Advanced Modulation Research Articles

Investigation of Optical Cavity Dynamics with Raman and Ytterbium-Doped Gain Media Integration

This study delves into a comprehensive examination of an optical cavity system that integrates Raman and Yb-doped gain media, with a focus on understanding their interactions. The research implies a characterization of each gain medium within the cavity while subjecting them to diverse co-pumping conditions with the other. When the Raman-lasing cavity is co-pumped by exciting the Yb-doped section, the resulting composite laser exhibits significant threshold reductions and there is an optimal co-pumping regime that enhances energy transfer from pump to Stokes. As for the complementary cavity, where the Yb-doped gain is influenced by the co-pumped Raman gain, at moderate pump powers a light-controlling-light behavior phenomenon arises. Within this regime, the 1064 nm signal suppresses the Yb-generated 1115 nm signal, suggesting potential applications in intracavity optical modulation. For higher pump levels, a cooperative effect emerges whereby both lasers mutually enhance each other. Minor variations in the primary 974 nm pump power, even by just a few milliwatts, result in significant capabilities for switching or modulating the Stokes signal. Under these conditions of mutual enhancement, the hybrid optical system validates notable improvements regarding energy transfer efficiency and threshold reduction. This research provides valuable insights into the intricate dynamics of optical cavity systems and reveals promising avenues for applications in advanced optical modulation technologies.

MULTILEVEL INVERTER WITH REDUCED SWITCHES FOR ELECTRICAL VEHICLE

This project focuses on the development of an innovative multilevel inverter designed specifically for electric vehicles (EVs), aiming to reduce complexity and enhance efficiency by minimizing the number of switches in the inverter topology. The conventional multilevel inverter configurations often involve a high count of switches, leading to increased costs, intricate designs, and elevated power losses. In contrast, our approach seeks to optimize the inverter's performance by employing advanced control strategies and modulation techniques while significantly reducing the number of switches. This reduction not only simplifies the design but also lowers production costs and maintenance efforts. Moreover, the project emphasizes the compact integration of the inverter into the EV powertrain, addressing the size and weight constraints of electric vehicles. Through thorough simulations and practical experiments, the proposed multilevel inverter's performance will be evaluated in terms of efficiency, power quality, and reliability. The anticipated outcomes aim to contribute to the advancement of power electronics for sustainable transportation, providing a more efficient, cost- effective, and compact solution tailored to the unique requirements of electric mobility. This research has the potential to significantly impact the field, fostering further innovations in power electronics for clean and efficient transportation solutions .Key Words: Electric vehicle, Inverters, Switches, Efficiency.

Three-Dimensional Space Vector Modulation for Four-Leg Current-Source Inverters

Unbalanced loads are general in uninterruptible power supply (UPS), standalone power generation applications and the failure mode of three-phase inverters. Three-phase four-leg inverters are a well-known solution to handle neutral currents caused by unbalanced loads. In four-leg inverters, three-dimensional space vector modulation (3DSVM) is widely used. However, the 3DSVM of four-leg current source inverter (CSI) has not been systematically studied so far. To fill this gap, this paper proposes a 3DSVM for CSI. The definition of the current vector, the identification of the position of reference vector and the sequence of switches are introduced. Under unbalance loads, the symmetrical load voltage can be realized, which complies with IEEE Std. 1159-2019. The output current can comply with IEEE Std. 519-2014. The proposed 3DSVM has the potential to extend the advanced modulation strategies that have been implemented/ proposed on three-leg CSI.

THE PROBLEM OF ORGANIZING VOLUNTEER ACTIVITIES TO PROVIDE A SAFE EDUCATIONAL ENVIRONMENT FROM THE PERSPECTIVE OF A HEALTHY LIFESTYLE OF STUDENT YOUTH

