Search

Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial–temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications.

Fasciolopsiasis is a disease caused by the largest food-borne intestinal trematode known as Fasciolopsis buski. Here we report a case of a 50-year-old female who presented with upper abdominal pain and vomiting for one month. She was previously diagnosed as a case of choledocholithiasis. Endoscopic retrograde cholangio-pancreatography (ERCP) revealed stones along with two leaf-shaped worms which were removed from the duodenum of the patient. The worms were identified as F. buski by its unique morphology. Awareness regarding this parasitic infestation, especially in the rural area, should gain attention while formulating strategies to prevent and manage such infestation.

System frequency may change from defined values while transmitting power from one area to another in an interconnected power system due to various reasons such as load changes and faults. This frequency change causes a frequency error in the system. However, the system frequency should always be maintained close to the nominal value even in the presence of model uncertainties and physical constraints. This paper proposes an Active Disturbance Rejection Controller (ADRC)-based load frequency control (LFC) of an interconnected power system. The controller incorporates effects of generator inertia and generator electrical proximity to the point of disturbances. The proposed controller reduces the magnitude error of the area control error (ACE) of an interconnected power system compared to the standard controller. The simulation results verify the effectiveness of proposed ADRC in the application of LFC of an interconnected power system.

Distributed generation (DG) is one of the key components of the emerging microgrid concept that enables renewable energy integration in a distribution network. In DG unit operation, inverters play a vital role in interfacing energy sources with the grid utility. An effective interfacing can successfully be accomplished by operating inverters with effective control techniques. This paper reviews and categorises different control methods (voltage and primary) for improving microgrid power quality, stability and power sharing approaches. In addition, the specific characteristics of microgrids are summarised to distinguish from distribution network control. Moreover, various control approaches including inner-loop controls and primary controls are compared according to their relative advantages and disadvantages. Finally, future research trends for microgrid control are discussed pointing out the research opportunities. This review paper will be a good basis for researchers working in microgrids and for industry to implement the ongoing research improvement in real systems.