FPGA-based Coherent MSK Spread Spectrum Modem for Small Satellites TT&C Transponders

In low earth orbit (LEO) satellite communication links it is required to employ modulation/demodulation techniques to achieve transmission with minimum power and efficient usage of spectrum (according to international telecommunication union (ITU) and consultative committee for space data systems (CCSDS) regulations and recommendations) with minimum bit error rate (BER) at the receiver. Minimum shift keying (MSK) meets all these requirements. It is required also to measure the varying distance between the satellite and the ground station (range) and the velocity of the satellite (range rate) to facilitate the tracking of the satellite by the ground station antenna tracking system and therefore, a direct sequence spread spectrum (DS-SS) technique is used. Thus, coherent MSK DS-SS modem is chosen in this paper for LEO communication links. This paper investigates design, implementation and testing of a FPGA-based coherent MSK DS-SS modem suitable for small satellites TT&C transponders. The modem includes a MSK modulator, an automatic gain control (AGC), and MSK DS-SS demodulator/synchronizer (where a proposed novel phase ambiguity solver algorithm is presented). Demodulator performance is evaluated by adding band-limited (nearly white within the signal bandwidth) Gaussian noise to the MSK DS-SS modulated signal (resulting in Eb/No near 0dB) and measuring the BER and the phase variances of the synchronized carrier and the clock with its operating Eb/No for the extracted chips, which show good performance

Digital Predistortion for Wideband High Efficiency RF Power Amplifiers for High Throughput Satellites

In satellite communications, there is considerable increasing demand to support higher data throughput on necessarily pre-allocated bandwidth channels. In communication payloads, the DC-to-RF power conversion efficiency is crucial and most of the DC power is consumed by the RF Power Amplifier (PA). Thus, maximizing the PA efficiency while maintaining low distortion is key. Both power and bandwidth efficiency could be increased by employing digital pre-distortion (DPD). This paper focuses on state-of-the-art DPD techniques and possible developments to fit in satellite communications. The toleration of effects of temperature, supply voltage, and load mismatch variations, are investigated. This research suggests that there are benefits to be gained; employing spectrally efficient modulation techniques and transmitting a high fidelity signal to result in less expensive space and ground segment transmitters. A proof-of-concept by simulation is presented for ultra-wideband applications. A novel DPD system architecture is proposed where envelope tracking (ET) of the driver amplifier (DA) and load modulation of the PA are used to maximize the overall PAE while high PAPR signals can be used. To the best of the authors’ knowledge, this is the first time that DPD has been proposed for large fractional bandwidth high PAPR signals for satellite communications

An approach for optimizing multi-objective problems using hybrid genetic algorithms

Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence

Erratum: An approach for evolving transformation sequences using hybrid genetic algorithms [International Journal of Computational Intelligence Systems, (2020) 13(1), (223-233), 10.2991/ijcis.d.200214.001)

© 2020 The Authors. Published by Atlantis Press B.V. There is an error in affiliation #1 of the published version of this article. The correct affiliation #1 should be: “Department of Computer Science, College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT)” The authors are sorry for any inconveniences caused by this error

An approach for optimizing multi-objective problems using hybrid genetic algorithms

© 2020, The Author(s). Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence

An approach for evolving transformation sequences using hybrid genetic algorithms

© 2020 The Authors. The digital transformation revolution has been crawling toward almost all aspects of our lives. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The vast field of software engineering that has witnessed a significant progress in the past years is responsible for this form of digital transformation. Software development as well as other branches of software engineering has been affected by this progress. Developing applications that run on top of mobile devices requires the software developer to consider the limited resources of these devices, which on one side give them their mobile advantages, however, on the other side, if an application is developed without the consideration of these limited resources then the mobile application will neither work properly nor allow the device to run smoothly. In this paper, we introduce a hybrid approach for program optimization. It succeeded in optimizing the search process for the optimal program transformation sequence that targets a specific optimization goal. In this research we targeted the program size, to reach the lowest possible decline rate of the number of Lines of Code (LoC) of a targeted program. The experimental results from applying the hybrid approach on synthetic program transformation problems show a significant improve in the optimized output on which the hybrid approach achieved an LoC decline rate of 50.51% over the application of basic genetic algorithm only where 17.34% LoC decline rate was reached

Assessing emotional intelligence domains and levels in substance use disorders

Abstract Background Many individuals with substance use disorders face challenges in their social interactions and often have strained relationships with peers. Challenges related to problem-solving, stress management, and impulsivity often contribute to their substance use disorders. Emotional intelligence plays a pivotal role in assisting individuals with substance use disorders in coping with stress, enhancing peer connections, resolving problems, and preventing relapse. Consequently, our study aimed to assess emotional intelligence in individuals with substance use disorders and explore the factors influencing it. A cross-sectional study compared 50 individuals with substance use disorders and 50 healthy individuals. We assessed various factors, including clinical data, sociodemographic variables, family socioeconomic status, Addiction Severity Index (ASI) scores, and Emotional Intelligence (EI) scale scores. Results Individuals with substance use disorders had significantly lower mean scores in total EI and its subscales compared to the healthy control group. Additionally, a higher percentage of individuals with substance use disorders exhibited low EI levels, while healthy individuals demonstrated better EI. Furthermore, there was a substantial association between higher ASI scores in individuals with substance use disorders and lower EI scores. Conclusions Lower EI scores are associated with an increased risk of substance use disorders. Also, can contribute to difficulties in impulse control, and challenges in managing relationships and stress. These findings underscore EI crucial role in preventing and treating substance use disorders