Mohd Foad Rohani

Efficient image duplicated region detection model using sequential block clustering

Apart from robustness and accuracy of copy-paste image forgery detection, time complexity also plays an important role to evaluate the performance of the system. In this paper, the focus point is to improve time complexity of the block-matching algorithm. Hence, a coarse-to-fine approach is applied to propose an enhanced duplicated region detection model by using sequential block clustering. Clustering minimizes the search space in block matching. This significantly improves time complexity as it eliminates several extra block-comparing operations. We determine time complexity function of the proposed algorithm to measure the performance. The experimental results and mathematical analysis demonstrate that our proposed algorithm has more improvement in time complexity when the block size is small.

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.

Continuous LoSS detection using iterative window based On SOSS model and MLS approach

This paper proposes a continuous Loss of Self-Similarity (LoSS) detection using iterative window and Multi-Level Sampling (MLS) approach. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. The probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100 ms but exceeds the threshold at sampling levels larger than 100 ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring.

Efficient image block matching algorithm with two layer feature extraction

Image block matching is the main step of duplicated region detection for exploring copy-paste image forgery. High computational time in this step is one of the most important problems to find similar regions. In this paper we propose an efficient image block matching algorithm based on two layer feature extraction in order to improve time complexity. Furthermore, we determine performance of proposed algorithm based on time complexity function. The experimental results and mathematical analysis demonstrate that two layer matching can be more time-efficient than previous common methods such as lexicographically sorting.

MULTI-LEVEL SAMPLING APPROACH FOR CONTINOUS LOSS DETECTION USING ITERATIVE WINDOW AND STATISTICAL MODEL

This paper proposes a Multi-Level Sampling (MLS) approach for continuous Loss of Self-Similarity (LoSS) detection using iterative window. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. Probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, false positive detection can occur where abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100 ms. However, the LoSS probability exceeds the threshold at sampling levels larger than 100 ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring.

Improved skin detection based on dynamic threshold using multi-colour space

Skin colour detection is widely used in applications such as adult image filtering, steganography, content-based image retrieval (CBIR), face tracking, face recognition, and facial surgery. Recently, researchers are more interested in developing high level skin detection strategy for still images based on online sample learning approach which requires no offline training dataset. Previous dynamic skin color detection works has shown high true positive result than the static skin detection in term of skin-like colour and ethnicity factors. However, dynamic skin colour detection also produced high false positives result which lowers the accuracy of skin detection. This is due to the current approach of elliptical mask model that is not flexible for face rotation and is based on single colour space. Therefore, we propose dynamic skin colour detection based on multi-colour space. The result shows the effectiveness of the proposed method by reducing the false positive rate from 19.6069% to 6.9887% and increased the precision rate from 81.27% to 91.49%.

Fairness of Channel Assignment Algorithm Based on Weighted Links Ranking Scheme for Wireless Mesh Network

Wireless Mesh Networks (WMNs) are promising means to provide inexpensive deployment, flexible and fast broadband access. Recent WMNs use multiple-radio and multiple channels to provide high performance. However, interference between channels is considered the key challenge for WMN performance. In WMN, the data flows are directed from/to the gateways. Thus, the quality of the critical links close to the gateways should be properly considered during channel assignments. Otherwise, network fragmentation and bottleneck problem may occur which affect the performance of the network due to congestions, and unfair channel distribution. Unfortunately, the existing channel assignments focusing only on the links close to the gateways and neglecting other critical links. This paper proposes a fair channel assignment algorithm based on weighted link ranking scheme in order to minimize the interference and thus improve the capacity of the network. The links are fairly ranked based on multiple criteria obtained from traffic and network topology such as distance from the gateways, interference index, and, traffic load. The results from numerical simulation demonstrate that the proposed channel assignment algorithm has reduced the interference, improving the network capacity, and achieves the fairness of channel distribution.

