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Higher-order modulation schemes in optical communication systems that suffer from several impairments can use artificial intelligence (AI) algorithms, among other possible techniques, to mitigate these issues. In this paper, several techniques for optical communication systems have been proposed to enhance the performance of dual-polarization (DP) M-ary Quadrature Amplitude Modulation (M-QAM) as DP-16-QAM, DP-64-QAM, DP-128-QAM, and DP-256-QAM with 240Gbps data rate. Artificial neural networks (ANNs) with seven different training algorithms have been applied to optimize the optical communication system. A high optimization of modulation format identification (MFI) with accuracy up to 100% was obtained at about 13 dB OSNR and at 22 dB OSNR for the DP-265-QAM format.

<p id="docs-internal-guid-6a9909c3-7fff-4ac4-3e3a-a2f823e1246f" dir="ltr"><span>A new design of microstrip dual mode band-pass-filter (BPF) by using stepped impedance resonator (SIR) based on shorting pin is proposed. The designed structure use two U-shaped tri-sections SIR resonators coupled to each other and a two coupled line feeding ports each of 50 ohm impedance. Shorting pins are used to excite the upper frequency passband in the re   sponse of the filter due to current distribution perturbation at the locations of the shorting pins.  For demonstration, WLAN (5.2-5.7 GHz) and GSM (1.85-1.99 GHz) and Advanced Wireless Services (AWS) (1.71-1.755 GHz). The return losses are -32.469 dB and -26.18 dB respectively at the operating frequencies of the filter.  The results of  insertion losses of the filter is 0.37 and 0.24 dB during the operating bands and more than 25 dB which consider a good out-of- band rejection. </span></p>

Street lighting is very important now-days especially at dangerous areas and highways but it consume a lot of power and it became challenging for many researchers in the past few years. Enormous efforts have been placed on the issue of reducing power consumption in illuminating cities and streets, researchers had various approaches and methods in tackling this challenging matter, till now there is no ideal system that has been developed to reduce the electricity usage. In this paper intelligent controller based on deep learning proposed to control the light at the street from sunset to sunrise, the system will decrease the light used to illuminate the streets in the absence of movements, the network trained based on deep learning with several image of different objects to help the system detecting any moving objects in the street to provide the street with the exact amount of light needed in order to reduce the waste of electrical energy resulting from street lighting and to help reduce accidents hence high percentage of criminal activity and life threatening conditions occur in the absence of light. The system was trained with a vast and diverse dataset to assure the accuracy and efficiency of the proposed system, the trained system showed a result of 90 precision of detecting moving objects, the proposed system was tested with a new dataset to assure the reliability and dependency of the system and reducing the errors to the minimum, the system shows promising results in detecting movements and objects, after the detection being complete, the system will send a pulse width modulation causing a 20 % light dimming, leading to enormous reduction in the power consumption, adding to that the proposed system is easy to use

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