no finger- print of sidelobes, and a wide tunable range of the radiation pattern directivity angle which can be steered by the spatial displacements of HRs.
we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed p...
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Chair and Varshney have derived an optimal rule for fusing decisions based on the Bayesian criterion. To implement the rule, the probability of detection P-D and the probability of false alarm P-F for each detector mu...
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Chair and Varshney have derived an optimal rule for fusing decisions based on the Bayesian criterion. To implement the rule, the probability of detection P-D and the probability of false alarm P-F for each detector must be known, but this information is not always available in practice. An adaptive fusion model which estimates the P-D and P-F adaptively by a simple counting process is presented, Since reference signals are not given, the decision of a local detector is arbitrated by the fused decision of all the other local detectors, Furthermore, the fused results of the other local decisions are classified as ''reliable'' and ''unreliable.'' Only reliable decisions are used to develop the rule, Analysis on classifying the fused decisions in term of reducing the estimation error is given and simulation results which conform to our analysis are presented.
Power systems with a higher share of inverter-based resources (IBR) have less strength and inertia, which are otherwise provided by synchronous generators (SG). In the case of a contingency, lack of strength (measured...
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This paper presents a wide area monitoring and protection technique based on a Learning Vector Quantization (LVQ) neural network. Phasor measurements of the power network buses are monitored continuously by a LVQ netw...
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