The well-being of citizens, encompassing their psychological health and adherence to a wholesome lifestyle (HLS), assumes a pivotal role in steering societal rejuvenation, modernization, and transformation. This is particularly vital for nurturing the younger generation and catalyzing the construction of New Kazakhstan, propelling its ascent to the ranks of the world's most developed nations. The presented article emerges from the context of a scientific project with the core objective of delving into the mechanics of orchestrating volunteer initiatives and furnishing scientific and methodological support. The central focus resides in cultivating a psychologically secure educational milieu that champions healthy lifestyles for young individuals within the vocational education system. The paramount relevance and societal gravitas of this issue have enabled the authors to delineate pathways to enhance the training of future teacher-psychologists. The spotlight of this enhancement is cast on the orchestration of volunteer activities that underpin a secure educational environment conducive to fostering healthy lifestyles among young learners. The efficacy of this ongoing endeavor emanates from its multi-pronged approach: a meticulous alignment of developed materials with regional nuances; a concentrated effort on cultivating practical proficiency in the application of health-preserving technologies; and a deliberate emphasis on fostering synergistic collaborations between educational institutions and community support structures. The yield of this rigorous exploration, coupled with scientifically grounded recommendations, serves as a guiding compass for educational managers and instructors within higher and secondary vocational education domains. These insights illuminate pathways for formulating educational programs, crafting elective courses at higher and postgraduate levels, shaping the preparation of bachelor's, master's, and doctoral candidates in the realm of psychological and pedagogical studies. Moreover, they also underpin the creation of advanced training modules tailored for educators in universities and colleges.

SIMULASI SKEMA MODULASI 1024-QAM DAN 4096-QAM PADA SISTEM KOMUNIKASI DIGITAL

Quadrature amplitude modulation (QAM) is a modulation scheme used by digital communication systems to obtain high data rates in bandwidth-limited channels. M-ary QAM schemes with M values greater than 256 are currently used by wireless network standards, such as WiFi 6 and WiFi 7 (final standard will be released in 2024), which use the 1024-QAM and 4096-QAM modulation schemes, respectively. With the continuous development of technology for digital communication systems, both wireline and wireless, it is essential to research regarding advanced modulation schemes. This study discusses the simulation of the 1024-QAM and 4096-QAM modulation schemes using MATLAB software to compare the performance of the two modulation schemes in data transmission through the AWGN channel without using an error control code. The simulation results show that 4096-QAM is superior to 1024-QAM but requires higher Eb/N0 and SNR values. The Eb/N0 value required by the 4096-QAM to achieve a bit error ratio (BER) of 10-4 is 31 dB, while the 1024-QAM requires an Eb/N0 value of 26 dB.

Ultra-long haul high speed 96 × 40 GB/s radio over IsOWC system employing advanced modulation scheme

Abstract This paper successfully demonstrates that point-to-point (P2P) superdense wavelength division multiplexing (SDWDM) based on the radio over intersatellite optical wireless communication (Ro-IsOWC) system of 96 channels separated by a 100 GHz bandwidth with 40 GB/s data rate on each stream at a 10,000 km link range has been designed and investigated. The performance of the proposed work has been evaluated by considering the comparison of advanced modulation schemes such as carrier-suppressed return-to-zero (CSRZ), duobinary return-to-zero (DRZ), and modified duobinary return-to-zero (MDRZ) with an optical preamplifier on the bases of bit error rate (BER), quality factor, eye height, and optical signal-to-noise ratio (OSNR) in the clear weather condition. Moreover, the effects of internal parameters such as transceiver aperture diameter and gain of an optical amplifier also have been analyzed with an optimized modulated system. Apart from this, the system performance also has been assessed under varying data rates and OWC frequency ranges with the best-suited modulation format. It has been found that the CSRZ modulation format has shown robust performance and can achieve BER as better as 10−09 with a 5.5 dB quality factor for all wavelengths. It also has been observed that the quality of the received signal deteriorates below an unacceptable value for data rate and link distance beyond 40 GB/s and 10,000 km, respectively.

Research Data Management Curriculum of the Research Data Services at the University Library Duisburg-Essen

