An Overview on a Technique to Measure and Control of the Electromagnetic Radiation Inside a Microwave Oven

The Microwave oven is a system used to convert the electromagnetic energy to thermal energy when the microwave cavity is loaded with a dielectric material. The ordinary microwave ovens are not supported with complex features for detecting parameters such as temperature, weight, and loaded material availability. Due to the lack of material availability, several laboratory and industrial applications require these features to switch off the oven. The reflections of electromagnetic radiation inside an empty microwave oven lead to oven damage. An overview of the microwave oven characteristics and emergence of electromagnetic radiation inside a microwave oven is presented in this study. The parameters measured inside the microwave oven, methods for power attenuation in a microwave oven, microwave power detector, and microwave oven leakage are discussed as well. Moreover in the methodology of this work, proposed a new technique based on the measurement of leaked microwave power to control the microwave oven. The preliminary results showed that the leakage measurement of electromagnetic power changes with the state/phase of the material inside the microwave oven, which ensured the possibility of the proposed promising technique. This work will be continued to connect the microwave oven with a spectrum analyzer and computer via hardware and software interfaces depending on the methodology of this article. A computer code will be developed to read the measured power and automatically switch off the microwave oven depending on materials state. Index Terms— Microwave, power, measurement, control.

Video Compression Based on FPGA Using SIMD Architecture

Recently, video files and images have became the dominant media material for transmitting or storing across different applications that are used by different people. So, there was a serious need to find more effective and efficient video compression techniques to reduce the large size of such multimedia files. This paper proposes SIMD based FPGA lossless JPEG video compression system with the facility of scalability. Generally, the proposed system consists of a software side and a hardware side. The digital video file is prepared to be processed by the hardware side frame by frame on the software side. The hardware side is proposed to consist of two main processing circuits, which are the prediction circuit for calculating the predicted value of each pixel in the certain frame and the encoding circuit that was represented by a modified RLE (Run-Length-Encoder) to encode the result obtained through subtracting the predicted value from the real value for each pixel to produce the final compressed video file. The compression ratio obtained for the proposed system is equal to 1.7493. The throughput improvement for the two and four processing units basing on SIMD architecture was 100 MP/s and 200 MP/s, respectively. The clock results showed that the number of clocks required had become 50% and 25% when using two processing units and four processing units, respectively, from the number of clocks using single processing units. Index Terms— Video Compression, Lossless JPEG, RLE, FPGA.

Recognition of Handwritten English Numerals Based on Combining Structural and Statistical Features

Generally, pattern recognition considered a strong challenge in many information processing research fields. The aim of this paper is to propose a highly accurate model for recognizing a handwritten English numeral through efficiently extracting the most valuable features of a certain handwritten numeral or digit. The handwritten English Numerals Recognition Model (HENRM) is proposed in this paper. The features extraction of the proposal based on combining both statistical and structural features of the certain numeral sample image. Mainly, the proposed HENCM has four phases which are image acquisition, image preprocessing, features extraction, and classification. In fact, four feature extraction approaches are utilized in this paper, which are the number of intersection points, the number of open-end points, calculation of density feature, and determining the chain code for each of the English numerals. The latter phase gives a features vector of 26-element size to be fed into the classifier that uses the Multi-class Support Vector Machine (MSVM) for the classification process. The experimental results showed that the proposed HENCM exhibits an average recognition rate equals to 97%. Index Terms—Chain Code, Density feature, MSVM, Recognition.

