Magnetostrictive Position Sensors (MPS), widely used for accurate displacement and velocity measurements, operate based on the Time-of-Flight (ToF) of structure-borne ultrasonic waves induced by magnetic interactions. However, their measurement accuracy can be significantly affected by External Magnetic Interference (EMI), which distorts the characteristic waveforms essential for precise ToF estimation. This study investigates the abilities of a Self-x concept to improve measurement accuracy by the integration of hybrid artificial intelligence techniques (Hybrid-AI) for the classification and mitigation of EMI-induced errors in MPS. The proposed approach is particularly suited for industrial environments characterized by diverse EMI sources. An additional external sensor is not required. Experimental evaluations confirm a significant improvement by self-monitoring and self-adjustments.

Magnetostrictive Position Sensors (MPS) are used for precise distance and velocity measurements. They utilize magnetostriction to generate structure-borne sound waves and work on the basis of Time-of-Flight (ToF) calculations. However, external electromagnetic interference (EMI) can impact the accuracy of these sensors by interacting with the magnetic fields of magnetostriction. To address this issue, a novel hybrid approach utilizing both neural and symbolic AI has been developed to classify the intensity of EMI. This system is based on the combination of Log-Likelihood Ratios (LLRs). This study’s findings are particularly significant for industrial environments with numerous sources of external electromagnetic interference, where precise measurement is critical.

Digital communication depends on channel coding for the integrity and correct reception of the data. The traditional communication techniques ignore the context and content associated with the received data while mitigating the channel effects. Image transmission in digital communication have many major constraints. Image quality degrades over wireless channel due to limited characteristic of transmitted data. To mitigate the effects of the noisy channel on the images different image denoising techniques are used such as DCT, Convolutional Denoising Autoencoders, DWT, BM3D, FFT, NLM and Deep Learning. Main objective of this paper is to study the application of machine learning in digital communication to correct errors and remove the effects of channel degradation. This will help us improve the energy consumption, resource utilization, and the Bit Error Rate. It will enable us to communicate with low bandwidth and using minimum resources. Machine learning for denoising the image has attracted substantial attentions because of its high denoising performance. We have constructed a Convolutional Neural Network that will denoise the image which is corrupted by the noisy channel. The existing traditional models only denoise the image for a specific noise type (Gaussian noise) and certain noise level σ = 25, but our trained network denoise the image for unknown noise level. By using the methods of batch normalization and residual learning the denoising performance is increased. Experiments prove that our trained model displays high efficiency in image denoising. The proposed network is trained in MATLAB and Python with the help of GPU computing which accelerates neural network's performance. The application areas of this paper mainly are in the domains related to digital communications. This include wireless communications, optical communications and line communications. However, as the wireless communications (especially underwater wireless) is extremely error prone, so this domain will be highly benefited. The applications can include military communications, Underwater Communication and Noisy Industrial Communication.

A key challenge in network virtualization is to efficiently map a virtual network (VN) on a substrate network (SN), while accounting for possible substrate failures. This is known as the survivable VN embedding (SVNE) problem. The state-of-the-art literature has studied the SVNE problem from infrastructure providers' (InPs') perspective, i.e., provisioning backup resources in the SN. A rather unexplored solution spectrum is to augment the VN with sufficient spare backup capacity to survive substrate failures and embed the resulting VN accordingly. Such augmentation enables InPs to offload failure recovery decisions to the VN operator, thus providing more flexible VN management. In this paper, we study the problem of jointly optimizing spare capacity allocation in a VN and embedding the VN to guarantee full bandwidth in the presence of multiple substrate link failures. We formulate the optimal solution to this problem as a quadratic integer program that we transform into an integer linear program. We also propose a heuristic algorithm to solve larger instances of the problem. Based on analytical study and simulation, our key findings are: 1) provisioning shared backup resources in the VN can yield ~33% more resource efficient embedding compared to doing the same at the SN level and 2) our heuristic allocates ~21% extra resources compared to the optimal, while executing several orders of magnitude faster.

