Adaptive Coding Research Articles

An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

Microwave Antenna Optimization for Low Latency and High Throughput Communication Systems

This paper presents a comprehensive exploration of microwave antenna optimization strategies aimed at achieving low latency and high throughput in modern communication systems. With the escalating demand for real-time applications and data-intensive services, the optimization of microwave antennas has become imperative to meet the stringent requirements of responsiveness and efficiency. The abstract delves into various facets of antenna optimization, including design parameters, signal processing techniques, and deployment considerations, all geared towards minimizing latency and maximizing throughput. Through a thorough review of existing literature and practical insights from real-world deployments, this research aims to provide valuable guidance for engineers and researchers in designing and implementing optimized microwave antennas for next-generation communication networks. By examining the intricate interplay between antenna design, signal processing algorithms, and deployment strategies, this paper elucidates the challenges and opportunities in achieving low latency and high throughput in microwave communication systems. Furthermore, it highlights the significance of factors such as gain, beamwidth, polarization, frequency band selection, multiple-input multiple-output (MIMO) systems, beamforming, adaptive coding and modulation (ACM), antenna placement, tower height, and line-of-sight considerations in optimizing antenna performance. The findings presented in this paper contribute to advancing the understanding of microwave antenna optimization and offer practical insights into enhancing the responsiveness, efficiency, and reliability of modern communication networks.

Empowering Learning: A Review of Online Programming Tools and their Evolving Landscape

Abstract: The emergence of online programming tools has significantly impacted computer programming education. This review paper delves into the current research landscape surrounding these tools, analyzing their influence on learning outcomes, collaboration, and the potential of AI-powered personalized learning. The review explores the foundational role of online compilers and collaborative environments. It then examines recent advancements in AI, including adaptive coding tutors and AIpowered code completion.

End-to-end learning of adaptive coded modulation schemes for resilient wireless communications

Adaptive modulation and coding schemes play a crucial role in ensuring robust data transfer in wireless communications, especially when faced with changes or interference in the transmission channel. These schemes involve the use of variable coding rates, which can be achieved normally through code puncturing or shortening, and have been adopted in 4G and 5G communication standards. In recent works, auto-encoders for wireless communications have demonstrated the ability to learn short code representations that achieve gains over conventional codes. Such a methodology is attractive as it can learn optimal representations under a variety of channel conditions. However, due to its structure the auto-encoder does not currently support multiple code rates with a single model. This article draws upon the discipline of multi-task learning, as it applies to deep learning and therefore devises a branching architecture for the auto-encoder and custom training algorithm in training transmitter and receiver for adaptive modulation and coding. In this article we aim to demonstrate improvements in Block Error Rate over conventional methods in the Additive White Gaussian Noise channel, and to analyse the performance of the model under Rayleigh fading channels without retraining the auto-encoder on the new channel. This article demonstrates a novel approach towards training auto-encoder models to jointly learn adaptive modulation and coding schemes framed as a multi-task learning problem. The research outcomes extend end-to-end learning approaches to the design of adaptive wireless communications systems.

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

Adaptive coding of reward in schizophrenia, its change over time and relationship to apathy.

Adaptive coding of reward is the process by which neurons adapt their response to the context of available compensations. Higher rewards lead to a stronger brain response, but the increase of the response depends on the range of available rewards. A steeper increase is observed in a narrow range and a more gradual slope in a wider range. In schizophrenia, adaptive coding appears to be affected in different domains, especially in the reward domain. Here, we tested adaptive coding of reward in a large group of patients with schizophrenia (n = 86) and control subjects (n = 66). We assessed: (i) the association between adaptive coding deficits and symptoms; (ii) the longitudinal stability of deficits (the same task was performed 3 months apart); and (iii) the stability of results between two experimental sites. We used functional MRI and the monetary incentive delay task to assess adaptation of participants to two different reward ranges: a narrow range and a wide range. We used a region-of-interest analysis to evaluate adaptation within striatal and visual regions. Patients and control subjects underwent a full demographic and clinical assessment. We found reduced adaptive coding in patients, with a decreased slope in the narrow reward range with respect to that of control participants, in striatal but not visual regions. This pattern was observed at both research sites. Upon retesting, patients increased their narrow-range slopes, showing improved adaptive coding, whereas control subjects slightly reduced them. At retesting, patients with overly steep slopes in the narrow range also showed higher levels of negative symptoms. Our data confirm deficits in reward adaptation in schizophrenia and reveal an effect of practice in patients, leading to improvement, with steeper slopes upon retesting. However, in some patients, an excessively steep slope may result in poor discriminability of larger rewards, owing to early saturation of the brain response. Together, the loss of precision of reward representation in new (first exposure, underadaptation) and more familiar (retest, overadaptation) situations might contribute to the multiple motivational symptoms in schizophrenia.

