Using computer simulations,  we  analyze the bit-error rate (BER) characteristics of a high-order QAM  optical transmission system repeated by two-mode phase-sensitive optical parametric amplifiers (PSAs).  In this  system, an idler, which is a phase-conjugated twin of a signal, propagate through the link together with the signal. One advantage of this system is that the  noise figure of two-mode PSAs used as repeaters can approach 0 dB. The other advantage is that  two-mode PSAs cancel out self-phase modulation of the signal at each repeater through the interaction between the transmitted  signal and idler. Owing to these advantages, the BER characteristics  of 16, 64, and 256QAM signals are significantly improved compared with the system using erbium-doped fiber amplifiers and phase-conjugation twin waves, provided that dispersion-shifted fibers are employed for transmission.

The penetration of weather dependent renewable energy sources which are highly stochastic in nature create new challenges with system security, reliability, flexibility, and sustainability. This research focuses on the development of an artificial intelligence-based control method for a laboratory-scale hardware-in-the-loop microgrid setup. This setup features a variety of loads, battery banks, protection relays, renewable and nonrenewable sources of energy, and advanced metering devices interfaced through standard communication protocols. A resilient, cost-effective distributed control strategy allows flexible and reliable autonomous operation and control of the microgrid. Thus, a deep reinforcement learning based multi-agent system is the primary focus for grid control in this research. These intelligent agents learn from the microgrid environment and take actions to maximize their cumulative rewards based on prior experiences. The agents receive rewards for taking good actions and are penalized for bad actions. The results obtained in this research prove the feasibility of this approach.

In this research paper, the problem of characterization of corner positive matrices ( an open research problem ) is addressed. Based on optimization of energy function associated with Hopfield Neural Network, an algorithmic approach to determine the corner positive definiteness of a symmetric matrix is proposed. It is reasoned that the problem addressed is an NP hard problem.

Grating-based X-ray phase-contrast and in particular dark-field radiography are promising new imaging modalities for medical applications. Currently, the potential advantage of dark-field imaging in early-stage diagnosis of pulmonary diseases in humans is being investigated. These studies make use of a comparatively large scanning interferometer at short acquisition times, which comes at the expense of a significantly reduced mechanical stability as compared to tabletop laboratory setups. Vibrations create random fluctuations of the grating alignment, causing artifacts in the resulting images. Here, we describe a novel maximum likelihood method for estimating this motion, thereby preventing these artifacts. It is tailored to scanning setups and does not require any sample-free areas. Unlike any previously described method, it accounts for motion in between as well as during exposures.

Paper on cryptography. The latest version of the paper has been accepted by Chinese Journal of Electronics.

This paper compares different types of GAA FET structures at 3 nm technology node using TCAD simulation, including Lateral Nanosheet, Lateral Nanowire, Vertical Nanosheet, and Vertical Nanowire. Single gate and gate stack structures are included in the analysis. The performance is evaluated using the key device parameters such as on-state current, leakage-current, thresh-old voltage for transfer characteristics, drain-induced barrier lowering (DIBL) and subthreshold slope (SS) for short-channel effects. Among the four GAA structures we found that, under the same effective width, Lateral Nanowire structure shows the highest on-state current, but also the worst performance in short channel effects. In contrast, Vertical Nanosheet shows the lowest DIBL and SS, thanks to

With the technological evolution and new applications, user equipment (UEs) has become a vital part of our lives. However, limited computational capabilities and finite battery life bottleneck the performance of computationally demanding applications. A practical solution to enhance the quality of experience (QoE) is to offload the extensive computation to the mobile edge cloud (MEC). Moreover, the network’s performance can be further improved by deploying an unmanned aerial vehicle (UAV) integrated with intelligent reflective surfaces (IRS): an effective alternative to massive antenna systems to enhance the signal quality and suppress interference. In this work, the MEC network architecture is assisted by UAV-IRS to provide computational services to the UEs. To do so, a cost minimization problem in terms of computing time and hovering energy consumption is formulated. Furthermore, to achieve an efficient solution to a formulated challenging problem, the original optimization problem is decoupled into sub-problems using the block-coordinate decent method. Moreover, numerical results are compared to baseline schemes to determine the effectiveness of the proposed scheme. Simulation results demonstrate that the optimal allocation of local computational resources results in minimizing tasks’ computational time and hovering energy consumption.

Using Newton’s gravitational equation, we have been able to calculate the orbital data of the planets in the solar system more accurately, but there are deviations between these data and astronomical observations. For example, the precession of Mercury’s orbit has a deviation of 43” per century. There are two important gravitational theories in modern physics: general relativity (GR) and quantum mechanics, of which GR has given an explanation for the precession deviation of Mercury, so from the perspective of quantum mechanics, can the quantum gravity equation also be derived? Can it be verified in the calculation of planetary orbits in the solar system? This paper will explore these questions.

Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompts our study, which focuses on demand prediction models for two-stage uncapacitated lot-sizing problems. In this paper, we present a linear prediction model that minimizes the decision error, which is measured by the optimization objective of lotsizing problems. Our model mitigates the impact of prediction errors by leveraging the structure of the lot-sizing problems. We subsequently extend the prediction model to a distributionally robust version. A cutting-plane method is proposed to solve the resulting model under the L_{1} and  L_{infinity} norms. The decision errors are used as loss functions to construct decision treeand neural network-based prediction models, respectively. The corresponding training methods are proposed as well. Numerical experiments demonstrate that the proposed prediction models are significantly superior to traditional prediction methods for small samples and high-dimensional data. In addition, the performance of the robust model is highly tolerant to model misspecification and imperfect data.

