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It is well established that several Homo sapiens populations experienced admixture with extinct human species during their evolutionary history. Sometimes, such a gene flow could have played a role in modulating their capability to cope with a variety of selective pressures, thus resulting in archaic adaptive introgression events. A paradigmatic example of this evolutionary mechanism is offered by the EPAS1 gene, whose most frequent haplotype in Himalayan highlanders was proved to reduce their susceptibility to chronic mountain sickness and to be introduced in the gene pool of their ancestors by admixture with Denisovans. In this study, we aimed at further expanding the investigation of the impact of archaic introgression on more complex adaptive responses to hypobaric hypoxia evolved by populations of Tibetan/Sherpa ancestry, which have been plausibly mediated by soft selective sweeps and/or polygenic adaptations rather than by hard selective sweeps. For this purpose, we used a combination of composite-likelihood and gene network-based methods to detect adaptive loci in introgressed chromosomal segments from Tibetan WGS data and to shortlist those presenting Denisovan-like derived alleles that participate to the same functional pathways and are absent in populations of African ancestry, which are supposed to do not have experienced Denisovan admixture. According to this approach, we identified multiple genes putatively involved in archaic introgression events and that, especially as regards TBC1D1, RASGRF2, PRKAG2, and KRAS, have plausibly contributed to shape the adaptive modulation of angiogenesis and of certain cardiovascular traits in high-altitude Himalayan peoples. These findings provided unprecedented evidence about the complexity of the adaptive phenotype evolved by these human groups to cope with challenges imposed by hypobaric hypoxia, offering new insights into the tangled interplay of genetic determinants that mediates the physiological adjustments crucial for human adaptation to the high-altitude environment. [ABSTRACT FROM AUTHOR]

Radiative cooling has been recently intensively explored for thermal management and enhancing energy efficiency. Yet, traditional materials with singular emissivity fall short in dynamic thermal management, highlighting the need for materials that can adjust their thermal radiation in real time. Active modulation methods, requiring external stimuli such as mechanical stretch, electric potential, or humidity change, offer adaptability but can increase energy use and complexity. Passive approaches, using materials' inherent thermal‐responsive properties, face manufacturing and scalability challenges. Here, a scalable yet effective passive approach is introduced for adaptive thermal modulation based on gold (Au) and liquid crystal elastomer (LCE) with a reversible response to environmental temperature changes. This modulator enables a "low thermal resistance" state through actuation‐induced microcracks that expose a high‐emissivity polymer substrate, and a "high thermal resistance" state by closing these microcracks and forming a high thermal resistance air gap between the modulator and the target object. The flexible design and fixed external dimensions of the Au‐LCE thermal modulator make it adaptable to various surface geometries. Furthermore, by adjusting the LCE's chemical composition, the modulator's transition temperature can be tailored, broadening its applications from enhancing building energy efficiency to improving clothing thermal comfort. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output systems with spatial modulation has evolved into an energy efficient and less complex wireless transmission system due to the use of limited radio frequency chains. In this work, a novel physical layer security scheme with enhanced spatial modulation (ESM) and secret key generation using multi-level adaptive threshold binary-coded decimal quantization algorithm is introduced. The proposed method, as SPACESM, enhances spectral efficiency using ESM and achieves confidentiality by adaptively selecting and shuffling the codebook in ESM. For selection of ESM codebook, SPACESM uses physical layer signature called channel signal to noise ratio (SNR) and for shuffling of codebook and rotation of transmit vector, secret key is generated at the transmitter and receiver separately by threshold-based quantization algorithm, which uses channel state information. Adaptive modulation technique selects the modulation order for different SNR. The performance of the proposed method is evaluated in terms of confidentiality, spectral efficiency, and robustness of secret key through MATLAB simulation.The proposed SPACESM technique provides 2 bpcu improvement for both N t = 2 and N t = 4 with M = 64 than the existing SM. Also it provides 3 dB and 4 dB SNR gain than SM for N t = 2 and N t = 4 respectively. Similarly, it provides 2 dB and 4 dB SNR gain than the SMX technique for N t = 2 and N t = 4 respectively. In summary, the proposed method outperforms than the SMX and SM technique in all aspects. [ABSTRACT FROM AUTHOR]

The digital voice is multimedia content of great importance, given the range of applications where it can be found. This paper addresses the shortcomings of existing voice authentication algorithms, presenting a completely blind speech authentication and recovery method based on fragile watermarking using the Least Significant Bit (LSB) method. This scheme obtains a compressed version of the original speech signal by Adaptive Differential Pulse Code Modulation (ADPCM) coding and the Discrete-Time Wavelet Transform (DTWT). Authentication bits are then generated by the SHA256 hash function, and the watermark is afterward embedded in the last three LSBs of the original audio samples. Experimental results evaluated on five different audio databases, each comprising speech signals recorded in different situations, contexts, and languages, have demonstrated a high embedding payload and imperceptibility of the watermark, obtaining an average Signal-to-Noise Ratio (SNR) value above 40 d B . Furthermore, the proposed method demonstrates a strong ability to accurately locate and restore up to 50% of a speech signal that has been tampered with, using no additional information. Moreover, the recovered speech signal is intelligible and has an SNR value higher than other recovery schemes, justifying the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions. [ABSTRACT FROM AUTHOR]