Diminished differentiation of rewards in individuals at clinical high-risk for psychosis.

Reward processing is impaired in people with schizophrenia, which may begin in the clinical high-risk (CHR) for psychosis period. The Monetary Incentive Delay (MID) task has been important in understanding the neural correlates of reward processing deficits in various psychiatric disorders. Previous research has found that CHR individuals have an imprecise mental representation of rewards, which leads to a diminished differentiation between rewards, though this has not been observed behaviorally. A total of 19 CHR individuals and 20 controls were given a novel variant of the MID task, designed to examine how modulating reward context may impact responses to reward cues, a process often referred to as "adaptive coding." Both groups appeared to update their behavior in response to the rewards available in this adaptive task. However, when compared to controls who showed a more graded decrease in response time to increasing reward contexts, CHR individuals appeared to have a sharp decrease in response time in the low reward context that is nearly stable across higher reward contexts. This is largely driven by the exponential component of the response time distribution, which is often interpreted to be more cognitively or effortfully influenced. Response times are related to negative symptoms, but not positive symptoms, disorganized symptoms, or estimated intelligence. Although an adaptive coding effect was not observed, these results provide novel insight into the reward processing mechanisms and volitional processes in the CHR population, as this was the first study to observe the diminished differentiation of rewards behaviorally.

B-CANF: Adaptive B-Frame Coding With Conditional Augmented Normalizing Flows

B-CANF: Adaptive B-Frame Coding With Conditional Augmented Normalizing Flows

Clumpy structures within the turbulent primordial cloud

ABSTRACT The primordial clouds in the mini-haloes hatch the first generation stars of the Universe, which play a crucial role in cosmic evolution. In this paper, we investigate how turbulence impacts the structure of primordial star-forming clouds. Previous cosmological simulations of the first star formation predicted a typical mass of around $\mathrm{ 100 \, M_\odot }$. This conflicts with recent observations of extremely metal-poor stars, suggesting a lower mass scale of about $\mathrm{25 \, M_\odot }$. The discrepancy may arise from unresolved turbulence in the star-forming cloud, driven by primordial gas accretion during mini-halo formation in the previous simulations. To quantitatively examine the turbulence effect on the primordial cloud formation, we employ the adaptive mesh refinement code Enzo to model the gas cloud with primordial composition, including artificially driven turbulence on the cloud scale and relevant gas physics. This artificially driven turbulence utilizes a stochastic forcing model to mimic the unresolved turbulence inside mini-haloes. Our results show that the turbulence with high Mach number and compressional mode effectively fragments the cloud into several clumps, each with dense cores of $\mathrm{22.7 - 174.9 \, M_\odot }$ that undergo Jeans instability to form stars. Fragmentation caused by intense and compressive turbulence prevents a runaway collapse of the cloud. The self-bound clumps with smaller masses in the turbulent primordial clouds suggest a possible pathway to decrease the theoretical mass scale of the first stars, further reconciling the mass discrepancy between simulations and observations.

Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function

There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells. Previous theoretical work shows that power-law history dependent spike time adaptation, seen in several brain areas and species, can be modeled with fractional order differential equations. Here, we show that fractional order spiking neurons can be implemented using super-capacitors. The super-capacitors have fractional order derivative and memcapacitive properties. We implemented two circuits, a leaky integrate and fire and a Hodgkin–Huxley. Both circuits show power-law spiking time adaptation and optimal coding properties. The spiking dynamics reproduced previously published computer simulations. However, the fractional order Hodgkin–Huxley circuit showed novel dynamics consistent with criticality. We compared the responses of this circuit to recordings from neurons in the weakly-electric fish that have previously been shown to perform fractional order differentiation of their sensory input. The criticality seen in the circuit was confirmed in spontaneous recordings in the live fish. Furthermore, the circuit also predicted long-lasting stimulation that was also corroborated experimentally. Our work shows that fractional order memcapacitors provide intrinsic memory dependence that could allow implementation of computationally efficient neuromorphic devices. Memcapacitors are static elements that consume less energy than the most widely studied memristors, thus allowing the realization of energetically efficient neuromorphic devices.

The galactic bubbles of starburst galaxies

Context. The galactic winds of starburst galaxies (SBGs) give rise to remarkable structures on kiloparsec scales. However, the evolution and shape of these giant wind bubbles, as well as the properties of the shocks they develop, are not yet fully understood. Aims. We aim to understand what shapes the galactic winds of SBGs, with a particular focus on the role of large-scale magnetic fields in the dynamical evolution of galactic wind-inflated bubbles. In addition, we aim to explore where the conditions for efficient particle acceleration are met in these systems. Methods. We performed magnetohydrodynamic simulations with the AMRVAC code (Adaptive Mesh Refinement Versatile Advection Code) with various configurations of the galactic medium density profile and magnetization. Results. We observe that the large-scale magnetic field, in which galactic winds expand, can impact the structure and evolution of inflated bubbles. However, the typical structures observed in starburst galaxies, such as M82, cannot be solely explained by the magnetic field structures that have been considered. This highlights the importance of other factors, such as the galactic disk, in shaping the galactic bubble. Furthermore, in all the magnetized cases we investigated, the forward wave resulting from the expanding bubbles only results in compression waves, whereas the wind termination shock features high Mach numbers, making it a promising site for diffusive shock acceleration up to ∼102 PeV. The synthetic X-ray images generated from our models reveal an envelope surrounding the bubbles that extends up to 2 kpc, which could correspond to the polarized emission observed from planar geometry in M82, as well as a large structure inside the bubble corresponding to the shocked galactic wind. Additionally, our findings indicate that, as observed with the SOFIA instrument, a large ordered magnetic field is associated with the free galactic wind, while a more turbulent magnetic field is present in the shocked region.

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

Betelgeuse as a Merger of a Massive Star with a Companion

We investigate the merger between a 16M ⊙ star, on its way to becoming a red supergiant (RSG), and a 4M ⊙ main-sequence companion. Our study employs three-dimensional hydrodynamic simulations using the state-of-the-art adaptive mesh refinement code Octo-Tiger. The initially corotating binary undergoes interaction and mass transfer, resulting in the accumulation of mass around the companion and its subsequent loss through the second Lagrangian point (L2). The companion eventually plunges into the envelope of the primary, leading to its spin-up and subsequent merger with the helium core. We examine the internal structural properties of the post-merger star, as well as the merger environment and the outflow driven by the merger. Our findings reveal the ejection of approximately ∼0.6 M ⊙ of material in an asymmetric and somewhat bipolar outflow. We import the post-merger stellar structure into the MESA stellar evolution code to model its long-term nuclear evolution. In certain cases, the post-merger star exhibits persistent rapid equatorial surface rotation as it evolves in the H–R diagram toward the observed location of Betelgeuse. These cases demonstrate surface rotation velocities of a similar magnitude to those observed in Betelgeuse, along with a chemical composition resembling that of Betelgeuse. In other cases, efficient rotationally induced mixing leads to slower surface rotation. This pioneering study aims to model stellar mergers across critical timescales, encompassing dynamical, thermal, and nuclear evolutionary stages.

An Improved Adaptive Coding and Modulation Scheme With Hybrid Switching Standard for UAV-to-Ground Free Space Optical Communication

An Improved Adaptive Coding and Modulation Scheme With Hybrid Switching Standard for UAV-to-Ground Free Space Optical Communication

CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.

Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder

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.

Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks

Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis.

Formalization and modeling of turbocode encoding/decoding processes for 5G communication systems

Improving the energy characteristics in 4G and 5G mobile communication systems is provided by the use of adaptive control technology for encoding, modulation and power parameters. The paper considers promising adaptive coding schemes in mobile communication systems. The article formalizes the process of optimizing the operation of the turbo code encoder and decoder by using adaptive selection of the size of state diagrams based on the proposed decoding uncertainty indicator. In contrast to the known methods of TC adaptation, the size of the TC encoder/decoder state diagram is adaptive depending on the signal-to-noise ratio in the channel and the values of the normalized number of changes in the sign of the a posteriori-a priori logarithmic relations of the likelihood functions about the transmitted data bits of the turbo code decoder. Analysis of the simulation results shows that the reliability of transmission is increased by choosing a rational size of the TC encoder/ decoder state diagram. The proposed formalization of the model of the TC functioning process is a further development of adaptive coding technologies while preserving the transmission system bandwidth and differs from those known in that various TC MLRS polynomials are used for adaptation, which determine the necessary structure and size of the TC encoder and decoder state diagram. The interleader parameters are selected as fixed. Simulation modeling was used to analyze the efficiency of optimizing the operation of the encoder and decoder of the turbo code by using adaptive size selection of state diagrams. For comparison, the fourth-generation LTE-Advanced Mobile Communication Standard was chosen. The simulation was carried out in Visual Studio 2019 the data transmission system with Turbo codes, modulator (demodulator) OFDM, channel with additive Gaussian white noise (ABGN) was modeled. The results of the work can be used in conjunction with other adaptation methods, such as coding rate adaptation, based on polynomials of TC component codes, in systems with multiparametric adaptation that function under conditions of complete or partial a priori uncertainty.

Adaptive Channel Coding to Enhance the Performance in Rayleigh Channel

Adaptive Channel Coding to Enhance the Performance in Rayleigh Channel