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Chemical sensor based on a solid-core photonic crystal fiber interferometer
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Photonic crystal fiber interferometers are used in many sensing applications. In this work, an in-reflection photonic crystal fiber (PCF) based on Mach-Zehnder (micro-holes collapsing) (MZ) interferometer, which exhibits high sensitivity to different volatile organic compounds (VOCs), without the needing of any permeable material. The interferometer is robust, compact, and consists of a stub photonic crystal fiber of large-mode area, photonic crystal fiber spliced to standard single mode fiber (SMF) (corning-28), this splicing occurs with optimized splice loss 0.19 dB In the splice regions the voids of the holey fiber are completely collapsed, which allows the excitation and recombination of core and cladding modes. The device reflection spectrum exhibits a sinusoidal interference pattern which shifts differently when the voids of the PCF are infiltrated with VOC molecules. The volume of voids responsible for the shift is less than 5microliters whereas the detectable levels are in the nanomole range. Laser diode with a wavelength 1550nm has been used as a pump light source. Two types of chemical liquids used (N-Hexane, and Propanol). The detection limits of our device associated with the maximum shifts of the wavelength is 4.4 nm for N-Hexane vapor when the length of the head sensor 20mm. In this work, the maximum sensitivity obtained of volatile organic compounds is 15420 nm/mol at the vapor of N-Hexane.

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Publication Date
Sun Sep 03 2023
Journal Name
Iraqi Journal Of Computers, Communications, Control & Systems Engineering (ijccce)
Efficient Iris Image Recognition System Based on Machine Learning Approach
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HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

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Publication Date
Mon Aug 01 2022
Journal Name
Mathematics
Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments
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Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima

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Publication Date
Thu Oct 01 2015
Journal Name
Journal Of Engineering
Development of Spatial Data Infrastructure based on Free Data Integration
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In recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of how the

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Publication Date
Tue Jan 01 2013
Journal Name
International Journal Of Application Or Innovation In Engineering & Management (ijaiem)
Probabilistic Neural Network for User Authentication Based on Keystroke Dynamics
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Computer systems and networks are increasingly used for many types of applications; as a result the security threats to computers and networks have also increased significantly. Traditionally, password user authentication is widely used to authenticate legitimate user, but this method has many loopholes such as password sharing, brute force attack, dictionary attack and more. The aim of this paper is to improve the password authentication method using Probabilistic Neural Networks (PNNs) with three types of distance include Euclidean Distance, Manhattan Distance and Euclidean Squared Distance and four features of keystroke dynamics including Dwell Time (DT), Flight Time (FT), mixture of (DT) and (FT), and finally Up-Up Time (UUT). The resul

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Publication Date
Tue Aug 24 2021
Journal Name
Conference: The 5th International Multi-conference On Artificial Intelligence Technology (mcait 2021).
Text Encryption Based on DNA Cryptography, RNA, and Amino Acid
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To achieve safe security to transfer data from the sender to receiver, cryptography is one way that is used for such purposes. However, to increase the level of data security, DNA as a new term was introduced to cryptography. The DNA can be easily used to store and transfer the data, and it becomes an effective procedure for such aims and used to implement the computation. A new cryptography system is proposed, consisting of two phases: the encryption phase and the decryption phase. The encryption phase includes six steps, starting by converting plaintext to their equivalent ASCII values and converting them to binary values. After that, the binary values are converted to DNA characters and then converted to their equivalent complementary DN

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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
Image Steganography using Dynamic Threshold based on Discrete Cosine Transform
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The art of preventing the detection of hidden information messages is the way that steganography work. Several algorithms have been proposed for steganographic techniques. A major portion of these algorithms is specified for image steganography because the image has a high level of redundancy. This paper proposed an image steganography technique using a dynamic threshold produced by the discrete cosine coefficient. After dividing the green and blue channel of the cover image into 1*3-pixel blocks, check if any bits of green channel block less or equal to threshold then start to store the secret bits in blue channel block, and to increase the security not all bits in the chosen block used to store the secret bits. Firstly, store in the cente

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Publication Date
Tue Mar 12 2019
Journal Name
Al-khwarizmi Engineering Journal
Optimization Drilling Parameters of Aluminum Alloy Based on Taguchi Method
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This paper focuses on the optimization of drilling parameters by utilizing “Taguchi method” to obtain the minimum surface roughness. Nine drilling experiments were performed on Al 5050 alloy using high speed steel twist drills. Three drilling parameters (feed rates, cutting speeds, and cutting tools) were used as control factors, and L9 (33) “orthogonal array” was specified for the experimental trials. Signal to Noise (S/N) Ratio and “Analysis of Variance” (ANOVA) were utilized to set the optimum control factors which minimized the surface roughness. The results were tested with the aid of statistical software package MINITAB-17. After the experimental trails, the tool diameter was found as the most important facto

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Hiding Techniques for Dynamic Encryption Text based on Corner Point
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Hiding technique for dynamic encryption text using encoding table and symmetric encryption method (AES algorithm) is presented in this paper. The encoding table is generated dynamically from MSB of the cover image points that used as the first phase of encryption. The Harris corner point algorithm is applied on cover image to generate the corner points which are used to generate dynamic AES key to second phase of text encryption. The embedded process in the LSB for the image pixels except the Harris corner points for more robust. Experimental results have demonstrated that the proposed scheme have embedding quality, error-free text recovery, and high value in PSNR.

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series
Modified PRESENT Encryption algorithm based on new 5D Chaotic system
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Cryptography is a major concern in communication systems. IoE technology is a new trend of smart systems based on various constrained devices. Lightweight cryptographic algorithms are mainly solved the most security concern of constrained devices and IoE systems. On the other hand, most lightweight algorithms are suffering from the trade-off between complexity and performance. Moreover, the strength of the cryptosystems, including the speed of the algorithm and the complexity of the system against the cryptanalysis. A chaotic system is based on nonlinear dynamic equations that are sensitive to initial conditions and produce high randomness which is a good choice for cryptosystems. In this work, we proposed a new five-dimensional of a chaoti

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