arXiv subjects

Ahmet Orun

Publications and source records attributed to Ahmet Orun.

9 recordsLinked to original sources

arXiv2024

An accurate detection of micro-collapse during the lyophilisation of a 5% w/v lactose solution using a combination of novel techniques: intelligent laser speckle imaging (ILSI) and through-vial impedance spectroscopy (TVIS)

Context: In a freeze drying (FD) process, an accurate observation and control of the process parameters at critical stages are at high importance. Particularly accurate and timely identification of the critical temperature (TC) at the end of primary drying phase would lead to heating energy cost reduction and product waste elimination by ending the process before the product's micro-collapse stage. Aim: Within this work, a combination of novel techniques optical technique called Intelligent Laser Speckle Imaging (ILSI) in association with product image texture analysis, coupled to an electrical impedance technique called through vial impedance spectroscopy (TVIS) has been applied onto the real-time product states observation process to identify the specific structural product surface/subsurface characteristics and hence the micro-collapse stage. Method: 2 cycles - one providing a profile for standard approach (non-collapsed) and another with a temperature ramp through TC to micro-collapse, TVIS provides an assessment of the onset of micro-collapse through the assessment of the acceleration in drying rate whereas the ILSI with pattern recognition detects the change in microstructure

eess.IV↗

arXiv2023

The lower energy consumption in cryptocurrency mining processes by SHA-256 Quantum circuit design used in hybrid computing domains

Cryptocurrency mining processes always lead to a high energy consumption at considerably high production cost, which is nearly one-third of cryptocurrency (e.g. Bitcoin) price itself. As the core of mining process is based on SHA-256 cryptographic hashing function, by using the alternative quantum computers, hybrid quantum computers or more larger quantum computing devices like quantum annealers, it would be possible to reduce the mining energy consumption with a quantum hardware's low-energy-operation characteristics. Within this work we demonstrated the use of optimized quantum mining facilities which would replace the classical SHA-256 and high energy consuming classical hardware in near future.

cs.ET↗

arXiv2023

Recognition of Cyber-Intrusion patterns in user cognitive behavioural characteristics for remote identification

Cyber-attacks keep threatening global networks and information infrastructures. The threat is getting more and more destructive and hard to counter day by day as the global networks continue to enlarge exponentially with limited security counter-measures. As this fact requires more sophisticated methods and techniques in urgency, a multidisciplinary remote cognitive observation technique is proposed in this paper to meet today's cybersecurity needs. The proposed method introduces a non-traditional Cognitive Psychology and Artificial Intelligence (AI) based remote threat identification which can be considered during the cyber security system design. It also enables to access the cognitive behavioural parameters of an intruder/hacker remotely without any physical contact via online connection, disregarding the distance of the thread. The ultimate goal of this work is to develop a supplementary cognitive cyber security tool for next generations secure online banking, finance or trade systems.

cs.HC↗

arXiv2022

Continuous monitoring of plant sub-cellular structural changes for plant and crop diseases detection by use of Intelligent Laser Speckle Classification (AI) technique

The continuous online monitoring of early signs of plant and crop diseases, at their early stages before a potential spread, is of high importance and necessitates multi-disciplinary techniques. Within this study a proposed technique achieves this goal by exploiting laser physics, textural image analysis, and AI for Shot hole disease. In this technique, specific laser light with a wavelength shorter than a sub-cellular component of an inspected plant, produces an interaction within the sub-cellular components and generates laser speckle patterns which can characterize those specific plant cells' features. The generated laser speckle image data then be quantized by texture analysis and classified by Bayesian networks. Such comparative methods manage to detect the differences at sub-cellular scales, such as nuclei modification, cellular shape, or size deformation, etc. for Shot hole disease with high classification accuracy between the healthy and diseased plants. The technique is capable of continuous online observation and monitoring of the plant or crop diseases via a wireless network at low instrumental cost and may replace the costly ground-truth field works