Perceived as a key enabling technology for the future Internet, Network Virtualization (NV) allows an Infrastructure Provider (InP) to better utilize their Substrate Network (SN) by provisioning multiple Virtual Networks (VNs) from different Service Providers (SPs). A key challenge in NV is to efficiently map the VN requests from SPs on an SN, known as the Virtual Network Embedding (VNE) problem. VNE algorithms are typically online in nature. A VN embedding can become suboptimal over time due to the arrival and departure of other VNs as well as due to changes in SN such as failures. One way to mitigate the impact of such dynamism is to periodically reallocate resources for the existing VNs. VNE reallocation can increase an InP's revenue by decreasing bandwidth consumption and by increasing the possibility of accepting future VNs. In this paper, we study Reallocation of Virtual Network Embedding (ReViNE) problem to minimize the number of over utilized substrate links and total bandwidth cost on the SN. We propose an Integer Linear Programming formulation for the optimal solution (ReViNE-OPT) and a simulated annealing based heuristic (ReViNE-FAST) to solve larger problem instances. Simulation results show that on average our proposed heuristic performs within ∼19% of the optimal solution. Moreover, ReViNE-FAST generates more than 2.5× better solutions compared to the state-of-the-art simulated annealing based heuristic for VNE reallocation.

Network virtualization has evolved as a key enabling technology for offering the next generation network services. Recently, it is being rolled out in data center networks as a means to provide bandwidth guarantees to cloud applications. With increasing deployments of virtual networks (VNs) in commercial-grade networks with commodity hardware, VNs need to tackle failures in the underlying substrate network. In this paper, we study the problem of recovering a batch of VNs affected by a substrate node failure. The combinatorial possibilities of alternate embeddings of the failed virtual nodes and links of the VNs make the task of finding the most efficient recovery both non-trivial and intractable. Furthermore, any recovery approach ideally should not cause any service disruption for the unaffected parts of the VNs. We take into account these issues to design a generalized recovery approach that can achieve customized objectives such as fair treatment on the failed VNs, partial treatment based on priority, and so on. We provide integer linear programming (ILP) formulations for two variants of our recovery scheme, namely, fair recovery model and priority-based recovery model. We also propose a fast and scalable heuristic algorithm to tackle the computational complexity of the ILP solution. Evaluation results demonstrate that our heuristic performs close to the optimal solution and outperforms the state-of-the-art algorithm.

Network visualization (NV) has evolved as a key enabling technology for offering the next generation network services. Recently, it is being rolled out in data center networks as a means to provide bandwidth guarantees to cloud applications. With increasing deployments of virtual networks (VNs) in commercial-grade networks with commodity hardware, VNs need to tackle failures in the underlying substrate network. In this paper, we study the problem of recovering a batch of VNs affected by a substrate node failure. The combinatorial possibilities of alternate embeddings of the failed virtual nodes and links of the VNs makes the task of finding the most efficient recovery both non-trivial and intractable. Furthermore, any recovery approach ideally should not cause any service disruption for the unaffected parts of the VNs. We take into account these issues to design a recovery approach for maximizing recovery and minimizing the cost of recovery and network disruption. We provide an Integer Linear Programming (ILP) formulation of our recovery scheme. We also propose a fast and scalable heuristic algorithm to tackle the computational complexity of the ILP solution. Evaluation results demonstrate that our heuristic performs close to the optimal solution and outperforms the state-of-the-art algorithm.

Adaptive relaying schemes significantly improve the performance of relay networks by switching the forwarding technique according to the reliability of the received signal. In this paper, semi-analytical solution for the power optimization in adaptive relay network is derived. Furthermore, the accuracy of the solution is confirmed by using the polyblock algorithm, which is an optimization tool for monotonic programs. The results show that significant power gains can be achieved by the devised algorithm.