Reproducible and transparent science requires good research data management (RDM). However, researchers are often not familiar with how to appropriately handle research data, publication options, legal aspects, and the multitude of available software tools. To support scientists at the University of Duisburg-Essen in gaining RDM knowledge and competencies, the Research Data Services developed a structured teaching program – the RDM Curriculum – for researchers from different disciplines and career levels.
 The RDM Curriculum is separated into a basic and an advanced module. In the basic module, participants can select from three full-day courses that address the basics of RDM with different disciplinary focus. The advanced module consists of various 2 hour online hands-on courses on diverse RDM topics and tools. As incentive to attend multiple courses, participants can earn the RDM Badge of the University Library Duisburg-Essen. The curriculum program is complemented by specialized RDM courses and self-learning materials.
 With the RDM Curriculum, we created an easily accessible and individually selectable RDM course offer that enables researchers to gain discipline-specific and individually relevant RDM knowledge. The curriculum structure can gradually be expanded to further target groups (e.g. students) and allows for continuously adding new contents in the advanced module. The high request for the RDM Badge after the first iteration of the RDM Curriculum in the winter semester 2022/2023 confirmed the positive effect of incentives. Consequently, we are planning to establish a Data Champion Award as appreciation of RDM achievements and to showcase best-practice examples at the University of Duisburg-Essen.

A 3.84-Gbit/s FBMC-NOMA-based VLC transmission system using DAC-based pre-equalization and DHT precoding technique

The transmission data rate of visible light communication (VLC) systems is mainly limited by the modulation bandwidth of light-emitting diodes (LEDs). Pre-equalization (PE) techniques in combination with advanced modulation formats have been employed to enhance the data rate of LED-based VLC systems. In this paper, we experimentally demonstrated a high spectral-efficiency filter bank multi-carrier (FBMC) and non-orthogonal multiple access (NOMA)-based VLC transmission system using digital-to-analog converter-based PE (DAC-PE) and discrete Hartley transform (DHT) precoding technique. Two PE techniques, i.e., DAC-PE and digital pre-equalization (DPE), are comparatively investigated in both orthogonal frequency-division multiplexing (OFDM)-NOMA-based VLC systems and FBMC-NOMA-based VLC systems. The results exhibit that, with the help of DHT precoding, the DAC-PE scheme outperforms the DPE scheme at the optimal power ratios (PRs). Besides, the FBMC-NOMA scheme can provide better bit error rate (BER) performance and higher data rate than OFDM-NOMA. At the optimal PR of 6 dB, the FBMC-NOMA signal with a record net data rate of 3.84 Gbit/s after 2.3-m free-space link transmission can be achieved with the BER below 3.8 × 10−3.

Dual-Site Förster Resonance Energy Transfer Route of Upconversion Nanoparticles-Based Brain-Targeted Nanotheranostic Boosts the Near-Infrared Phototherapy of Glioma.

Glioblastoma multiforme (GBM) is the most common malignant brain tumor with low survival, primarily due to the blood-brain barrier (BBB) and high infiltration. Upconversion nanoparticles (UCNPs)-based near-infrared (NIR) phototherapy with deep penetration is a promising therapy method against glioma but faces low photoenergy utilization that is induced by spectral mismatch and single-site Förster resonance energy transfer (FRET). Herein, we designed a brain-targeting NIR theranostic system with a dual-site FRET route and superior spectral matching to maximize energy utilization for synergistic photodynamic and photothermal therapy of glioma. The system was fabricated by Tm-doped UCNPs, zinc tetraphenylporphyrin (ZnTPP), and copper sulfide (CuS) nanoparticles under multioptimized modulation. First, the Tm-doping ratio was precisely adjusted to improve the relative emission intensity at 475 nm of UCNPs (11.5-fold). Moreover, the J-aggregate of ZnTPP increased the absorption at 475 nm (163.5-fold) of monomer; both together optimize the FRET matching between UCNPs and porphyrin for effective NIR photodynamic therapy. Simultaneously, the emission at 800 nm was utilized to magnify the photothermal effect of CuS nanoparticles for photothermal therapy via the second FRET route. After being modified by a brain-targeted peptide, the system efficiently triggers the synergistic phototherapy ablation of glioma cells and significantly prolongs the survival of orthotopic glioma-bearing mice after traversing the BBB and targeting glioma. This success of advanced spectral modulation and dual-site FRET strategy may inspire more strategies to maximize the photoenergy utilization of UCNPs for brain diseases.

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC) , which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data.