Predicting Incident Duration Based on Machine Learning Methods

Traffic incidents dont only cause various levels of traffic congestion but often contribute to traffic accidents and secondary accidents, resulting in substantial loss of life, economy, and productivity loss in terms of injuries and deaths, increased travel times and delays, and excessive consumption of energy and air pollution. Therefore, it is essential to accurately estimate the duration of the incident to mitigate these effects. Traffic management center incident logs and traffic sensors data from Eastbound Interstate 70 (I-70) in Missouri, United States collected during the period from January 2015 to January 2017, with a total of 352 incident records were used to develop incident duration estimation models. This paper investigated different machine learning (ML) methods for traffic incidents duration prediction. The attempted ML techniques include Support Vector Machine (SVM), Random Forest (RF), and Neural Network Multi-Layer Perceptron (MLP). Root mean squared error (RMSE) and Mean absolute error (MAE) were used to evaluate the performance of these models. The results showed that the performance of the models was comparable with SVM models slightly outperforms the RF, and MLP models in terms of MAE index, where MAE was 14.23 min for the best-performing SVM models. Whereas, in terms of the RMSE index, RF models slightly outperformed the other two models given RMSE of 18.91 min for the best-performing RF model. Index Terms— Incident Duration, Neural Network Multi-Layer Perceptron, Random Forest, Support Vector Machine.

Photonic Crystal Fiber Sensor for X Ray Approximate Effective Radiation Dose

X-ray is electromagnetic wave pass through all human tissue and show tissue image by black and white, and the explosion for x ray in short treatment may cause cancer, Photonic crystal fiber used as detector for x- ray approximate effective radiation dose for human bone for spine x- ray 1.5 mSv which need 6month to repeat the exposure and extremity (foot, hand and etc.) x-ray 0.001 mSv need to 3 hours to repeat exposure , the change in x ray dose can be detected by measuring the change in wavelength shifting of laser 450nm which pass through photonic crystal fiber and effected by the emission of x-ray to record the small change in x-ray dose and save the human from radiation and this sensor is small compact and easy to use and have high sensitivity for x -ray used to measure the bone x-ray as its value lower than other x-ray used in others tissue. Index Terms— photonic crystal fiber, sensor ,x- ray, laser.

Application of Self-Managing System in Greenhouse with Wireless Sensor Network

It is well known that the Wireless Sensor Network (WSN) is a part of different fields of modern life (self-managing life). The automation of operating the WSN without any need for human efforts develops the technique used in this network in terms of power consumption, costs, and so on. In this paper, the WSN application that adopts the self-managing property is presented as well as applying this property in a greenhouse as a case study. It uses remote controlling technology for data exchanging in a multi-layer WSN. Different hardware and software equipment have been employed in the application based on self-managing techniques. Such hardware includes (NodeMCU ESP8266) as a microcontroller, Raspberry pi3 as a base station that captures the sensor reading from the node. Additionally, it was utilized two types of sensors (DS18B20, soil moisture) to sense the environmental parameters such as temperature and soil moisture and two actuators (LED1, LED2). The results show the proper performance of the presented WSN in terms of the self-managing side by checking the received data in real-time mode. These results are achieved for the threshold values of 25ᵒC temperature and 500 soil moisture as an upper level for operating the actuators. Index Terms—WSN, MQTT protocol, NodeMCU ESP8266, Raspberry pi3, sensors.

Hybrid Model for PAPR Minimization in OFDM System

The technology has been growing rapidly in the form of portable wireless devices that can perform multiple functions to cope with the state-of-the-art technology and synchronization. A total device capacity must be increased to accommodate new wireless applications. This can be achieved by leveraging new technologies, with higher data rates. Spectrum pooling has gained immense popularity, with increased demand for frequency range and bandwidth availability constraints. Statistics suggest that much of the spectrum licensed is not used all the time. Because of the transmitter's nonlinearity nature, the large (peak to average power ratio (PAPR)) phenomenon is a drawback in orthogonal frequency division multiplexing (OFDM). Several hybrid approaches have recently been implemented to minimize PAPR's high value, at the expense of increasing the level of computational complexity in the system. In this paper, a new hybrid approach has been introduced in parallel to combine the selective mapping approach (SLM) with the partial transmit sequence (PTS) approach to improve the efficiency of PAPR reduction with lower numerical method complexity. The findings reveal that the OFDM systems with the proposed hybrid approach have better efficiency in terms of PAPR elimination, side-information, and computational complexity compared to current hybrid methods. Also, a hybrid approach proposed output could be maintained without degradation. Index Terms: OFDM, PTS, PAPR, SLM

