Pattern matching has been used extensively for many machine vision applications such as optical character recognition, face detection, object detection, and defect detection. The normalized cross correlation (NCC) is the most commonly used technique in pattern matching. However, it is computationally intensive, sensitive to environmental changes such as lighting and shifting, and suffers from f...

BACKGROUND Accurate diagnosis of myocardial infarction (MI) is of paramount importance in patient management, which necessitates the development of efficient and accurate diagnostic methods. Q wave is not present in all patients with MI, and its prevalence is declining. Recently, fragmented QRS (fQRS) complex has been introduced as a marker of prior MI. AIM To investigate diagnostic value of ...

This paper presents a version of simple type theory called Q uqe 0 that is based on Q0, the elegant formulation of Church’s type theory created and extensively studied by Peter B. Andrews. Q uqe 0 directly formalizes the traditional approach to undefinedness in which undefined expressions are treated as legitimate, nondenoting expressions that can be components of meaningful statements. Q uqe 0...

Fractional differential calculus have recently been addressed by many researchers of various fields of science and engineering such as physics, chemistry, biology, economics, control theory, and biophysics, etc. [1-4]. In particular, the existence of solutions to fractional boundary value problems is under strong research recently, see [5-7] and references therein. The fractional q-difference c...

Hypertension is one of the major risk factors of stroke and vascular dementia (VaD). We used stroke prone spontaneous hypertensive rats (SPSHRs) as a model for neuronal degeneration frequently occurring in humans with vascular disease. Recently, high b value q-space diffusion-weighted imaging (DWI) was shown to be very sensitive to the pathophysiological state of the white matter. We studied th...

Reinforcement learning is an efficient method for solving Markov Decision Processes that an agent improves its performance by using scalar reward values with higher capability of reactive and adaptive behaviors. Q-learning is a representative reinforcement learning method which is guaranteed to obtain an optimal policy but needs numerous trials to achieve it. k-Certainty Exploration Learning Sy...

Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal policies in Markov decision processes. However, Q-learning shows artifacts in non-stationary environments, e.g., the probability of playing the optimal action may decrease if Q-values deviate significantly from the true values, a situation that may arise in the initial phase as well as after change...

The interconnected nature of the different components of smart grids is a prime example of complexity in technological systems. Developing such systems is highly dependent on the wishes and needs of end-users and other stakeholders. We argue that stakeholder values should be taken into consideration during the design and standardisation of complex infrastructures, and illustrate this with a cas...