Scientia Iranica
Volume:29 Issue: 4, Jul-Aug 2022

Computer vision based traffic sign detection and recognition is an active field of research but the task becomes challenging when the sign of interest is partially occluded by nearby objects like a tree, pole or vehicle. Another difficulty posed especially in the developing countries is the lost colors problem that arises due to aging and poor maintenance. This work presents an automatic technique that focuses on visible parts only and suppresses occluded portions. Features are collected using a convolutional neural network inspired invariant feature extraction technique augmented with feature interaction based dimensionality reduction. Further, with the use of dynamic parameter estimation, an adaptive system for continuous learning is also proposed. Since the effect of partial occlusion has not been thoroughly studied, there is no benchmark database available for this purpose. We have prepared two datasets by combining originally and synthetically occluded images taken from field surveys and from famous GTSRB database. Experiments revealed that our technique outperforms state of the art recognition methods previously used for visible and occluded signs by obtaining 0.81 precision and 0.79 recall values on the average. The proposed method also shows a remarkably low error rate as the amount of occlusion is increased.

Sleep stages Classification is a useful way to diagnose sleep problems. This is based on the processing of bio-signals (ECG, EEG, EOG, PPG). The less complex this signal is, the better the detection and processing. Feature extraction methods using hand are tedious and long lasting. Extraction of features without hand intervention are deep features, which are usually extracted from images. Analysis of time-frequency characteristics of non-static bio-signals is very important and has useful information. In this study, time-frequency image was extracted using ECG signal spectrogram and deep features were extracted using convolutional neural network. After extracting deep features, sleep stages were classified using deep transfer learning method. Network training was performed using one of the ECG signal and testing was performed with the other ECG signal channel.The results show that it is possible to detect sleep stages with acceptable accuracy with different amplitudes of signals. Sleep stages were detected with 98.92% accuracy and 96.52% sensitivity.

An approach is proposed to construct fuzzy confidence intervals for unknown parameters in statistical models. In this approach, a family of confidence intervals of the unknown crisp parameter has been considered. Such confidence intervals are used to obtain a fuzzy confidence interval for the parameter of interest. ‎The proposed approach benefits a wide range of confidence intervals to obtain a trapezoidal shaped fuzzy set of the parameter space as the fuzzy confidence interval for the parameter of interest. By using the resolution identity, it is shown that the constructed fuzzy confidence intervals are really fuzzy sets of the parameter space.Some numerical examples are provided to explain the approach in one-sided and two-sided fuzzy confidence intervals. ‎‎Moreover, ‎an‎‎‎ application in health sciences ‎‎‎is provided about the ‎‎‎‎recovery time of olfactory and gustatory dysfunctions for COVID-19 patients.

Researchers on antenna arrays usually neglect the effect of mutual coupling of antennas placed in proximity to each other. The interchange of electromagnetic energy happening between an antenna and a far field point depends not only on the transmitting antenna, but also from its neighbouring antennas. This effect is referred to as mutual coupling between dipole antenna elements and it is considered here in the synthesis of phase only reconfigurable antenna arrays. The main objective of this work is to produce desired Side Lobe Level and Voltage Standing Wave Ratio in addition to few other radiation pattern parameters. Multiverse Optimization algorithm is employed for the purpose of generating voltage amplitude and discrete phase distributions in the dipole elements for the generation of flat-top beam/pencil beam patterns. These two patterns share the common amplitude distributions and differ in phase distributions. Results obtained using MATLAB simulations prove that this algorithm accomplished its task successfully and is also found to be superior over other algorithms like Particle Swarm Optimization, Grey Wolf Optimization and Imperialist Competitive Optimization algorithms.

