فصلنامه روشهای هوشمند در صنعت برق
پیاپی 47 (پاییز 1400)

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological and psychiatric disorder which causes to attention deficit, anxiety, hyperactivity and impulsive behaviors. ADHD is more common in children and directly leads to their learning disability. The aim of this study was to accurately identify ADHD patients by using wavelet-based features of brain signals (EEG). Recorded EEG signals from 61 children with ADHD (diagnosed according to the DSM-IV criteria) and 60 healthy controls in the age range of 7-12 years were used to design the system. In the proposed method by applying wavelet transform, EEG signals were decomposed into subbands; for the time version of the signals in each subband, the temporal and statistical features were calculated. The reduced feature set by principal component analysis (PCA) method was then used to train the classification unit to identify ADHD patients from healthy individuals. To obtain the desired results, different types of wavelet functions and decomposition levels were investigated. The bior3.1 wavelet function with the support vector machine (SVM) classifier and the rbio1.1 wavelet function with the k-nearest neighbor (kNN) classifier presented the best performance with the recognition accuracy of 98.33% and 99.17%, respectively. The SVM classification method with radial basis kernel function (RBF) and the kNN method with the number of nearest neighbors, k = 3 obtained the best results.The results obtained in this study compared to the results reported in previous studies showed at least a 2% improvement in the recognition accuracy of ADHD patients.

In this paper, we present the new positioning algorithm of ad-hoc network nodes based on the exchange of radio signals among nodes. The proposed algorithm is independent of GPS data. In conventional positioning methods, the location of nodes in a network is estimated in two steps. In the first step, each node calculates its local location and neighbors based on the Multi-dimensional scaling (MDS) method. In the second step, the local location of the nodes transforms into a unique global location. In these methods, the location of the positioned nodes is used as a rotation reference for the next nodes though this calculated location is not accurate.  Also, the Dijkstra method is not exact either and produces errors which transmitted to the next nodes. Therefore, the positioning error is very high in the final nodes positioned. In the proposed algorithm, using the optimal algorithm, the estimated locations in the conventional method use as initial estimates, and these errors reduce so that the whole network reaches a stable state. The simulation results show that the proposed algorithm can solve the error propagation problem compared to the MDS algorithm and achieved the appropriate accuracy and stability with the least increase of overhead and low computational complexity load by mobility of the existing nodes.

With the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks are one of the best options for collecting data from the environment due to their easy configuration and no need for expensive equipment. The energy of sensors in wireless sensor networks is limited, which is a major challenge due to the lack of a fixed charge source. Because most of the sensors' energy is wasted during data transmission, a sensor that transmits more data than others and transmits data over long distances with packets will run out of energy sooner than others. When a sensor in the network runs out of energy, the network process may be disrupted. Therefore, due to the dynamic topology and distributed nature of wireless sensor networks, designing energy efficient routing protocols is one of the main challenges. Therefore, in this article, energy-aware routing protocol based on multi-objective particle swarm optimization algorithm is presented. In the proposed approach, the fitness function of the particle swarm optimization algorithm for selecting the optimal cluster head based on quality-of-service goals including residual energy, link quality, end-to-end delay and delivery rate. The simulation results show that the proposed approach has less energy consuming and extend network lifetime due to balancing the goals of quality-of-service criteria than other approaches.

In the few past years, Solar cells based on Cu2ZnSnS4 (CZTS) are very promising thin-film solar cells due to their appropriate absorption coefficient and optical band gap, low-cost, non-radioactive and environmental friendly behavior. However, CZTS devices show poor efficiency and identifying deficiencies and making improvements is necessary. In the present study, various anti-reflection coatings at the top surface of the solar cell were proposed. Minimization of the reflectance is carried out to optimize the thickness of ARC layers using Lumerical software. The density of the short-circuit photocurrent increases from 18.4 mA.cm−2 for solar cells without an antireflection coating to 36 mA.cm−2 for those with MgF2 layer coating.

This paper constructs a new complex hyper-chaotic system with attractive coexisting dynamic behaviors. We analyze the hyper-chaotic attractors, equilibrium points, Poincaré maps, Kaplan-York dimension, and Lyapunov exponent behaviors. The characteristics of hyper-chaotic systems include higher complexity, higher parametric resistance and sensitivity to very small changes in initial conditions. We prove that the introduced hyper–chaotic system is much more complex than the similar hyper-chaotic systems, that can suitable for use in encryption and secure communication. Next, the work describes a fast terminal sliding mode controller scheme for the fast synchronization and stability of the new complex hyper–chaotic system. It is shown that by applying uncertainty to the system, both steps of the sliding mode control have finite-time convergence properties. Next, a comparison will be made between a newly designed controller and a similar. Finally, using the MATLAB simulation, the results are confirmed for the new system. The results shown that the new hyper-chaotic system with many adsorbents is much more complex than similar systems, and the proposed controller has a faster convergence response than the similar controller.

Memristor, as a fundamental element of SRAM and DRAM memories, can effectively reduce startup time and power consumption of the circuits. Non-volatility, high density of the final circuit, and reduction of power delay product (PDP) are some of the significant facts of memristor circuits, which has led to the suggestion of a memory cell including and four transistors and two memristors (4T2M) in this paper. In order to simulate the proposed memory cell, the length of memristors has been selected 10 nm, and their on/off state resistors have been selected 250 Ω and 10 KΩ respectively. In addition, the proposed memory cell MOS transistors are simulated by the 32 nm CMOS PTM model. Simulation in the HSPICE software with 1V supply voltage and comparison with two conventional six-transistor (6T) and two transistors-two memory (2T2M) cells show that the use of memristors has made the proposed memory cell and 2T2M cell non-volatile. Moreover, the power consumption of the proposed circuit has decreased by 99.8% and 57.2%, compared to the previous two circuits respectively, and the power average delay product has also improved by 99.4% and 26.7%, respectively; however, the writing delay of this cell and 2T2M cell increased by 400% and 218% compared to 6T cell, respectively.