The world is currently undergoing a rapid transformation in technology that will drastically change our lives, and potentially redefine what it means to be human. Machine learning has advanced significantly in the last few years. AI has become popular recently because of the large-scale data processing and managing capacities of machines. The application of machine learning in healthcare has two main domains: computer science and medical science. Machine learning techniques have brought advancement in medical science, allowing for the analysis of complex medical data. However, in the healthcare industry, machine learning serves as the doctor’s brain and knowledge. In this paper, we’ll look at some of the benefits of ML-based solutions and how they can be applied to healthcare. In recent years, many conferences have been held to explore new automated technologies in medicine to provide better health care. Several researchers are working in this domain to bring new innovations and features. This paper will outline the main advancements of machine learning in the automation of data analysis for patients’ health records and making predictions based on the data. It will also compare the leaps that have been made in computer-aided diagnosis, drug discovery, and personalized medicine.

In this work we fabricate and electrically demonstrate a 65nm technology- compatible PCM pillar device using Sc- doped SbTe (ST) instead of GeSbTe (GST), for the first time fabricated on 300mm wafer in 1T1R configuration. ST was chosen over GST to achieve a higher speed and endurance due to its faster crystallization speed and reduced volume variation during switching.  Detailed knobs how to improve stack in terms of CD, thickness (of electrode and chalcogenide material) and Sc doping are presented. The optimized stack shows AC switching from 300ns-1µs for SET and RESET with current in the order of  mA and programming voltage less than 2.5V. The endurance shows marginal memory window degradation up to 1E8 cycles and more than 1hour retention at 85℃ is achieved for the optimized stack of C:Si /50nm ST:Sc 6%. The fabricated devices show potential to extend PCM technology towards high-speed SCM applications.

We construct linear codes over odd prime fields for correcting two-dimensional Lee-metric errors on the square and hexagonal signal constellations. They are obtained by puncturing and enlarging either Reed-Solomon codes or Bose-Chaudhuri-Hocquenghem codes. We introduce two-dimensional Lee weight on the square and hexagonal constellations in a similar way as the method presented by the first author in 2019 International Symposium on Information Theory, and propose an effective method for correcting  two-dimensional Lee-metric errors of weight t>=1. The concept of value-locator of an error, which was introduced implicitly by K. Nakamura in 1979 and inherited to the above conference paper, is a key for decoding error-correcting Lee-metric codes. Our decoding method is based on the Buchberger algorithm for finding Groebner bases of ideals in the multivariate polynomial ring. The results of simulations show that our codes are more powerful for correcting two-dimensional Lee-metric errors in the two-dimensional signal constellations than the conventional codes for correcting Hamming-metric errors and one-dimensional Lee-metric errors, and the decoding method works well for correcting two-dimensional Lee-metric errors of small weight t.

The use of AI has already penetrated some areas of life. By shaping machine learning, it can also be used in the area of construction financing. This includes the factors of advice, the potential of land and building, as well as financial security for lending. Trends in IT are primarily AI and its sub-areas of machine learning and dynamic automation. This paper presents a scenario of how AI can transform and automate the field of construction financing. Corresponding recommendations for action a

IEEE time-sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) or distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, the data flow control for network load balancing in an automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were performed. The results show that enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are only alternatives for data load control in small networks or in conjunction with slow applications.

This paper explores the ease of us of standard Kali Linux tools to analyse network activity, and conduct a series of man-in-the-middle attacks within a wireless local area network. These tests primarily include ARP poisoning, deauthentication attacks and finally cloning a rogue access point to carry out further social engineering attacks and web browser control (with BeEF). Lastly this paper explores the usage of VPNs to protect a user’s privacy as it is difficult to prevent packet sniffing and man-in-the-middle attacks outright

Many Internet-of-Things (IoT)-over-satellite applications rely on affordable location-aware but energy-constrained IoT sensors. In this paper, we propose a novel method to estimate the satellite visibility window in IoT devices based on simple Doppler measurements. We present two scenarios where the orbital information of the serving satellite’s is initially unknown to the IoT device: (i) we assume that the geographic coordinates are known to the IoT device, and (ii) we assume that the coordinates are completely unknown. Accordingly, we  derive the Doppler measurement likelihood function, and simplify it to a root mean square error (RMSE) minimization problem. From a sequence of Doppler measurements, we estimate the orbital parameters of the serving satellite using a stochastic optimizer to minimize the RMSE. From the orbital estimation, we then predict the satellite visibility window (satellite pass). To gauge the accuracy of the window estimation, we apply the intersection-over-union metric to compute the overlapping visibility window between the ground truth and the estimation, and consequently present the results based on extensive Monte Carlo simulations.

Smart Grid (SG) technology has been invasive to privacy – the proponents of SG have little assurance that personal data originating from smart meters (SMs) are entirely private. In recent years, new momentum has been building up for a major rethink on the privacy-preserving mechanism for personal SM data, which is essential for efficient utilization of the advances in SG.

Organ segmentation from CT images is critical in early diagnoses of diseases, progress monitoring, pre-operative planning, radiation therapy planning and CT dose estimation. However, data limitation remains one of the main challenges in the medical image segmentation domain. This challenge is particularly huge in pediatric medical imaging due to the patients’ heightened sensitivity to radiation. To address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed CFG-SegNet (conditional feature generation segmentation network) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7. CFG-SegNet achieves an average segmentation accuracy of 0.681 DSC (Dice Similarity Coefficient) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with 4-fold cross-validation. We compare the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by  2.7\%, 2.6\%, 2.8\%, and 3.4\% for prostate, uterus, liver, and heart respectively. The results indicate that our high-performing segmentation framework can precisely segment organs when limited training images are available.