Computer-aided design model for a quantum-cascade laser

Abstract:In this study, the rate equation analysis of a BH-laser diode was performed for output carrier density ( No) using the zero-degree solution of the Volterra series. The carrier rate equation of the laser diode was analyzed in terms of input carrier density ( Ni) and output carrier density modeling ( Ni − No) , with respect to the DC current ( Io) .The polynomial root, which is obtained from the zero-degree solution ( No) and limit values of Io , was found. For the linear operation of the laser diode, the range of Io current was also determined using a linearization approach and the maximum value of No .

“…Comprehensive analysis of this solution and the Volterra series is made in [24,[33][34][35][36]. The roots curves that are obtained from the zero-degree equation (Eq.…”

Section: The Zero-order Solutionmentioning

confidence: 99%

The detailed analysis of rate equation roots of BH-laser diode using Volterra series

Yıldırım¹,

Karaarslan

2015

“…It is stated that each artificial neuron or processing element has weighted inputs, summation function, activation function, and an output. The architecture, learning rule, and activation function determine the behavior of artificial neural networks [35,36].…”

Section: Competitive Learning Networkmentioning

confidence: 99%

Motion clustering on video sequences using a competitive learning network

Görgünoğlu¹,

Altay²

2014

Abstract:It is necessary to track human movements in crowded places and environments such as stations, subways, metros, and schoolyards, where security is of great importance. As a result, undesired injuries, accidents, and unusual movements can be determined and various precautionary measures can be taken against them. In this study, real-time or existing video sequences are used within the system. These video sequences are obtained from objects such as humans or vehicles, moving actively in various environments. At first, some preprocesses are made respectively, such as converting gray scale, finding the edges of the objects existing in the images, and thresholding the images. Next, motion vectors are generated by utilizing a full search algorithm. Afterwards, k-means, nearest neighbor, image subdivision, and a competitive learning network are used as clustering methods to determine dense active regions on the video sequence using these motion vectors, and then their performances are compared. It is seen that the competitive learning network significantly determines the classification of dense active regions, including motion. Moreover, the algorithms are analyzed in terms of their time performances.

“…Artificial intelligence methods are commonly used in nonlinear systems with no exact solution [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Optimization with genetic algorithm of temperature-dependent fiber length of L-band EDFA gain

Yücel

Mustafa²

2016

Erbium-doped fiber amplifiers (EDFAs) have great importance in long-distance communication. It is required to have equal gain for all signals that are transferred and to avoid loss in the receiver of long-distance communication systems. However, temperature dependence changes the output spectrum of the designed gain-flattening systems. In this study, each erbium-doped fiber (EDF) length of a two-stage L-band EDFA has been optimized using a genetic algorithm method; because of the temperature dependence of EDFAs, there is no general rule. Thus, a simple, fast, dynamic, and highly accurate model has been developed and obtained for different EDF lengths that will fix gain along the L-band. The results have been shown to be very compatible with the previously obtained numerical values.