The developments of wireless networks have directed to search for opportunities of a broad diversity of improved and new networking contributions. Wireless Asynchronous Transfer Mode (ATM) is a non-synchronous or random mode of transferring information. The advantages of circuit switching include dedicated connections and guaranteed traffic parameters and the benefits of packet switching are the efficiency at the physical layer and a more cost-effective design. ATM is the only protocol that offers the best of both communication methods. Although the Variable Bit-Rate (VBR) transmission presents a promising prospective of stable data quality, it is usually accompanied by network traffic overload and cell packet loss, which extensively weakens that potential. This work overcomes these concerns by developing a switching-based multiple access control model to improve the data transmission performance of wireless ATM. Therefore, this work discusses the effectiveness of the developed approach to minimize the cell packet losses and network traffic overload in wireless ATM. Three control access is processed; polling, token passing, and reservation algorithms for collision avoidance. The reservation stage reserves the data before sending, which includes two timeline intervals; a fixed-time reservation period, and variable data transmission interval. Using OPNET 10.5, the results show that the presented switching-based multiple access control model can achieve a throughput value of 98.3 %, data transmission delay of about 40.2 ms, and 0.024 % of packet losses during data transmission between the source and destination. It is demonstrated that the introduced method effectively transmits information without creating any network complexity and delay

Early detection of faults in DC motors extends their life and lowers their power usage. There are a variety of traditional and soft computing techniques for detecting faults in DC motors. Many diagnostic techniques have been developed in the past to detect such fault-related patterns. These methods for detecting the aforementioned potential failures of motors can be utilized in a variety of scientific and technological domains. Motor Power Pattern Analysis (MPPA) is a technology that analyzes the current and voltage provided to an electric motor using particular patterns and protocols to assess the operational status of the motors without disrupting production. Engineers and researchers, particularly in industries, face a difficult challenge in monitoring spinning types of equipment. In this work, we are going to explain how to use the motor power pattern/signature analysis (MPPA) of a power signal driving a servo to find mechanical defects in a gear train. A hardware setup is used to simplify the demonstration of obtaining spectral metrics from the power consumption signals. A DC motor, a set of metal or nylon drive gears, and a control circuit are employed. The speed control circuit was eliminated to allow direct monitoring of the DC motor's current profiles. Infrared (IR) photo-interrupters with a 35 mm diameter, eight-holed, standard servo wheel were employed to gather the tachometer signal at the servo's output. The mean value of the measurements was 318 V for the healthy profile, while it was 330 V for the faulty gears power data. The proposed power consumption profile analysis approach succeeds to recognize the mechanical faults in the gear-box of a DC servomotor via examining the mean level of the power consumption pattern as well as the extraction of the Power Spectral Density (PSD) through comparing faulty and healthy profiles

A numerical study on deformation piezoelectric sensors is described in this study. Major objectives of this research are to compare the impacts of direct current voltage on piezoelectric structure, the effects of direct current voltage on the resonance frequency of piezoelectric knock sensors, and the effects of these parameters on the sensitivity and accuracy of the sensors. The impedance properties of the transient structure are studied under different engine operating conditions and in relation to various forms of sensor damage. Determining the degree of damage sensors and the prediction quality of the piezoelement within the sensor may be accomplished by measuring material flaws and fluctuations in material coefficients that are connected to the frequency characteristic of the sensor. To some extent, the preceding can be used in the calculations of several structural parts of knock sensors. On a prototype knock sensor, ranges of modes were tested using piezoelectric elements with varying numbers of cracks. In this work, it has discussed seven scenarios of frequency analysis to examine the piezoelectric in engine knock sensor with different electricity modes of operation. These scenarios include the engine normal operation mode, start engine operation mode, and different frequency of operation mode (2Hz, 200Hz, 2KHz, 20KHz, 200KHz).