The Design and Simulation of FBG Sensors for Medical Application

Fiber Bragg Grating sensors have a wide range of applications, ranging from their use for health monitoring, in medical applications and also as biomedical sensors, among others. Moreover, since fiber Bragg gratings have many advantages that qualify them to be of great benefit, of course, the most important of these applications are vital signs of human health condition Such as blood pressure, heart rate (pulse rate), and body temperature. The Vital-signs are noticeable variables. The temperature and blood pressure are changed according to the physical, involuntary, nervous and psychological state of the person. Therefore, the measurement of vital signs is very necessary, In this work, fiber Bragg grating sensor has been designed and simulated to study the performance of fiber Bragg grating sensor as a Body temperature of human beings ranged from (35°C to 40°C, which is from hypothermia to hyperthermia) and blood pressure that ranged between lower and higher extremities (40 to 190 mmHg ) from hypotension to hypertension, using optigrating and optisystem simulation softwar. The designed sensor was very sensitive to human temperature and blood pressure ranges which were 13.632 pm/oC and 15.75 pm/mmHg, respectively.

Low-Cost MEMS-Based NARX Model for GPS-Denied Areas

Autonomous vehicle navigation has witnessed a huge revolutionary revision regarding development in Micro-Electro Mechanical System (MEMS) technology. Most recently, Strapdown Inertial Navigation System (SDINS) has successfully been integrated with Global Positioning System (GPS). However, different grades of MEMS inertial sensors are available and choosing the convenient grade is quite important. Noises in inertial sensor are mostly treated through de-noising the additive errors to improve the precision of SDINS output. Unfortunately, integration in SDINS mechanization causes a growing in SDINS error output which considered the main challenge in integrating MEMS inertial sensors with GPS. This paper aims to promote the long-term performance of the MEMS-SDINS/GPS integrated system. A new integrated structure is proposed to model the nonlinearities that exist in SDINS dynamics in addition to the error uncertainty in the inertial sensors’ measurements. A robust Nonlinear AutoRegressive models with eXogenous inputs (NARX) based algorithm are designed for data fusion in the proposed GPS/INS integrated system. Validation for the proposed integrated system has been carried out using different field tests data in order to assess the accuracy of the system during GPS denied environment. The results obtained demonstrate that the proposed NARX model is applicative and satisfactory which shows a desired prediction performance.

The Effect of Mode Area and Refractive Index for Optical TE Mode Propagation in Hybrid LN/Si Electro-Optic Structure of Mach-Zehnder Modulator

We propose and analyse a silicon based hybrid modulator on the nano thin film of the lithium niobate or commonly known as silicon-on-insulator technology. The Mach–Zehnder stripe optical waveguide of electro-optical modulator operats at GHz frequencies with large bandwidth and low losses between electrical and optical frequencies.The design and simulation of Mach-Zehnder modulator is based on a hybrid integration platform of silicon and lithium niobate that satisfies a single mode condition. The Silicon stripe waveguide is of 0.6 μm thickness in a silicon on insulator (SOI) of width 15 um and 0.05 um thickness x-cut LiNbO3 thin film, all sets use the pulse laser deposition (PLD) method. The Optical electric field distributions and effective mode area in the optical-waveguides were studied and discussed in this designated waveguide.The relationship between the width of waveguides regions with effective mode index and effective mode area was investigated. At 0.6 um width of waveguide and 0.2 um thickness, the effective mode index 1.9802 was recorded while the effective mode area 0.144 um2 was monitored. This shows the decrement in both: the width and thickness of the waveguide with the effective mode index and effective mode area.