A Survey on Multi-Active Bridge DC-DC Converters: Power Flow Decoupling Techniques, Applications, and Challenges

Multi-port DC-DC converters are a promising solution for a wide range of applications involving multiple DC sources, storage elements, and loads. Multi-active bridge (MAB) converters have attracted the interest of researchers over the past two decades due to their potential advantages such as high power density, high transfer ratio, and galvanic isolation, for example, compared to other solutions. However, the coupled power flow nature of MAB converters makes their control implementation difficult, and due to the multi-input, multi-output (MIMO) structure of their control systems, a decoupling control strategy must be designed. Various control and topology-level strategies are proposed to mitigate the coupling effect. This paper discusses the operating principles, applications, methods for analyzing power flow, advanced modulation techniques, and small signal modelling of the MAB converter. Having explained the origin of cross-coupling, the existing power flow decoupling methods are reviewed, categorized, and compared in terms of effectiveness and implementation complexity.

Exploration of the HBSM MMC Five-Level Inverter for D-STATCOM Application

This paper presents the concert exploration of a Modular Multilevel Converter (MMC) and its application for D-STATCOM. The Half Bridge (HB) sub-module is adopted in the MMC due to its simplicity and low conduction losses. An advanced modulation technique of the Phase Disposition (PD) scheme is applied for the control of the HB MMC switches. The converters are facing DC voltage balance problems under non-linear load conditions. The Proportional and Integral (PI) controller is adopted for the balance of DC voltage. Performance parameters such as apparent power, active power, reactive power, power factor, DC voltage balance, and Total Harmonics Distortion (THD) are presented and compared with the ones of the traditional two-level converter. The proposed scheme is simulated in MATLAB/Simulink.

A numerical study of the nanofluid mixtures inside a Buoyancy-driven cavity in the presence of a variable magnetic field

While magnetic flow applications have become a topic of great interest for decades, the coupling between fluid flow and electromagnetic forces is not straightforward. Therefore, the necessity of a robust guideline to simulate these types of problems has become apparent. Moreover, the heat transfer capabilities and the interaction between electromagnetic forces such as Lorentz and Kelvin require further investigation.In this regard, two objectives are pursued to address the noted issues. First, a robust and flexible solution framework using User-Defined-Functions (UDF)s is presented so that magnetic flow applications can be investigated without software limitations such as discretization scheme, solution setup, and simultaneous use of other advanced modules. Next, the impact of electromagnetic forces on streamlines and isotherms has been studied, and the forces’ area of influence is carefully investigated.Based on the results, the introduced framework has successfully predicted consistent results for three previous studies. Next, by considering a wide range of Rayleigh (Ra), Hartmann (Ha), and Magnetic (Mn) numbers, valuable revelations regarding the force interactions, thermofluidic properties, and force area of influence were revealed. Finally, the situations in which the Kelvin and Lorentz forces are influential are identified based on dimensional analysis.

Foreground Fusion-Based Liquefied Natural Gas Leak Detection Framework From Surveillance Thermal Imaging

A leak detection and repair survey (LDAR) is essential to ensure a reliable and safe liquefied natural gas (LNG) supply. Modern LDAR systems deploy numerous fixed thermal imaging devices to automatically monitor the risk of potential leaks empowered by computational intelligence frameworks. Existing frameworks employ either background subtraction-based (BGS-based) or deep neural network-based (DNN-based) frameworks for LNG leak detection from thermal images. However, the BGS-based frameworks feature high sensitivity to perceive LNG emissions with low precision. On the contrary, the DNN-based frameworks can precisely classify the LNG leak after training while the sensitivity is low. Additionally, conventional DNN-based frameworks are difficult in modeling non-rigid objects such as LNG gas due to limited perceptive fields. Therefore, this study proposes a hybrid framework, namely foreground fusion-based gas detection (FFBGD), combining the advantages of BGS-based and DNN-based detectors for improved detection robustness through newly introduced concept of information fusion to LNG industries. Specifically, a foreground fusion network (FFN) is designed to fuse information of original thermal and foreground images after BGS based on the visual attention mechanism. Meanwhile, several advanced modules, i.e. deformable convolution, feature pyramid network, and cascade region-of-interest (ROI) head are adopted to enhance leak detection by offering better perceptive fields. Extensive experiments are carried out in this study to demonstrate the significant improvement brought by the proposed FFBGD over leak detection accuracy and robustness. Hence, the proposed solution can be deployed in energy facilities and enable reliable visual surveillance of LNG leaks.