eess.IV↗

arXiv2022

Knowledge acquisition via interactive Distributed Cognitive skill Modules

The human's cognitive capacity for problem solving is always limited to his/her educational background, skills, experiences, etc. Hence, it is often insufficient to bring solution to extraordinary problems especially when there is a time restriction. Nowadays this sort of personal cognitive limitations are overcome at some extend by the computational utilities (e.g. program packages, internet, etc.) where each one provides a specific background skill to the individual to solve a particular problem. Nevertheless these models are all based on already available conventional tools or knowledge and unable to solve spontaneous unique problems, except human's procedural cognitive skills. But unfortunately such low-level skills can not be modelled and stored in a conventional way like classical models and knowledge. This work aims to introduce an early stage of a modular approach to procedural skill acquisition and storage via distributed cognitive skill modules which provide unique opportunity to extend the limits of its exploitation.

cs.AI↗

arXiv2022

Automatic Real-time Vehicle Classification by Image Colour Component Based Template Matching

Selection of appropriate template matching algorithms to run effectively on real-time low-cost systems is always major issue. This is due to unpredictable changes in image scene which often necessitate more sophisticated real-time algorithms to retain image consistency. Inefficiency of low cost auxiliary hardware and time limitations are the major constraints in using these sorts of algorithms. The real-time system introduced here copes with these problems utilising a fast running template matching algorithm, which makes use of best colour band selection. The system uses fast running real-time algorithms to achieve template matching and vehicle classification at about 4 frames /sec. on low-cost hardware. The colour image sequences have been taken by a fixed CCTV camera overlooking a busy multi-lane road

cs.CV↗

arXiv2021

Supervised laser-speckle image sampling of skin tissue to detect very early stage of diabetes by its effects on skin subcellular properties

This paper investigates the effectiveness of an expert system based on K-nearest neighbors algorithm for laser speckle image sampling applied to the early detection of diabetes. With the latest developments in artificial intelligent guided laser speckle imaging technologies, it may be possible to optimise laser parameters, such as wavelength, energy level and image texture measures in association with a suitable AI technique to interact effectively with the subcellular properties of a skin tissue to detect early signs of diabetes. The new approach is potentially more effective than the classical skin glucose level observation because of its optimised combination of laser physics and AI techniques, and additionally, it allows non-expert individuals to perform more frequent skin tissue tests for an early detection of diabetes.

eess.IV↗

arXiv2021

Progressive observation of Covid-19 vaccination effects on skin-cellular structures by use of Intelligent Laser Speckle Classification (ILSC)

We have made a progressive observation of Covid-19 Astra Zeneca Vaccination effect on Skin cellular network and properties by use of well established Intelligent Laser Speckle Classification (ILSC) image based technique and managed to distinguish between three different subjects groups via their laser speckle skin image samplings such as early-vaccinated, late-vaccinated and non-vaccinated individuals. The results have proven that the ILSC technique in association with the optimised Bayesian network is capable of classifying skin changes of vaccinated and non-vaccinated individuals and also of detecting progressive development made on skin cellular properties for a month period.

eess.IV↗

arXiv2021

Low-level cognitive skill transfer between two individuals' minds via computer game-based framework

The novel technique introduced here aims to accomplish the first stage of transferring low-level cognitive skills between two individuals (e.g. from expert to learner) to ease the consecutive higher level declarative learning process for the target "learner" individual in a game environment. Such low-level cognitive skill is associated with the procedural knowledge and established at low-level of mind which can be unveiled and transferred by only a novel technique (rather than by a traditional educational environment ) like a highly interactive computer game domain in which a user exposes his/her unconscious mind behaviors via the game-hero non-deliberately during the game sessions. The cognitive data exposed by the game-hero would be recorded, and then be modelled by the artificial intelligence technique like Bayesian networks for an early stage of cognitive skill transfer and the cognitive stimuli are also generated to be used as game agents to train the learner.

cs.HC↗