In the advanced applications, based on infrared detection systems, the precise detection of small targets has become a tough work today. This becomes even more difficult when the background is highly dense in addition to the nature of small targets. The problem raised above is solved in various ways, including infrared patch image (IPI) based methods which are considered to have the best performance. In addition, the greater shrinkage of singular values in the methods based on IPI leads to the problem of nuclear norm minimization (NNM), which leads to the problem of incorrectly recognizing small targets in a highly complex background. Hence, this paper proposed a new method for infrared small target detection (ISTD) via total variation and partial sum minimization (TV-PSMSV). The proposed TV-PSMVS in this work basically replaces the IPI’s NNM with partial sum minimization (PSM) of singular values and, additionally, the total variance (TV) regularization term is inducted to the background patch image (BPI) to suppress the complex background and enhance the target object of interest. The mathematical solution of the proposed TV-PSMSV approach was performed using alternating direction multiplier (ADMM) to verify the proposed solution. The experimental evaluation using real and synthetic data set was performed, and the result revealed that the proposed TV-PSMSV outperformed existing referenced methods in the terms of background suppression factor (BSF) and the signal to gain ratio (SCRG).

A huge amount of generated data is regularly exploding into the network by the users through smartphones, laptops, tablets, self-configured Internet-of-things (IoT) devices, and machine-to-machine (M2M) communication. In such a situation, satisfying critical quality-of-service (QoS) requirements (e.g., throughput, latency, bandwidth, and reliability) is a large challenge as a vast amount of data travels into the network. Nowadays, strict QoS requirements must be satisfied efficiently in many networked multimedia applications when intelligent multi-homed devices are used. Such devices support the concept of multi-homing. To be precise, they have multiple network interfaces that aim to connect and communicate concurrently with different networking technologies. Therefore, many multipath transport protocols are provided to multi-homed devices, which aim (1) to take advantage of several network paths at the transport layer (Layer-4) and (2) to meet the strict QoS requirements for providing low network latency, higher data rates, and increased reliability. To this end, this survey first presents the challenges/problems for supporting multipath transmission with possible solutions. Then, it reviews recent research efforts related to the concurrent multipath transmission (CMT) protocol and the multipath transmission control protocol (MPTCP). It reviews the latest research efforts by considering (1) how a multipath transport protocol operates (i.e., its functionality); (2) in what type of network; (3) what path characteristics it should consider; and (4) how it addresses various design challenges. Furthermore, it presents some lessons learned and discusses open research issues in multipath transport protocols.

Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI‐based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp‐ norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.

The usage of the Internet increased dramatically during the start of the twenty-first century, entangling the system with a variety of services, including social media and e-commerce. These systems begin producing a large volume of data that has to be secured and safeguarded from unauthorised users and devices. In order to safeguard the information of the cyber world, this research suggests an expanded form of differential evolution (DE) employing an intelligent mutation operator with an optimisation-based design. It combines a novel mutation technique with DE to increase the diversity of potential solutions. The new intelligent mutation operator improves the security, privacy, integrity, and authenticity of the information system by identifying harmful requests and responses and helping to defend the system against assault. When implemented on an e-commerce application, the performance of the suggested technique is assessed in terms of confidentiality, integrity, authentication, and availability. The experimental findings show that the suggested strategy outperforms the most recent evolutionary algorithm (EA).

The target detection ability of an infrared small target detection (ISTD) system is advantageous in many applications. The highly varied nature of the background image and small target characteristics make the detection process extremely difficult. To address this issue, this study proposes an infrared patch model system using non-convex (IPNCWNNM) weighted nuclear norm minimization (WNNM) and robust principal component analysis (RPCA). As observed in the most advanced methods of infrared patch images (IPI), the edges, sometimes in a crowded background, can be detected as targets due to the extreme shrinking of singular values (SV). Therefore, a non-convex WNNM and RPCA have been utilized in this paper, where varying weights are assigned to the SV rather than the same weights for all SV in the existing nuclear norm minimization (NNM) of IPI-based methods. The alternate direction method of multiplier (ADMM) is also employed in the mathematical evaluation of the proposed work. The observed evaluations demonstrated that in terms of background suppression and target detection proficiency, the suggested technique performed better than the cited baseline methods.