Improvement of silicon heterojunction module efficiency using fast illumination post-treatments

Intense illumination treatments on silicon heterojunction cells (SHJ) have recently gained interest to improve the final cell efficiency and are now being implemented into cell manufacturing tools. However, additional efforts are still required to clarify the robustness of such approach. Indeed, it has been reported that the positive effects of the intense illumination treatments applied to cells may be partly lost during module assembly. Therefore, in order to circumvent these potential issues, we reveal herein a broad investigation on the interest of intense illumination treatments directly at module level, using an experimental lab tool provided by Applied Materials, Baccini (Italy). Efficiency gains up to +0.3 %abs have been achieved within a treatment process time compatible with industry requirements. The gains remained stable for two months after a slight decrease during the first days (−0.05 %abs). Moreover, the reliability tests against UV, thermal cycling and damp heat did not reveal any detrimental influence of the post-treatments applied to the modules, further highlighting the fast illumination post-treatment as promising candidate for the next generation of high technologically advanced photovoltaic modules manufacturing production lines.

Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter With Minimized Current Stress

The dual active bridge (DAB) converter has been popular in many applications for its outstanding power density and bidirectional power transfer capacity. Up to now, triple phase shift (TPS) can be considered as one of the most advanced modulation techniques for DAB converter. It can widen zero voltage switching range and improve power efficiency significantly. Currently, current stress of the DAB converter has been an important performance indicator when TPS modulation is applied for smaller size and higher efficiency. However, to minimize the current stress when the DAB converter is under TPS modulation, two difficulties exist in analysis process and realization process, respectively. Firstly, three degrees of modulation variables in TPS modulation bring challenges to the analysis of current stress in different operating modes. This analysis and deduction process leads to heavy computational burden and also suffers from low accuracy. Secondly, to realize TPS modulation, if a lookup table is adopted after the optimization of modulation variables, modulation performance will be unsatisfactory because of the discrete nature of lookup table. Therefore, an AI-based TPS modulation (AI-TPSM) strategy is proposed in this paper. Neural network (NN) and fuzzy inference system (FIS) are utilized to deal with the two difficulties mentioned above. With the proposed AI-TPSM, the optimization of TPS modulation for minimized current stress will enjoy high degree of automation which can relieve engineers’ working burden and improve accuracy. In the end of this paper, the effectiveness of the proposed AI-TPSM has been experimentally verified with a 1 kW prototype.

Optical channel aggregation based on modulation format conversion by coherent spectral superposition with electro-optic modulators

Spectrally efficient modulation formats become essential for optical network scaling as the demand for routed data streams exceeds the present wavelength-division multiplexing systems’ throughput. However, achieving high spectral efficiency at high data rates requires complex and bandwidth-intensive electronics. In this study, we propose an all-optical aggregation scheme that combines multiple low spectral efficiency optical wavelength channels from an optical frequency comb based transmitter into fewer channels with higher spectral efficiency. Our method utilizes coherent spectral superposition and optical vector summation, eliminating the need for optical nonlinearities and relying on linear signal processing with an electro-optic modulator. By adjusting the phase of the radio frequency signal driving the modulator, we can easily achieve the required optical phase tuning for vector addition in the I-Q plane. Through experimental demonstrations, we show that the proposed approach enables the generation of 10 GBd PAM-4 and 10 GBd quadrature phase shift keying (QPSK) signals by aggregating two 10 GBd binary phase shift keying signals. Similarly, we aggregate two 10 GBd QPSK signals into one 10 GBd quadrature amplitude modulation-16 (QAM-16) signal. The experiments employ both conventional and sinc-shaped Nyquist signals. Our in-line, all-optical aggregation concept significantly enhances operational capacity while reducing complexity. It offers a promising solution for realizing flexible integrated optical transmitters for advanced modulation format signals using lower-quality electronics. Additionally, it aligns with the requirements of future dynamically reconfigurable optical networks that leverage spectral traffic aggregation. Given its reliance on linear signal processing with an electro-optic modulator, the integration of the method into any integrated photonic platform is straightforward.