A novel traveling-wave (TW)-based protection algorithm for power transmission lines using intelligent systems is proposed in this paper. The first part of the algorithm identifies internal faults from external ones and the other part is used for fault type classification and faulted phases selection. In order to extract TW signals, Teager energy operator (TEO) is used. Then hidden Markov model (HMM) is utilized to identify internal faults from external faults according to the output of TEO. Fault type classification and faulted phases selection are other important tasks in protection algorithms. In this paper, a very accurate and robust classification algorithm based on fuzzy systems is presented. This algorithm uses different ratios of modal components of the faulted current signal as the input variable of fuzzy systems. The test system is simulated in PSCAD software and the algorithm is implemented in MATLAB. Testing the proposed algorithm with a large number of test signals in different fault conditions shows the robustness of both internal fault identification and fault type classification algorithms.

Two different configurations have been considered for the optimal design of radiated electric field pattern characteristics of antenna arrays. Time-modulated concentric circular antenna array (TMCCAA) and time-modulated half symmetric circular antenna array (TMHSCAA) have been dealt with for the comparative analysis of control parameters of the radiation properties of the antenna array. Collective animal behaviour (CAB), an evolutionary approach, is applied as the optimisation tool. The effect of different control parameters of the radiation pattern of TMCCAA is compared by taking two different cases. In the first case, i.e., Case-1, only one parameter (switching time sequence) is used as the control parameter for the optimisation of the radiated electric field pattern of TMCCAA. In the second case, i.e., for Case-2, three control parameters, switching ON-OFF time of each ring, inter-element spacing, and ring radii are optimised.In the second array, the effect of different control parameters of the radiation pattern of Time-Modulated Half Symmetric Circular Antenna Array (TMHSCAA) is compared by taking two different cases. Simulation results obtained for 20- elements TMHSCAA with uniform amplitude and spacing are analysed. Optimized SLL values obtained for the Case-1 and Case-2 of TMCCAA are -24.68 dB and -46.15 dB, respectively.

While it has been known for a long time that the cache behavior is a powerful source of the information leakage, more realistic attack scenarios have received a lot of attention by the cryptographic community. To develop practical cache-based attacks, there is an increasingly need to automate the process of finding exploitable cache-based side-channels in computer systems. Cache template attack is a generic technique that utilizes Flush+Reload attack in order to automatically exploit cache vulnerability of Intel platforms. Cache template attack on T-table-based AES implementation consists of two phases including the profiling phase and the key exploitation phase.Profiling is a preprocessing phase to monitor dependencies between the secret key and behavior of the cache memory. In addition, the addresses of T-tables can be obtained automatically.In the key exploitation phase, most significant bits (MSBs) of the secret key bytes are retrieved by monitoring exploitable addresses. In this paper, we propose a simple yet effective searching technique which accelerates the profiling phase by a factor of at most 64. To verify the theoretical model of our technique, we implement the described attack on AES. The experimental results confirmed a shorter runtime of the attack in comparison to the original attack.

Ocean thermal energy conversion system uses from water in surface of the ocean as high temperature source and water in the depth of the ocean as low temperature source. Three types of ocean thermal energy conversion systems including close cycle, open cycle and hybrid systems are available. In a close cycle system, working fluid through a thermodynamic cycle based on the Rankine cycle can rotate the turbine and generate electricity. Due to the variation in the ocean surface temperature, the output power of the ocean thermal energy conversion system is not fixed and controllable and so this uncertainty nature results in the numerous states in the generated power of this plant. Thus, in integrating ocean thermal energy conversion systems to the power system, many aspects of power system such as reliability may be affected and so new approaches must be developed for investigation these effects. In this regard, in this paper for the first time, the reliability of power system containing ocean thermal energy conversion system is evaluated and the valuable indices such as loss of load expectation, expected energy not supplied and peak load carrying capability that can be used for generation expansion planning of power system, are calculated.

This paper presents an analytical study of the radial-flux slotless limited-angle torque motors. The modelling is based on the magnetic equivalent circuit of the actuators and uses the electromagnetic equations to calculate the air-gap flux as well as the produced torque. This model is then used for designing the actuators both in outer-rotor and inner-rotor structures, considering the design constraints and desired characteristics. As the objectives in design stage usually conflict with each other, an intelligent multi-objective optimization algorithm is required to design the best fit actuators. Analytical and simulation results are presented and compared to show the accuracy of the model and verify the design equations as well as the design approach. As this type of actuators is a key element in industrial control, the contributions of this paper are focused on new analytical field solution based on magnetic equivalent circuit, design and optimization approach for radial-flux structure and introducing a general procedure that could be extended to similar structures and actuators.

