فهرست مطالب

Majlesi Journal of Telecommunication Devices
Volume:9 Issue: 3, Sep 2020

  • تاریخ انتشار: 1399/09/01
  • تعداد عناوین: 6
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  • Farbod Setoudeh*, Mohsen Pooya Pages 99-107

    In this paper we want to design linear amplifiers for the Multi Frequency Network (MFN) or Single Frequency Network (SFN) digital terrestrial applications for Digital Video Broadcasting-Terrestrial (DVB-T/T2) transmitters with frequencies of 400 to 900 MHz, so to achieve this aim, we have used Laterally Diffused Metal Oxide Semiconductor (LDMOS) transistor technology has been used. The RF input signal reaches the divider ballun, and then the input of both LDMOS transistors isgiven and the output of two transistors by the collector balloon reaches the output of the amplifier. We have simulated this design by using the Agilent Design System (ADS) software simulator with a gain more than 24 dB, the (Inter Modulation Distortion (IMD) signal has been attenuated from -29 to -52.5dBc and Power Added Effeciency (PAE)from 48 to 66% with 4-stage matching network consisting of capacitors and microstrips with a drain bias voltage of 28 volts and gate bias voltage of 2.86 volts in the frequency range 400 to 900 MHz. Eventually all of the circuit is designed on a printed circuit board

    Keywords: Memcapacitor, Emulator, Electronic Device, Advance Design System
  • Maliheh Ghasemzadeh Pages 109-114

    The purpose of this study is to identify images with deep learning with the least error. In machine learning projects, the basis of the work is extracting features from raw data. Finally, we differentiate different features through classifiers. In the present project, images with dimensions of 224*224 are applied to the network. Most networks use color images, which have 3 channels, the final dimensions of which are 3*224*224. We used the vgg19 network to extract the feature from the image with the highest accuracy. To increase the speed of weight correction operations, batch_size = 30 is considered. 70% of the images were used for network training, 20% for validation and 10% of the data for network testing and evaluation. The speed and accuracy of this project is high.

    Keywords: Deep Learning, Deep Convolutional Network Learning, Supervised LEarning
  • Amin Karimi Dastgerdi Pages 115-126

    The advancement of technology along with the feature of sensor networks has increased the benefits of using remote health networks. Sensor networks designed with a sense of human health parameters have created a physical network. Nodes in the body network are directly connected to the human body and this requires a lot of care. Some health care programs require continuous work to collect patient data without user intervention at any time. Such applications require energy conservation of sensors in these networks, which are limited. Wireless sensor networks are used to monitor the status of patients with disabilities in the hospital, and to track and monitor the movement of specific patients. Wireless sensors can also monitor the performance of operating operators. These networks are used remotely, finding patients and doctors in a therapeutic setting, and managing drugs in a hospital. In this study, our focus will be on reviewing new methods of communication and transmission in the transmission of a WBSN sensor to monitor the vital symptoms of telemedicine patients in the field of electronic health systems. Evaluation and implementation of wireless sensor networks embedded in the body has also been studied in this study due to their importance in the field of human health, especially the control and detection of vital signs and the challenges ahead for their implementation. They will be analyzed.

    Keywords: Electronic Health Systems, Remote Patient Monitoring, Wireless Body Sensor Network, WBAN, Vital Signs Diagnosis
  • Teimour Tajdari Pages 127-131

    This study examines and compares the application of the optimal design method and the recursive least squares (RLS) method to improve the quality of the audio signal in noisy environments. Noise can be incorporated into audio signals through many sources, including amplification systems and electronic switches, which cause loss of signal information or affect the quality of the audio signal. RLS is an adaptive filtering procedure used to design a system that recursively minimizes the noise amplitude of a contaminated signal by comparing the filter output with a desired signal using new incoming signal samples. The optimal design is an FIR filter design technique that has been used to cut parts of the corrupted signal to improve the signal-to-noise ratio. In this study, samples of audio signals contaminated by white noise were used. The noise reduction results show that the RLS approach has vastly improved the quality of the signals. FIR filters, by contrast, can partially improve signal quality. The functionality of the RLS method depended highly on the precision of the measured noise signal. The FIR filter has shown much less signal improvement than the RLS method, but FIR filters are very practical when noise cannot be measured.

    Keywords: Adaptive filtering, Error, FIR filter, Noise, Optimal design, RLS filter
  • Mojtaba Nasehi*, Mohsen Ashourian, Payman Moallem Pages 133-135

    Today, large-scale vehicles are scattered in different parts of the city and therefore need to be controlled by programmed systems. Applications of these systems include traffic control, urban planning, driverless vehicles, parking lot management by announcing the arrival of a vehicle, detecting stolen or offending vehicles, and so on. Due to challenges such as the multiplicity of objects in the image, weather conditions, different colors and designs of the type of vehicles and very diverse images from different angles of a vehicle in the section identifying the type of vehicles in the photo, Films, moving images, etc. have led to a variety of research, and in this article we will examine some of the techniques.

    Keywords: convolution, vehicle, Neural Network
  • Nahid Sarbandi Farahani, Asad Vakili* Pages 137-143

    Research on topology control protocols in wireless sensor networks has often been designed with the goal of creating a dynamic topology and extensibility. The present study focuses on finding high quality paths, instead of minimizing the number of hops that can cause reduction of the received signal strength and maximizing the rate of loss. The purpose of this research is to create a topology control that focuses on reducing the fault and minimizing interference simultaneously. For this purpose, the fault rate and the degree of interference minimizing functions are modeled by using a two-objective genetic algorithm. Since the genetic algorithm is a revelation algorithm, the proposed method is compared in terms of convergence with similar algorithms. The obtained graphs show that the proposed algorithm has a good degree of convergence compared to similar models. The "runtime", "memory consumption" and "energy required to transmit the statement" are the variables used to compare with similar algorithms. By observing the obtained graphs, the proposed algorithm compared to similar methods, reduces the time needed for topology control and also it lowers the energy consumption, but is not able to reduce memory consumption for more packages. The main reason for conducting the test is the comparison of the quality of the routes created, which were executed in 20 different requests with the number of routes 5, 10 and 20. The quality of the routes produced by the proposed method has a 1% improvement over the SMG method and a 3% compared to the PSO method according to the route quality criteria.

    Keywords: Topology Control, Fault Tolerance, Interference, Wireless Sensor Networks, NSGA-II Algorithm, Throughput