Ground-based thermal infrared observations face substantial challenges in correcting the predominant background emitted as thermal radiation from the atmosphere and the telescope itself. With the upcoming 40 m class ELTs, unprecedented sensitivities from ground will be reached, underlining the need of even more sophisticated background correction strategies. This study aims to investigate the impact of thermal backgrounds on ground-based observations and identify possible limiting factors in dedicated correction strategies. We evaluate temporal and spatial characteristics of the thermal background in direct imaging data obtained with different telescopes and observation modes. In particular, three distinct data sets, acquired using VLT/NACO and KECK/NIRC2, are analyzed. Our analysis reveals that the observations are not fully photon shot noise limited, but exhibit additional sensitivity losses caused by imperfect background compensation in the different data sets. We identify correlations between background fluctuations and the activity of the adaptive optics system. We hypothesize that the pupil modulation of the adaptive optics mirrors introduces high frequency spatial and temporal fluctuations to the background, which could ultimately constrain the detection limit if they are not compensated adequately. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the diverse demands of mobile users and vertical industries, it is crucial for 5G-advanced (5G-A) networks to exhibit flexibility in system design across both the physical and higher layers. A single-frame structure alone cannot provide the necessary flexibility to accommodate multiple traffic modes. Therefore, we investigate a heterogeneous frame structure that meets the requirements. However, this structure will cause severe cross-link interference (CLI) and degrade the performance. To solve this problem, four CLI mitigation methods are proposed: slot level adaptive modulation and coding (AMC) scheduling (method 1), interference mitigation algorithm (method 2), adaptive slot turn-off (method 3), and edge user adaptive scheduling (method 4). The experimental results show that the CLI problem can be completely mitigated by deployed method 3 in a macro-base station (BS) with a low load scenario, the micro-BS uplink (UL) throughput was improved by 75.8%. Otherwise, with macro-BS in the high load scenario, when method 4 was implemented in macro-BS and methods 1 and 2 were deployed in micro-BS, a nearly complete CLI mitigation effect was obtained, reflecting that the micro-BS UL throughput increased to 57.4%. These methods are elaborated upon and evaluated within practical 5G-A networks, and the experimental results not only demonstrate the viability and efficacy of the above methods but also highlight their potential applications in future usage scenarios. [ABSTRACT FROM AUTHOR]

The quadratic buck converter is renowned for its steep step‐down capability. It encounters increased losses due to its high component count. In scenarios with light loads, switching losses become the dominant factor. Additionally, the presence of two right‐half plane zeros impairs transient response, even at high‐frequency switching operations. Incorporating this converter into the digital domain introduces an undesired phenomenon known as subharmonic oscillation, rendering the system unstable, albeit potentially mitigated over time—a drawback particularly undesirable for converters tasked with rapid load dynamics. This paper introduces an adaptive pulse skipping modulation scheme to control metal–oxide–semiconductor field‐effect transistor (MOSFET) switching actions, enhancing overall efficiency in discontinuous conduction mode. Furthermore, the effects of right half‐plane (RHP) zeros on stability are analyzed within these switching schemes. The proposed scheme is integrated with voltage‐mode control. Simulation and theoretical analyses are conducted to validate this converter. A flat efficiency of 89% to 86% for the load range of 25 to 700 mA is obtained, outperforming other existing schemes. The results demonstrate that the adaptive pulse modulation scheme effectively improves efficiency and stability in discontinuous conduction mode converters. This research provides valuable insights for optimizing power electronics systems with varying load dynamics. [ABSTRACT FROM AUTHOR]

Featured Application: This study will focus on enhancing vehicle design by integrating advanced multisensory and culturally adaptive emotion modulation systems to meet the diverse needs of drivers globally and improve overall driving safety. Road accidents are caused by multiple factors. Aggressive driving and traffic violations account for 74% of road traffic accidents. In total, 92% of fatalities occur in low- and middle-income countries. Drivers' emotions significantly influence driving performance, making emotional modulation critical during vehicle interaction. With the rise of smart vehicles, in-vehicle affective computing and human-centered design have gained importance. This review analyzes 802 studies related to driver emotional regulation, focusing on 74 studies regarding sensory stimuli and cultural contexts. The results show that single-sensory methods dominate, yet multisensory approaches using auditory and visual elements are more effective. Most studies overlook cultural factors, particularly the differences in East–West cultural values, indicating a need to tailor modulation methods based on cultural preferences. Designs must emphasize adaptability and cultural consistency. This review aims to analyze driver emotional modulation thoroughly, providing key insights for developing vehicle systems that meet the diverse emotional and cultural needs of global drivers. Future research should focus on creating multisensory emotional modulation systems that offer positive reinforcement without causing excessive relaxation or aggression, accommodating subtle cultural and individual differences, thus enhancing the safety of autonomous driving. [ABSTRACT FROM AUTHOR]

Automatic Modulation Format Identification(AMFI) is an important issue in Visible Light Communication (VLC). There are several factors that affect the performance of vehicular VLC (V-VLC) systems. To accomplish the required communication quality for different traffic scenarios, It needs adaptive modulation techniques. Adaptive modulation requires modulation format identification at the receiver to avoid the overhead required to determine the proper modulation format. In this paper, we propose an AMFI scheme based on Deep Learning (DL) for Infrastructure-to-Vehicle (I2V) VLC. Our scheme utilizes the Hough transform to project the constellation diagram of the received modulated signals onto another space to extract features efficiently. Hough transform is estimated for eight different VLC modulation formats, including Q/8/16-Phase Shift Keying (PSK) and 4/16/32/64-Quadrature Amplitude Modulation (QAM) at signal to noise ratio (SNR) ranging from 5 to 25 dB. Conventional classifiers such as Alexnet, Sequeeznet and Googlenet are used to perform the task of AMFI. A study of the impact of the weather conditions and the communication distance on the accuracy of the used classifiers is introduced. The obtained results prove that the proposed scheme can identify the modulation format with high classification accuracy up to 100% at lower SNR levels and different weather conditions. [ABSTRACT FROM AUTHOR]

Free-space optics (FSO)-based non-terrestrial networks (NTN) have garnered significant attention as a potential technology for forthcoming 6G wireless communications due to their exceptional data rate and extensive global coverage capability. Nevertheless, atmospheric attenuation, cloud attenuation, geometric loss, and atmospheric turbulence present numerous difficulties in developing these networks. To cope with these difficulties, we propose to apply a joint adaptive modulation and power control (JAMPC) scheme to FSO-based NTN. Our proposed JAMPC algorithm aims to enhance energy efficiency while guaranteeing the targeted outage probability, bit-error rate, and the required data rate. We develop mathematical models and derive closed-form expressions to implement the proposed algorithm and solve the optimization problem. The numerical results confirm that the JAMPC scheme helps NTN provide better energy efficiency and the ability to adapt to various channel conditions.

Smart windows with excellent near-infrared (NIR) light shielding capacity and controlled visible light transmittance are expected to reduce building energy consumption and improve indoor comfort. However, the ability of current thermochromic materials to regulate solar gain with the tunable extinction of phase-change materials is still far from optimum. In this work, an fluorine (F)-doped ATO (FATO) nanocrystals (NCs) are designed as a photothermal material to optimize the solar modulation of thermochromic hydrogels. F ion doping can modify the electronic structure of ATO, resulting in a high concentration of oxygen vacancies and free electrons, thereby enhancing the NIR absorption performance and photothermal conversion performance of the ATO NCs. On this premise, the designed smart window based on FATO NCs and thermochromic hydrogels exhibits the remarkable features of excellent NIR shielding, high initial visible transmittance and fast response speed. The window is capable of shielding 99.5% of the solar radiation from 300 to 2500 nm and permitting 75.0% of initial visible light. Moreover, the simulated indoor temperature of the smart window was 17.2 °C lower compared with the black one, demonstrating potentials in practical application of building energy saving. [Display omitted] • Novel chain-like FATO NCs were prepared by precipitation-hydrothermal method. • F-doping optimized electronic structure of ATO and improved the LSPR absorption. • The fabrication of FATO-PNC hydrogel provides a novel idea for smart window. • The smart window could shield 99.5% of solar radiation after optical switching. [ABSTRACT FROM AUTHOR]

Clustering is one of the most promising approaches to reducing energy usage in Wireless Sensor Networks, where power remains a bottleneck (WSNs). The vast majority of advances in energy efficiency result in exorbitant computing costs. Because of its superiority in simulating human decision making and its capacity to turn several inputs into a single output, the fuzzy-logic based clustering algorithm is preferred over others. This paper proposes a technique to energy-efficient clustering based on fuzzy logic (FLEE). Energy consumption is a significant barrier for geographically dispersed wireless sensor networks (WSNs). Because of the rapid improvement of energy harvesting technology, a novel approach known as simultaneous wireless information and power transmission (SWIPT) has emerged as a hot research topic due to its use in tackling the energy scarcity problem in WSNs. The bulk of existing SWIPT systems rely on time switching (TS) or power splitting due to their high complexity, lack of flexibility, and implementation issues (PS). In this paper, we provide a novel Fuzzy Logic-based Energy Efficient—SWIPT approach for adaptively dividing data packets into energy data packets and information data packets using fountain codes and real-time node decoding status information. The modulation switching thresholds and transmit power allocation at the SWIPT transmitter, as well as the power splitting ratios at the SWIPT receiver, are jointly changed to maximise the spectrum efficiency of wireless information transfer in wireless sensor networks. The numerical demonstration of the FLEE-SWIPT performance of many fixed modulation schemes under different fading situations. The FLEE-SWIPT transceiver with adaptive modulation has been proved to be useful. The results show an increase in Network Lifetime of 12.342 s, an increase in Network Throughput of 1579.41 kbps, a decrease in Energy consumption of 23.789 J, a decrease in Packet Loss Ratio of 38.61%, a decrease in Packet Delivery Ratio of 96.33%, a decrease in Encryption Time of 2.987 s, a decrease in Decryption Time of 3.567 s, and a decrease in Execution. [ABSTRACT FROM AUTHOR]

Introduction Allergic Rhinitis is defined clinically as having two or more symptoms of anterior or posterior rhinorrhoea, sneezing, nasal blockage and or itching of nose during two or more consecutive days for more than one hour on most days. Exercise induces modulation in innate and adaptive immune system, dependent on host defence, activity level and disease susceptibility. This study was conducted to evaluate the effect of exercise on allergic rhinitis. Methods We report a comparative study conducted among 43 MBBS students of Mysore medical college and research institute. The study was a conducted from March 2022 to December 2022. The study subjects were selected based on SFAR (score for allergic rhinitis) score and they were divided into two groups. Those students with allergic rhinitis and performed exercise of 30 minutes for 5 days a week were considered cases and those who didn't exercise but had allergic rhinitis were placed in the control group. They were followed up for a period of 9 months. The effect of exercise on allergic rhinitis was determined by SFAR score obtained by the students at the end of the study period. Result 43 MBBS students were included in this study. 22 were cases (those with allergic rhinitis and performed exercise) and 21 were controls (those with allergic rhinitis but didn't exercise). 13 students (59.1%) from the case group reported improvement in their allergic symptoms with exercise. 8 (61.5%) out of the 13 students who reported improvement were involved in indoor sports activity. 5 (22.7%) of the cases reported no change in severity of symptoms with exercise. While 4 (18.2%) of the cases reported worsening of their symptoms with exercise. Conclusion During exercise, autonomic reflexes improve nasal efficiency. In dynamic exercise training due to an increase of nasal sympathetic activity, venous sinusoids constrict. This study showed that exercise has a beneficial effect on allergic rhinitis. [ABSTRACT FROM AUTHOR]

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence channels experience quasi-static fading, which makes it relatively straightforward to utilize feedback information for link adaptive transmission. Applying adaptive modulation to MIMO systems can effectively mitigate the degradation of Free-Space Optical (FSO) signals under adverse turbulence conditions. In this paper, the joint influence of pointing error and Malaga distribution turbulence fading on the probability density function (PDF) of the received light intensity of a MIMO channel employing maximal ratio combining (MRC) is considered. The PDF of MRC combined channel is derived based on the moment generation function. Then, using the cumulative density function (CDF)-based method, we analyze the asymptotic spectral efficiency (SE) and average bit error rate (BER) performance of the adaptive MPSK-MIMO system under different turbulence intensities, beam waist radius, jitter standard deviation, and the number of transmit/receive apertures. In addition, we also compare the average BER performance of adaptive and non-adaptive MIMO systems with MRC receivers. Monte Carlo simulation further verifies our analysis. The derived results help quantify the diversity order of the systems we are considering. [ABSTRACT FROM AUTHOR]

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid control method with adaptive phase-shifting modulation (PSM) and pulse-frequency modulation (PFM) is proposed to optimize the steady-state performance of an LLC resonant converter in wide voltage-gain range application. For the primary-side switches under the hybrid control method, zero-voltage switching (ZVS) performance is maintained over wide voltage-gain range and the turn-off loss is reduced; therefore, the converter reconciles wide voltage-gain range and high efficiency. Mode characteristics under different phase-shifting-angle-θ and switching-frequency-fs combinations is analyzed, and then the soft-switching characteristic is revealed. By introducing time domain analysis, the turn-on current of the primary-side switches is calculated, and thus the ZVS boundary of different θ and fs combinations is inferred. In addition, to acquire the optimum steady-state operation of the converter, the turn-off current is calculated; by making the converter work near to the minimum turn-off current operating point, the turn-off loss can be reduced greatly. With the principles of achieving ZVS performance and minimizing turn-off current, the phase-shifting angle θ of PSM is designed to be adaptive to the reference output voltage, no additional circuits are needed, and the two control degrees (θ and fs) are simplified to one (fs). The simulation and experiment are developed to verify the feasibility and effectiveness of the hybrid control method; the results show that ZVS performance is maintained at wide voltage-gain range and the turn-off current of the hybrid control method is reduced to that of the single PFM, and thus the turn-off loss is reduced. The efficiency comparison validates the fact that the hybrid control method has less power loss than single PSM and single PFM. [ABSTRACT FROM AUTHOR]

Adaptive free-space optical (FSO) communication links have been extensively studied in order to adapt to variable atmospheric channel environments due to factors such as atmospheric turbulence. As a supporting technology, an all-in-one BPSK/QPSK switchable transmission and reception method is proposed and experimentally demonstrated for adaptive modulation format switching in FSO links. The transmission and reception of both modulation formats are realized based on the same IQ modulator and single-photodetector coherent receiver. Simulation and experimental results show that the QPSK signal has a power penalty of about 3–4 dB compared to the BPSK signal with a BER of about 1 × 10−3. The basis for format switching is given according to the various atmospheric channel conditions. The proposed method provides a flexible and efficient solution for variable FSO communication environments to improve their performance. [ABSTRACT FROM AUTHOR]

Although the dead-time optimization design of resonant converters has been widely researched, classical design methods focus more on achieving zero-voltage switching (ZVS) operation. The body diode loss is always ignored, which results in low-efficiency of the converter, especially, in energy router (ER). To deal with this problem, this paper proposes an adaptive deadtime modulation scheme for bidirectional LLC resonant converters in ER. First, the power loss of the MOSFET is analyzed based on the dead-time. Then, a novel dead-time optimization modulation principle is proposed. It can eliminate the body diode loss of MOSFET compared with existing literature. Based on the optimization modulation principle, this paper proposes an adaptive dead-time modulation scheme. To this end, the converter adopting the scheme no longer needs to calculate dead-time, which simplifies the parameter design process. Meanwhile, this scheme enables dead-time to dynamically change with working conditions according to the dead-time optimization modulation principle. With these effects, the ZVS operation is achieved, and the body diode loss of MOSFET is also eliminated. Furthermore, a digital implementation method is designed to make the proposed modulation scheme have fast-transient response. Finally, experimental results show that the proposed dead-time modulation scheme enables converters to achieve ZVS operation in all working conditions, and has higher efficiency than classical dead-time design methods.

There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive M-ary pulse amplitude modulation (PAM) and precoding, which aims to optimize wiretap visible-light channels’ secrecy capacity and bit error rate (BER) performances. The proposed design is motivated by higher-order modulation, which results in better secrecy capacity at the expense of a higher BER. On the other hand, a proper precoding design, which can manipulate the received signal quality at the legitimate user and the eavesdropper, can also enhance secrecy performance and influence the BER. A reward function that considers the secrecy capacity and the BERs of the legitimate user’s (Bob) and the eavesdropper’s (Eve) channels is introduced and maximized. Due to the non-linearity and complexity of the reward function, it is challenging to solve the optical design using classical optimization techniques. Therefore, reinforcement learning-based designs using Q-learning and Deep Q-learning are proposed to maximize the reward function. Simulation results verify that compared with the baseline designs, the proposed joint designs achieve better reward values while maintaining the BER of Bob’s channel (Eve’s channel) well below (above) the pre-FEC (forward error correction) BER threshold.

The cell-free (CF) systems are a key technology for future wireless networks, which exploit the scenario spatial macro-diversity to offer high channel capacity and link reliability, uniform user quality of service (QoS), and better coverage. A technique for access point selection (APS) must be applied to improve the use of the macro-diversity. This paper proposes a low-complexity APS technique based on the derived signal-to-interference-plus-noise ratio (SINR) theoretical expression. Additionally, to increase the system’s spectral efficiency (SE), an expression for the bit error probability (BEP) of the generic square $M_{m}$ -ary quadrature amplitude modulation ( $M_{m}$ -QAM) scheme for each user is proposed and applied to an adaptive modulation (AM) algorithm. The results revealed that the proposed APS technique outperforms techniques with similar simplicity and that the AM algorithm provides higher system SE maintaining the error rate below a given threshold.

Abstract This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

Helicobacter pylori, a widely renowned bacterium, has recently gained attention owing to its potential impact on extragastric health. The emergence of research linking H. pylori infection with metabolic syndrome (MetS)-related cardiovascular diseases (CVDs) has raised intriguing questions about the pathogenic linkage and its translational implications for clinicians. MetS encompasses a collection of metabolic abnormalities that considerably elevate the risk of CVDs and cerebrovascular diseases. Emerging evidence supports a potential pathogenetic role of H. pylori for MetS-related disorders through mechanisms implicating chronic smoldering inflammation, insulin resistance (IR), and modulation of immune responses. One intriguing aspect of this possible connection is the role of mast cells (MCs), a subset of immune cells representing innate immune system effector cells. They play a fundamental role in innate immune responses and the modulation of adaptive immunity. Activated MCs are commonly found in patients with MetS-related CVD. Recent studies have also suggested that H. pylori infection may activate MCs, triggering the release of pro-inflammatory mediators that contribute to IR and atherosclerosis. Understanding these intricate interactions at the cellular level provides new insights into the development of therapeutic strategies targeting both H. pylori infection and MetS-related MCs activation. This review investigates the current state of research regarding the potential impact of H. pylori infection and MetS-related MCs activation on the pathophysiology of CVD, thereby opening up new avenues for related research and paving the way for innovative approaches to prevention and treatment in clinical practice. [ABSTRACT FROM AUTHOR]

The voltage source converter-based multi-terminal DC transmission (VSC-MTDC) system can use additional frequency control to respond to the frequency change of faulty AC system. However, the control coefficient of traditional additional frequency control is mostly fixed, and the control flexibility is insufficient, so it cannot be adjusted adaptively according to the frequency change of the system. Therefore, a frequency control strategy of the VSC-MTDC system based on fuzzy logic control is proposed. Based on the DC voltage slope controller, this strategy introduces an additional frequency controller based on fuzzy logic control, takes the frequency deviation and frequency change rate as the additional controller input, and dynamically adjusts the control quantity through the fuzzy logic control link to realize the adaptive adjustment of the VSC-MTDC system to the AC system's frequency. Finally, a three-terminal flexible HVDC system is built on the PSCAD/EMTDC simulation platform for simulation verification. The results show that the proposed control strategy can effectively use the flexible DC system to support the frequency of the AC system and significantly improve the frequency stability of the faulty AC system. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is the most emerging radio access scheme for fifth-generation (5G) cellular networks. However, the major drawbacks of present NOMA systems are limited channel feedback and the difficulty of combining them with other advanced adaptive modulation and coding systems. Thus, this paper proposes an optimized NOMA system using a stacked Bi-GRU-based deep learning method for estimating the channel. Initially, the messages are coded by utilizing hybrid polar low-density parity-check (LDPC) code for error-free and efficient coding. The coded messages are then adjusted for frame error rate reduction and data sub-block security using a rate optimization approach, and they are modulated with quadrature amplitude modulation (QAM). Finally, channel estimation is performed using a novel stacked bidirectional Gated recurrent unit (Bi-GRU) technique to manage long-term relationships in information. After transmitting the messages via the estimated channel, decoding is performed by a selective extended segment successive cancellation list (SES-SCL) method on the receiver side. For experimentation, the MATLAB platform is preferred, and the results are evaluated and compared with other existing methods. The comparison analysis demonstrates that the proposed method performs better in terms of outage probability, loss, root mean square error (RMSE), bit rate error (BER), and frame error rate (FER). The obtained findings demonstrate that the proposed mechanism is extremely useful for the NOMA system in providing effective services. [ABSTRACT FROM AUTHOR]

Summary: Communication systems that simultaneously combine orthogonal (OMA) and nonorthogonal multiple access (NOMA) are currently under research. Such systems are called hybrid and have more flexibility in user multiplexing, which allows for increased system capacity. One of the main focus areas of research in such systems is radio resource scheduling. The existing research works on scheduling approaches are based on the Shannon's formula, which can be used to obtain the limit capacity by ideal modulation and coding schemes (MCS). However, it is more correct to consider scheduling based on the real capacity. To get it, one should consider the performance of real MCSs with finite code length; however, they are very rarely taken into account in NOMA research. The reason is that one of the basic unresolved problems in NOMA is MCS selection for users. To solve it, we have presented a table of joint pairwise MCS combinations with their adaptive selection for NOMA. In this work, we propose a radio resource scheduling approach for the OMA/NOMA system based on real MCSs that uses our own method of joint MCS selection for two NOMA users. By performing the simulation of the proposed scheduling approach, we have obtained both total and user capacity in the OMA/NOMA system, as well as the distribution of traffic among users, demonstrating the execution of the scheduling strategies. [ABSTRACT FROM AUTHOR]

Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field. [ABSTRACT FROM AUTHOR]

This study introduces a brand-new modified QAM modem that transmits data at a rate of 14.4 kbps over a 32 kbps Adaptive Differential Pulse Code Modulation (ADPCM) channel. This modified QAM modem's goal is to lessen the nonlinear distortion that ADPCM introduces. The effectiveness of ADPCM is investigated using modified and regular QAM modems with various constellations. The simulation findings demonstrate that ADPCM performs better when using a customized QAM modem than when using a normal QAM modem. Additionally, a circular constellation performs better than a rectangular one. [ABSTRACT FROM AUTHOR]

This study illustrates the salinity tolerance mechanisms in Volkameria inermis (a mangrove-associate), making it an ideal candidate for establishment in saline lands. The plant was exposed to 100, 200, 300, and 400 mM NaCl and the TI value indicates that the stress-imparting concentration was 400 mM. There was a decrease in biomass and tissue water, and a gradual increase in osmolytes like soluble sugars, proline, and free amino acids content was observed in plantlets with the increase in NaCl concentrations. Higher number of lignified cells in the vascular region of the plantlet's leaves treated with NaCl (400 mM) may influence the transport through the conducting tissues. SEM data reveals the presence of thick-walled xylem elements, an increased number of trichomes, and partially/fully closed stomata in the 400 mM NaCl-treated samples of V. inermis. In general, macro and micronutrient distribution tend to be affected in the NaCl-treated plantlets. However, Na content increased remarkably in plantlets treated with NaCl, and the highest accumulation was observed in roots (5.58-fold). Volkameria inermis can be a good option for phytodesalination in salt-affected areas since it is equipped with strong NaCl tolerance strategies and can be exploited for desalinization purpose of salt affected lands. The phytodesalination potential of V. inermis was proved with the aid of physiochemical and anatomical studies, which was not yet revealed. The present study elucidated the level of NaCl tolerance in V. inermis and the development of associated adaptive responses. [ABSTRACT FROM AUTHOR]

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER. [ABSTRACT FROM AUTHOR]

To solve the problems of low spectral efficiency and poor performance of physical layer security (PLS) in single-input single-output (SISO) wireless systems with adaptive modulation in the presence of an active eavesdropper, multiple-input multiple-output (MIMO) wireless systems with secure adaptive modulation (SAM) were proposed.The design idea was to improve the spectral efficiency of the main channel by antenna diversity technology, which used the transmit antenna selection (TAS) at the transmitter and the maximal ratio combining (MRC) at the legitimate receiver to improve the signal-to-noise ratio.Simultaneously, the PLS of the legitimate receiver was guaranteed by limiting the bit error rate of the active eavesdropper so that it could not demodulate the confidential information from the main channel.With the joint constraints of the average transmission power and PLS, the optimal switching thresholds and the analytical expressions of spectral efficiency and the eavesdropper’s average bit error rate of the SAM-MIMO systems were derived.Numerical and simulation results show that SAM systems using TAS/MRC technology can not only improve the spectral efficiency of the main channel, but also ensure the PLS against the active eavesdropper, compared with the SAM-SISO systems.

Nowadays, the rising demand for digital communication technologies has contributed to the increase in the volume of traffic. This continuous trend of internet traffic has led to the deterioration of the quality of service (QoS) with reduced throughput and increased latency. This also is due to the proliferation of new broadband applications which require low latency and high throughput such as virtual reality and real-time gaming. Therefore, considering the aforementioned challenge in QoS of wireless networks, a link adaptation method is suggested in this study, in order to enhance the performance of the QoS in IEEE 802.11ac amendment wireless local-area network (WLAN). The proposed technique adaptively changes the transmission data rate by increasing or decreasing the modulation and coding scheme (MCS) level according to the traffic conditions. With the use of an OMNeT++ computer-aided design (CAD)-based simulation model, the effectiveness of the suggested approach is examined. Simulated findings were compared with the link adaptation approach of the default condition. The results of the simulation demonstrate that the proposed technique significantly increases throughput (36.48%) and decreases latency in comparison to the default situation. These findings demonstrate the technique's potential to improve WLAN QoS efficiency, notably in regard to throughput and latency. [ABSTRACT FROM AUTHOR]

Summary: The work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI. [ABSTRACT FROM AUTHOR]

This paper explores the use of load modulation feedback (LMF) in adaptive matching networks (MN) for low-coupling inductive wireless power transfer systems, with an emphasis on its use in implantable medical devices. After deriving the handy expressions of link efficiency and modulation depth in the case of LMF in the case of loose coupling, a brief overview of the most common capacitive resonance networks is presented. In particular, the MN employing two capacitors in Series–Parallel and in Parallel–Series configurations allow adaptivity with a wide range of load conditions. Then, the authors describe an effective design procedure of an adaptive matching network with LMF for an inductive wireless power transfer system, exploring the trade-off between power efficiency and modulation depth. Analytical and electrical simulations show that the proposed simple modulation strategy can successfully achieve high power transfer efficiency while maintaining steady back telemetry under varying loading conditions. [ABSTRACT FROM AUTHOR]

Digestion is driven by digestive enzymes and digestive enzyme gene copy number can provide insights on the genomic underpinnings of dietary specialization. The "Adaptive Modulation Hypothesis" (AMH) proposes that digestive enzyme activity, which increases with increased gene copy number, should correlate with substrate quantity in the diet. To test the AMH and reveal some of the genetics of herbivory vs carnivory, we sequenced, assembled, and annotated the genome of Anoplarchus purpurescens, a carnivorous prickleback fish in the family Stichaeidae, and compared the gene copy number for key digestive enzymes to that of Cebidichthys violaceus, a herbivorous fish from the same family. A highly contiguous genome assembly of high quality (N50 = 10.6 Mb) was produced for A. purpurescens, using combined long-read and short-read technology, with an estimated 33,842 protein-coding genes. The digestive enzymes that we examined include pancreatic α-amylase, carboxyl ester lipase, alanyl aminopeptidase, trypsin, and chymotrypsin. Anoplarchus purpurescens had fewer copies of pancreatic α-amylase (carbohydrate digestion) than C. violaceus (1 vs. 3 copies). Moreover, A. purpurescens had one fewer copy of carboxyl ester lipase (plant lipid digestion) than C. violaceus (4 vs. 5). We observed an expansion in copy number for several protein digestion genes in A. purpurescens compared to C. violaceus, including trypsin (5 vs. 3) and total aminopeptidases (6 vs. 5). Collectively, these genomic differences coincide with measured digestive enzyme activities (phenotypes) in the two species and they support the AMH. Moreover, this genomic resource is now available to better understand fish biology and dietary specialization. [ABSTRACT FROM AUTHOR]

The objective of fifth generation networking technologies seems to provide faster data throughput, exceptional user exposition, power efficiency, and incredibly low delay. To provide clients with the appropriate quality of services, these wireless networks will employ a variety of multi-layer designs made up of device-to-device connections, macro-cells, as well as various types of smaller cells. Machine-to-machine (M2M) technologies are continuing to increase rapidly in quantity. Without careful and effective utilization of resources, as more gadgets accumulate, M2M connectivity will be irregularly impeded by the speed of service shortage. To solve such problems, we provide a dynamical rate adapting (DRA) method in this study that combines time-dependent with event-based M2M technologies to produce optimized rates of service dispersion. By utilizing the median-valued theory of integrals with generalized processing share, DRA enables real-time tracking of M2M data arriving rate, a foundation upon which the speed of service allocation amongst M2M apps will be briefly modified. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) has been employed to assign transmission parameters to all sub-carriers in a set frequency band for anti-jamming Orthogonal Frequency Division Multiplexing (OFDM) systems. However, prior works often overlooked the influence of wireless environment fading and convergence issues stemming from overly large parameter sets. To address these challenges, an anti-jamming scheme was proposed based on the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) communication system integrated with reinforcement learning. First, all sub-carriers were divided into sub-bands, and a Finite State Markov Sub-bands (FSMS) model was established to describe the time-varying fading characteristics of each sub-band by combining Adaptive Modulation and Coding (AMC) technology. To mitigate instability due to the fading channel, a joint sub-band and modulation anti-jamming decision scheme was adopted, enabling the transmitter to select the optimal sub-band and transmission rate. Ultimately, this decision-making process was modeled as a Markov Decision Process (MDP), and an Upper Confidence Bound based Q-learning (UCB-Q) anti-jamming algorithm was proposed for obtaining the joint sub-band and transmission rate selection strategies. Simulation results indicate that the proposed algorithm demonstrates enhanced speed and superior average throughput. Additionally, the algorithm showcases the same commendable anti-jamming performance in scenarios with time-varying dynamic jamming. [ABSTRACT FROM AUTHOR]

A method for modulating transverse magnetic intrinsic graphene plasmons by temperature is proposed. In contrast to existing 2D or 3D infinite graphene structures, an intrinsic graphene 3D finite structure with periodicity in all three directions is designed. At the matching point, where the dispersion curve of the intrinsic graphene plasmons intersects with that of electromagnetic radiation, the intrinsic graphene plasmons and electromagnetic radiation can be excited by each other; in other words, the plasmon can be excited by light without additional mechanisms. Furthermore, the influence of environment temperature on the matching point is shown. The study reveals that the plasmon modulation is adaptive to the temperature. These results are beneficial for the design of compact graphene‐based optoelectronic devices. [ABSTRACT FROM AUTHOR]

Arbitrary-style-per-model (ASPM) style transfer algorithms transfer arbitrary styles based on a single model. Statistics-based learning algorithms of ASPM, represented by adaptive instance normalization (AdaIN), conduct instance normalization and then perform an affine transformation on target features. These algorithms are computationally efficient and easy to embed in convolutional neural networks. Consequently, they are widely used in image synthesis tasks to control the style of the resulting images. However, the style of stylized images may be a combination of content and stylized images, which suggests that these methods do not transform styles accurately. In this work, we rethink the function of AdaIN in controlling style. We show that the role of AdaIN is to (1) give each input content image a specific optimization target, (2) dynamically set cross-channel correlations, and (3) act as a feature selector after combining it with an activation function. Accordingly, we propose adaptive style modulation (AdaSM), which fully leverages the three roles mentioned above and thereby enables more precise control of global style. Experimental results show that AdaSM provides superior style controllability, alleviates the style blending problem, and outperforms state-of-the-art methods in artistic style transfer tasks. [ABSTRACT FROM AUTHOR]

