No abstract

Edge detection is the process of determining where boundaries of objects fall within an image. So far, several standard operators-based methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard operators-based edge detection methods. The proposed preprocessing approach involves computation of the histogram, finding out the total number of peaks and suppressing irrelevant peaks. From the intensity values corresponding to relevant peaks, threshold values are obtained. From these threshold values, optimal multilevel thresholds are calculated using the Otsu method, then multilevel image segmentation is carried out. Finally, a standard edge detection method can be applied to the resultant segmented image. Simulation results are presented to show that our preprocessed approach when used with a standard edge detection method enhances its performance. It has been also shown that applying wavelet edge detection method to the segmented images, generated through our preprocessing approach, yields the superior performance among other standard edge detection methods.

No abstract

No abstract

No abstract

The intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all network administrators. Several existing methods have been proposed for early intrusion detection systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced long-short term memory (ELSTM) technique with recurrent neural network (RNN) (ELSTM-RNN) to enhance security in IDS. Intrusion detection technology has been associated with various problems, such as gradient vanishing, generalization, and overfitting issues. The proposed system solves the gradient-clipping issue using the likely point particle swarm optimization (LPPSO) and enhanced LSTM classification. The proposed method was evaluated using the NSL-KDD dataset (KDD TEST PLUS and KDD TEST21) for validation and testing. Many efficient features were selected using an enhanced technique, namely, the particle swarm optimization. The selected features serve for effective classification using an enhanced LSTM framework, where it is used to efficiently classify and detect the attack data from the normal data. The proposed system has been applied to the UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, and BOT _DATASET datasets for further verification. Results show that the training time of the proposed system is much less than that of other methods for different classes. Finally, the performance of the proposed ELSTM-RNN framework is analyzed using various metrics, such as accuracy, precision, recall, and error rate. Our proposed method outperformed LPBoost and DNNs methods.

The power system must be kept safe during load disturbances in order to control frequency instability during load disturbances change. Cascade Controller (CC) is employed to boost the performance of the power system mainly in the presence of nonlinear aspects. As a result, in this study, A proposed cascade fractional order proportional-derivative proportional integral (FOPDPI) controller is used to fine-tune the load frequency control (LFC) subjects of a three-area power system (thermal-thermal-wind) in the interconnected power system (IPS). As a third area in the studied model, renewable energy is used, such as high penetrating power wind turbines. The FOPDPI controller gains are adjusted using a recently published optimization scheme, such as the Harris hawk optimizer (HHO). To thoroughly test the efficiency and fitness of the proposed controller, the HHO-based FOPDPI and conventional PID controllers are applied to a threearea model with/without nonlinearities such as generation rate constraint (GRC), governor dead band (GDB), and boiler dynamics (BD) under different step load perturbation (SLP). The HHO algorithm's cost function is the Integral time multiply absolute error (ITAE) criterion. The investigation reveals that the proposed scheme HHO: FOPDPI provides greater stability than HHO: PID in both linearities by 58% and nonlinearity aspects by 62%.

This paper suggestsproposes a high-gain reconfigurable polarization antenna using a metasurface polarizaer. The metasurface polarizer is a rectangular array that consists of similar 25 unit-cell elements. Each metamaterial (MM) unit-cell element consists of a circular copper patch attached to two copper arrow-shaped strips installed at its circumference. The circular patch and two arrows are installed between a rectangular superstrate at the top and a rectangular substrate at the bottom, which is backed with a perfect electric conductor with a relative permittivity of εsub = 3.38. The MM characteristics are obtained in a wide range of frequencies from 1.4 to 2.1 THz. The metasurface polarizer array is installed at an optimized height of 25 μm under a linear polarized dipole antenna that operates at 1.81 THz with a bandwidth (BW) of 0.2 THz from 1.75 to 1.95 THz (11.05%, − 10 dB BW) and gain of 2.27 dBi. The incident-plane wave from the antenna can be converted into a reconfigurable left- or right-hand circular polarization according to the directions of the arrow of the MM unit-cell element. Moreover, the operating − 10-dB BW of the dipole antenna increases to 30.93%, and the gain is enhanced to 6.18 dBi at the same operating frequency. A reconfigurable polarization conversion for the dipole antenna can be obtained over wide 3-dB axial ratio BW from 1.45 to 1.95 THz (33.3% BW).