An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes

This paper proposes an improved model based on YOLOv5s, specifically designed to overcome the challenges faced by current target detection algorithms in the field of electric bike helmet detection. In order to enhance the model’s ability to detect small targets and densely populated scenes, a specialized layer dedicated to small target detection and a novel loss function called Normalized Wasserstein Distance (NWD) are introduced. In order to solve the problem of increasing model parameters and complexity due to the inclusion of a small target detection layer, a Cross-Stage Partial Channel Mixing (CSPCM) on top of Convmix is designed. The collaborative fusion of CSPCM and the Deep Feature Consistency (DFC) attention mechanism makes it more suitable for hardware devices. In addition, the conventional Nearest Upsample technology is replaced with the advanced CARAFE Upsample module, further improving the accuracy of the model. Through rigorous experiments on carefully constructed datasets, the results show significant improvements in various evaluation indicators such as precision, recall, mAP.5, and mAP.95. Compared with the unmodified YOLOv5s algorithm, the proposed enhanced model achieves significant improvements of 1.1%, 8.4%, 5.2%, and 8.6% on these indicators, respectively, and these enhancements are accompanied by a reduction of 778,924 parameters. The experimental results on our constructed dataset demonstrate the superiority of the improved model and elucidate its potential applications. Furthermore, promising improvements for future research are suggested. This study introduces an efficient approach for improving the detection of electric bike helmets and verifies the effectiveness and practicality of the model through experiments. Importantly, the proposed scheme has implications for other target detection algorithms, especially in the field of small target detection.

Enhancing energy efficiency of chemical absorption-based CO2 capture process with advanced waste-heat recovery modules at a high capture rate

A commercial-ready amine-based CO2 capture process (CCP) restricts its potential for industrial deployment at a high capture rate to achieve a net-zero carbon target due to its energy-intensive nature. This study aims to enhance the energy efficiency of a monoethanolamine (MEA)-based CCP, operating at a 95% capture rate, by developing advanced process modules that utilize low-grade waste heat. To establish a foundation for designing cascade modules that can recover energy from all available waste heat, the waste heat characteristics were analyzed, and the feasibility of four single modules was explored to recover the latent heat in a stripped gas. Subsequently, this study comparatively investigated the equivalent electrical works of the two designed cascade modules combined with lean vapor compression (LVC). A techno-analysis indicated that cascade modules provided outstanding energy-saving performance ranging from 12.27% to 24.55%, with an average improvement of 50% over single modules. Although the cascade modules combined with LVC showed similar energy-saving performances, higher robustness for equivalence factor (EF) and more energy-savings were discovered in the electricity generation and direct heat supply strategies, respectively. Economic analysis indicated that advanced process modules were economically feasible, with a savings-to-investment ratio (SIR) exceeding 1. The SIR and payback period (PBP) depended on EF and electricity prices. Significantly, the cascade module for direct heat supply emerged as the most appropriate in terms of both economic viability and energy-saving efficiency in amine-based CCPs, particularly at a higher EF.

A Comprehensive Analysis of PAPR Reduction in Scrambled UFMC and OFDM Using Artificial Bee Colony Algorithm for 5G Communications

ABSTRACT With the advent of 5G communications, the demand for high data rates and improved spectral efficiency has necessitated the exploration of advanced modulation techniques. Orthogonal Frequency Division Multiplexing (OFDM) and Universal Filtered Multi-Carrier (UFMC) are two prominent modulation schemes for 5G systems. However, both OFDM and UFMC suffer from a high Peak-to-Average Power Ratio (PAPR), leading to signal distortion and inefficient power amplification. The Partial Transmit Sequence (PTS) approach is a promising strategy to minimize PAPR in OFDM and is applied to UFMC to minimize PAPR in UFMC and is known as scrambled UFMC (S-UFMC). PTS, however, involves an extensive search to identify the best phase factors that increase computational complexity. The artificial bee colony algorithm is proposed to reduce the computational complexity S-UFMC and OFDM. A novel modulation scheme, amplitude phase shift keying (APSK), is used instead of quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) in S-UFMC and OFDM, which has better performance than conventional modulation schemes. In this research, OFDM, PTS OFDM, UFMC and S-UFMC are compared and it is found that the proposed scrambled UFMC with APSK modulation using ABC algorithm performs better than OFDM and UFMC in terms of PAPR, spectral efficiency and computational complexity.