• A dynamic convolution is used into a CNN to address limitations in depth and width of lightweight CNNs for pursuing good denoising performance. The combination signal processing technique discriminative learning image denoising. Enhanced residual dense architectures are remove redundant information improving effects Deep convolutional neural networks (CNNs) via automatically mining accurate s...

Using fingerphoto images acquired from mobile cameras, low-quality sensors, or crime scenes, it has become a challenge for automated identification systems to verify the identity due various acquisition distortions. A significant type of photometric distortion that notably reduces quality is blurring image. This paper proposes deep deblurring model restore ridge information degraded by image bl...

In the last decade, multi-stage stochastic programs with recourse have been broadly used to model real-world applications. This paper reviews the main optimization methods that are used to solve multi-stage stochastic programs with recourse. In particular, this paper reviews four types of optimization approaches to solve multi-stage stochastic programs with recourse: direct methods, decompositi...

In this paper we examine the queuing behavior of a multi-stage multiplexer with fixed length packets flowing through the stages. The system consists of two components: a front end multi-input, multi-output device as a preliminary stage, and a single-server, deterministic service time queue system (multiplexer) as a final stage. We treat arbitrary exogenous arrival patterns and examine sample pa...

This paper describes a geometric algorithm for automated design of multi-stage molds for manufacturing multi-material objects. In multi-stage molding process, the desired multimaterial object is produced by carrying out multiple molding operations in a sequence, adding one material in the target object in each mold-stage. We model multi-material objects as an assembly of single-material compone...

This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...