Application of Random Walk Model for Timing Recovery in Modern Mobile SATCOM Systems

In a modern mobile satellite communication (SATCOM) system, a ground terminal receiver receives a radio frequency signal that is demodulated to generate a baseband digital signal waveform containing a self-clocking bit stream of digital data. The received baseband digital signal waveform is recovered and tracked using a timing recovery loop (TRL). The traditional TRLs use early-and-late gates, digital transition tracking, filter-and-square, and delay-and-multiply functions. In bit timing detection, the bit stream is self-clocking and the timing differential dithers about correct bit timing in the TRLs. For mobile satellite communication environments, the traditional TRLs drop lock when the loop signal-to-noise ratio (SNR) is smaller than a threshold value or the residual Doppler frequency is larger than the operating loop bandwidth. After dropping lock, the traditional TRLs experience long hang up time due to the need to reacquire the timing pulses. Recently, random walk filters (RWF) have been adapted to improve the bit clock locking stability and are applied to recover bit timing information of a digital data stream. This chapter describes random walk model for timing jitter and discusses how RWF solution can address the timing recovery challenges in mobile satellite communication environments

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Exploring Millions of 6-State FSSP Solutions: the Formal Notion of Local CA Simulation

In this paper, we come back on the notion of local simulation allowing to transform a cellular automaton into a closely related one with different local encoding of information. This notion is used to explore solutions of the Firing Squad Synchronization Problem that are minimal both in time (2n -- 2 for n cells) and, up to current knowledge, also in states (6 states). While only one such solution was proposed by Mazoyer since 1987, 718 new solutions have been generated by Clergue, Verel and Formenti in 2018 with a cluster of machines. We show here that, starting from existing solutions, it is possible to generate millions of such solutions using local simulations using a single common personal computer

Heterologous expression of a recombinant lactobacillal -galactosidase in Lactobacillus plantarum: effect of different parameters on the sakacin P-based expression system

Background: Two overlapping genes lacL and lacM (lacLM) encoding for heterodimeric -galactosidase from Lactobacillus reuteri were previously cloned and over-expressed in the food-grade host strain Lactobacillus plantarum WCFS1, using the inducible lactobacillal pSIP expression system. In this study, we analyzed different factors that affect the production of recombinant L. reuteri -galactosidase. Results: Various factors related to the cultivation, i.e. culture pH, growth temperature, glucose concentration, as well as the induction conditions, including cell concentration at induction point and inducer concentration, were tested. Under optimal fermentation conditions, the maximum -galactosidase levels obtained were 130 U/mg protein and 3540 U/ml of fermentation broth corresponding to the formation of approximately 200 mg of recombinant protein per litre of fermentation medium. As calculated from the specific activity of the purified enzyme (190 U/mg), -galactosidase yield amounted to roughly 70% of the total soluble intracellular protein of the host organism. It was observed that pH and substrate (glucose) concentration are the most prominent factors affecting the production of recombinant -galactosidase. Conclusions: The over-expression of recombinant L. reuteri -galactosidase in a food-grade host strain was optimized, which is of interest for applications of this enzyme in the food industry. The results provide more detailed insight into these lactobacillal expression systems and confirm the potential of the pSIP system for efficient, tightly controlled expression of enzymes and proteins in lactobacilli.(VLID)90704

Enhancing students' digital skills through promotion relationship between business and university to digital era integration

The rapid changes in the technology and labour market requires actions in education throughout the world. Currently, the Ministry of Education and Training in Vietnam provides guidelines and encourages universities and colleges to have direct interaction with industry partners to close skill gaps, especially in the digital era when universities lack updated resources for training. To prepare well essential skills for students at 8 higher education institutions in Vietnam, collaboration between industry partners and university is a must. This paper will explore how to develop a sustainable university-industry linkage at Phu Xuan University to enhance the digital capacity of students in the digital era. Reviewing existing university-industry linkages and proposing new activities and solutions for sustainable collaboration based on current practices will be presented in this paper

Program Management Integrated with Data and Decision Sciences

Program management (PM) complexity depends on the budget size and program types. In general, the program types can be classified into three categories, namely, defense, commercial, and civilian types. This chapter presents and discusses an approach for integrating the PM discipline areas with emerging data science and decision science1 (DDS) for any program type. Additionally, we describe the key PM areas and present a corresponding generalized model consists of a list of multiple PM discipline areas that can be tailored for any program types. To demonstrate the PM-DDS integration approach, we focus on three key PM areas and corresponding PM discipline areas related to schedule, cost, and risk management. These three discipline areas are analyzed to identify appropriate program elements that can be enhanced using existing DDS technology enablers (TEs). We also propose a flexible PM-DSS integration framework by leveraging existing machine learning operations (MLOps) framework. The proposed integration framework is expected to allow for enhancing the program planning and execution by reducing the program risk using a wide range of DDS TEs, including big data analytics, artificial intelligence, machine learning, deep learning, neural networks, and artificial intelligent