RMEER: Reliable Multi-path Energy Efficient Routing Protocol for Underwater Wireless Sensor Network

Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4 . The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique.The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.This paper presents a novel technique for numeral reading in Indian language speech synthesis systems using the rule-based Concatenative speech synthesis technique. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. To analyze the performance of the proposed technique, a set of numerals are considered in different context and a comparison of the proposed technique with an existing numeral reading method is also presented to show the effectiveness of the proposed technique in producing intelligible speech out of the entered text.This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data com- ing directly from Two EUMETSAT Metop satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feed forward ar- tificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco.Advanced Communication Systems are wideband systems to support multiple applications such as audio, video and data so and so forth. These systems require high spectral efficiency and data rates. In addition, they should provide multipath fading and inter-symbol interference (ISI) free transmission. Multiple input multiple output orthogonal frequency division multiplexing (MIMO OFDM) meets these requirements Hence, MIMO-OFDM is the most preferable technique for long term evaluation advanced (LTE-A). The primary objective of this paper is to control bit error rate (BER) by proper channel coding, pilot carriers, adaptive filter channel estimation schemes and space time coding (STC). A combination of any of these schemes results in better BER performance over individual schemes. System performance is analyzed for various digital modulation schemes. In this paper,adaptive filter channel estimated MIMO OFDM system is proposed by integrating channel coding, adaptivefilter channel estimation, digital modulation and space time coding. From the simulation results, channel estimated 2×2 MIMO OFDM system shows superior performance over individual schemes.Electricity markets are different from other markets as electricity generation cannot be easily stored in large amounts and in order to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a large extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks in order to predict short-term (72 hours ahead) wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. Implementing an in-house self-adaptive model should yield good results in the dynamic uncertain Irish energy market. We have identified the features that such a model demands and outline it here.Received May 2, 2018 Revised Jul 9, 2018 Accepted Aug 2, 2018 Zigbee technology has been developed for short range wireless sensor networks and it follows IEEE 802.15.4 standard. For such sensors, several considerations should be taken including; low data rate and less design complexity in order to achieve efficient performance considering to the transceiver systems. This research focuses on implementing a digital transceiver system for Zigbee sensor based on IEEE 802.15.4. The system is implemented using offset quadrature phase shift keying (OQPSK) modulation technique with half sine pulse-shaping method. Direct conversion scheme has been used in the design of Zigbee receiver in order to fulfill the requirements mentioned above. System performance is analyzed considering to BER when it encountered adaptive white Gaussian noise (AWGN), besides showing the effect of using direct sequence spread spectrum (DSSS) technique. Keyword:This paper presents the use of Simelectronics Program for modeling and control of a two degrees-of freedom coupled mass-spring-damper mechanical system.The aims of this paper are to establish a mathematical model that represents the dynamic behaviour of a coupled mass-spring damper system and effectively control the mass position using both Simulink and Simelectronics.The mathematical model is derived based on the augmented Lagrange equation and to simulate the dynamic accurately a PD controller is implemented to compensate for the oscillation sustained by the system as a result of the complex conjugate pair poles near to the imaginary axis.The input force has been subjected to an obstacle to mimic actual challenges and to validate the mathematical model a Simulink and Simelectronics models were developed, consequently, the results of the models were compared. According to the result analysis, the controller tracked the position errors and stabilized the positions to zero within a settling time of 6.5sec and significantly reduced the overshoot by 99.5% and 99. 7% in Simulink and Simelectronics respectively. Furthermore, it is found that Simelectronics model proved to be capable having advantages of simplicity, less time-intense and requires no mathematical model over the Simulink approach.

Energy efficient routing protocols for UWSN: A review

The Underwater Sensor Network (UWSN) is main interesting area due to its most valuable applications like: disaster preventions, distributed tactical surveillance, undersea exploration, seismic monitoring, environmental monitoring and many more. The design of energy efficient routing protocol however is a challenging issue because in underwater environment the batteries of the sensor nodes cannot be recharged easily. Majority of the researchers have adapted the terrestrial WSN methodologies to overcome this problem but in underwater environment the terrestrial WSN approach is not feasible due to the acoustic signaling and water current. This research paper focuses the key limitation of the current energy efficient routing protocols. The simulation results with comparative analysis for energy efficient routing protocols are also presented in this research article; which helps the researchers to find the further research gap in the field of energy efficient routing protocols.

An optimized skin texture model using gray-level co-occurrence matrix

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection.