In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

This study aimed at comparing the performance of vertical, horizontal and hybrid subsurface flow systems in secondary treatment for the effluent wastewater from the primary basins at Al-Rustumia wastewater treatment plant, Baghdad, Iraq. The treatments were monitored for six weeks while the testsduration were from 4 to 12 September 2018 under continuous wastewater feeding for chemical oxygen demand (COD), total suspended solid (TSS),ammonia-nitrogen(NH₄-N) and phosphate (PO₄-P) in comparison with FAO and USEPA standards for effluent discharge to evaluate the suitability of treated water for irrigation purposes. Among the systems planted with Phragmites Australia, the hybrid subsurface flow system which cons

The Al Mishraq site has been the subject of many scientific studies for the period before and
after the fire in 2003. Five visits to the site were conducted twice in 2003 for general fact-finding, twice
in 2004, and once in 2005 for detailed sampling and monitoring. Desk-based research and laboratory analysis of soil and water samples results indicate that surface water and groundwater pollution from Al Mishraq site was significant at the time of its operation. The primary pollution source was the superheated water injection process, while the principal receptor is the River Tigris. Now that the plant is idle, this source is absent. Following the June 2003 sulphur fire, initial investigations indicate that short damage to
vegeta

This study was aimed to assess the efficiency of N.oleander to remove heavy metals such as Copper (Cu) from wastewater. A toxicity test was conducted outdoor for 65-day to estimate the ability of N.oleander to tolerate Cu in synthetic wastewater. Based on a previous range-finding test, five concentrations were used in this test (0, 50, 100, 300, 510 mg/l). The results showed that maximum values of removal efficiency was found 99.9% on day-49 for the treatment 50 mg/l. Minimum removal efficiency was 94% day-65 for the treatment of 510 mg/l. Water concentration was within the permissible limits of river conservation and were 0.164 at day-35 for the 50 mg/l treatment, decreased thereafter until the end of the observation, and 0.12 at d

Computer systems and networks are being used in almost every aspect of our daily life, the security threats to computers and networks have increased significantly. Usually, password-based user authentication is used to authenticate the legitimate user. However, this method has many gaps such as password sharing, brute force attack, dictionary attack and guessing. Keystroke dynamics is one of the famous and inexpensive behavioral biometric technologies, which authenticate a user based on the analysis of his/her typing rhythm. In this way, intrusion becomes more difficult because the password as well as the typing speed must match with the correct keystroke patterns. This thesis considers static keystroke dynamics as a transparent layer of t

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

This study aims to evaluate the performance of the sewage treatment plant in Al-Diwaniya, one of cities in the southern part in Iraq. This evaluation could be used to facilitate effluent quality assessment or optimal process control of the plant. The influent reaching the plant is considered a medium to strong in strength with BOD5/COD ratio in the range 0.23 and 0.69 which can be considered an easily degradable sewage by the biological processes performed by the activated sludge unit. The quality of the effluent was found to be higher than the Iraqi standards for disposal to water bodies. The BOD5/COD ratios of the treated sewage varied over a wide range as low of 0.13 to 1.48 indicating operational problems in the plant. Regression ana