The signal quality in high-bandwidth free space optical (FSO) systems deteriorates due to atmospheric turbulence and pointing errors. Employing techniques such as adaptive transmission and relay selection (RS) can mitigate their effects. This paper analyzes the performance of a dual-hop decode-and-forward multi-relay FSO system with an adaptive M-ary phase shift keying scheme. This analysis is based on the recently proposed Fisher–Snedecor  F channel model and considers the impact of pointing errors. We propose two partial relay selection schemes based on the source-to-relay or relay-to-destination channel state information to reduce the complexity of the optimal relay selection scheme. In this investigation, we derive closed-form expressions for the outage probability, modulation level selection probability, and spectral efficiency (SE) and compare the performance of the proposed RS schemes under balanced and unbalanced link cases. We observe an improvement in the SE with an increase in the number of modulation levels and the number of relays. Moreover, it is noted that the performance of the system can be restricted by the quality of either the source-to-relay or the relay-to-destination link, even if the quality of the other link is perfect. Finally, the outcomes obtained through the derived expressions are validated using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, low complexity rate and power optimization schemes operating in the spatial and frequency domains are proposed in a cognitive radio (CR) setting involving multi-user multiple-input-multiple-output-orthogonal frequency division multiplexing (MU-MIMO-OFDM). Under the assumption of a perfect secondary channel state information (CSI) at the receiver, the presented architectures encompass two main stages. In the first one, spatial power waterfilling-like method is performed per each MIMO subchannel pertaining to each subcarrier of each secondary user (SU). The resulting allocated power per each eigen-channel is considered as the power budget in the second stage. In this latter, stochastic algorithm-based approach wherein the transmit parameters per each subcarrier of each SU are adapted such that to maximize the achievable sum-rate capacity of the SUs. Three different schemes are introduced in this work. First, the derivation of the continuous rate MU-MIMO-OFDM-CR version, referred to as C-MU-MIMO-OFDM-CR is presented. Obviously, this proposition is theoretical and is taken as a benchmark for the two remaining counterparts. The second proposition we called discrete-rate MU-MIMO-OFDM-CR, and briefly designated as D-MU-MIMO-OFDM-CR which is to round the provided allocated rate. Finally, the third modified solution, denoted as P-D-MU-MIMO-OFDM-CR proceeds in a similar way as the D-MU-MIMO-OFDM-CR alternative, but superimposes the non/over-used amount of power to the power budget in next iteration. The simulation results show that, compared to the discrete rate D-MU-MIMO-OFDM-CR solution, the P-D-MU-MIMOOFDM-CR approach exhibits an approximate power gain of 1 dB when the SNR level is low, and of 5 dB at high SNR range. [ABSTRACT FROM AUTHOR]

This paper proposed a fully integrated adaptive on-time (AOT) controlled buck converter with fast load transient. An adaptive on-time generator is presented to stabilize the output frequency. To enhance the light load efficiency, the converter could transfer from the pulse width modulation (PWM) to pulse skip modulation (PSM) as the load current decreases. The buck converter can switch between these two modulation modes adaptively with the assistance of a zero current detection circuit. Implemented in the TSMC 0.18 µm BCD (BiCMOS/DMOS) process, the proposed buck converter works with an input voltage ranging from 5.5 to 15 V, an output voltage ranging from 0.5 to 5 V, and an output load ranging up to 5 A. The experimental results show that based on the dual modulation adaptive on-time controlled mode, the transient recovery time from light to heavy load and from heavy load to light load is 13 µs and 15 µs, respectively. An overshot voltage of 57 mV and an undershot voltage of 53 mV are also achieved. [ABSTRACT FROM AUTHOR]

The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratiothroughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained. [ABSTRACT FROM AUTHOR]

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links.

The aim of this paper is to create an autonomous reconfigurable radio communication system that can produce an adequate transmission rate according to propagation medium fluctuation while maintaining a higher quality of service (QoS). In this work, we have achieved an experimental implementation of adaptive modulation technique using SDR platforms as USRP B210, RTL-SDR and HackRF One. The implemented system provides a real time data communication equipped by an adaptive modulation techniques. The system autonomously chooses the most suitable modulation among BPSK, QPSK and 8-PSK based on the estimation of link quality measured by the signal to noise ratio (SNR). [ABSTRACT FROM AUTHOR]

Deep learning-based methods have recently advanced image generation by exploiting the valuable information within immense training data, but they struggle to synthesize new images for rare categories that are scarce enough to cover some visual concepts. Image generation based on few-shot learning is developed to capture the generalizable generative feature from limited data to produce new images for unseen categories. Existing few-shot methods focus on the high-level semantic difference between conditional images and fuse a generative feature based on the semantic metric. However, it ignores the impact of semantic information underlying different level feature subspaces throughout the generation process, leading to the degradation of visual quality when the diversity of synthetic images improves. In this work, we propose a novel Adaptive Multi-scale Modulation Generative Adversarial Network (AMMGAN) for few-shot image generation, leveraging the U-Net with skip connections to exploit multi-scale semantic metric information. Specifically, an adaptive self-metric fusion module is introduced at the junction between the encoder and decoder of generator, which measures the pixel-wise semantic information of conditional images as a self-metric attention map in each level of deep features to fuse a general feature of interest and adaptively adjust the mean and variance of the fused feature based on the high-level semantic feature of decoder. Meanwhile, the part network of discriminator is developed to co-learn with the generator to formulate a reference-metric modulation code embedding channel-wise metric information, which is integrated into the decoder of generator by learnable residual transformation to refine the unsampled fused feature from the channel dimension. Extensive experiments on several benchmark datasets demonstrate the effectiveness of AMMGAN on few-shot image generation and downstream visual classification tasks. [ABSTRACT FROM AUTHOR]

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70. [ABSTRACT FROM AUTHOR]

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Recent advancements in immunology and chemistry have facilitated advancements in targeted vaccine technology. Targeting specific cell types, tissue locations, or receptors can allow for modulation of the adaptive immune response to vaccines. This review provides an overview of cellular targets of vaccines, suggests methods of targeting and downstream effects on immune responses, and summarizes general trends in the literature. Understanding the relationships between vaccine targets and subsequent adaptive immune responses is critical for effective vaccine design. This knowledge could facilitate design of more effective, disease-specialized vaccines. [ABSTRACT FROM AUTHOR]