Increases in tightening the correlation of gas and electricity systems (G&ES), affected by diverse factors, ranging from anthropogenic climate change to the advent of new conversion/generation technologies, have remarkably brought the co-expansion of G&ES using a new concept, the so-called Energy Hub (EH), as well as the potential of storage systems into focus. To assess the effectiveness of EH approach and the role of storage systems in the coordinated plans of G&ES, this paper proposes a comprehensive EH-based planning model for co-expansion of G&ES supply chains with respect to the role of gas storage systems (GSSs). As a mixed-integer linear programming (MILP) problem, the model is applied to a real large-scale case study, i.e. the Iranian G&ES and is solved via GAMS package. The simulation results reveal that incorporation of the interactions existing between G&ES into their planning problems in the framework of an EH can reach more flexible, realistic and optimal expansion plans compared with their traditional integrated expansion planning methods. Furthermore, findings show that the involvement capacities of GSSs provides the opportunity of optimal matching of demand with supply by increasing the productivity of the gas pipelines, allowing technically and economically sensible long-term management of gas supply systems.

Resolver, as an electromagnetic sensor, is widely used in many industrial applications. It can detect the position of the rotary part of the electric machines precisely. In commercial resolvers, the excitation winding is connected to the high frequency (HF) AC source. The amplitude-modulated voltages induced in the signal windings need to be demodulated in order to calculate the envelope of the output signals and accordingly detect the position. On the other hand, in PM-resolver, signal windings replaced by Hall-effect sensors to measure the DC magnetic flux which is produced by permanent magnets. In this study, the performance of both AC and DC flux resolvers is investigated under different circumstances. All the simulations are done by the time-stepping finite element method (TSFEM).

Most adult Cystic Fibrosis (CF) patients frequently suffer from Pseudomonas aeruginosa (PA) infection, which is strongly associated with inflammation, lung destruction, and increased mortality. Diagnosis of PA infection in the primary stage is essential to initiate the treatment and reduce the risk of chronic infection. Sputum culture is the gold standard ‎for infection detection, but it is time-consuming. The objective of this study was to suggest and examine a method to decide about PA infection status in CF patients based only on their respiratory sound. Respiratory sounds were recorded from 36 CF patients. Some features which were generated from Tunable Q-factor wavelet transform(TQWT) components, were investigated. The features were fed into Support Vector Machine and also Ensemble classifier. The proposed method achieved an accuracy of 90.3% in identifying PA infection in CF patients. Furthermore, the probability of categorizing respiratory sounds as PA CF decreased significantly after the treatment of PA infection(P-value<0.003). Moreover, the method had a satisfactory performance in the presence of noises and artifacts. The developed method represents a novel approach to the diagnosis of PA infection in CF patients based only on respiratory sound signals, which is a necessary and innovative approach for early diagnosis of PA infection.

High penetration of Power Electronic (PE) converters in DC power grids has caused new stability challenges due to dynamic interactions among a network’s subsystems. Dynamic interactions can be avoided by the impedance coordination between the subsystems through the modification of control loops or passive elements inside a grid. Impedance coordination is a very complex and time-consuming task with no adaptations to dynamic changes in a power grid. In this paper, the concepts of dynamic interaction and passivity are explained and combined together to provide an online stability measure in terms of the DC bus impedance characteristics. A novel DC Power System Stabilizer (PSS) is proposed which is connected to a DC bus as a separate module passivizing the bus impedance at non-passive interaction frequencies. The interaction frequencies are detected through a broadband online identification process. The PSS working principle, topology, modeling, and control designs are explained in detail. Finally, the functionality and performance of the proposed stabilizer are validated by simulation results.