Low-complexity Adaptation Research Articles

Neural network based adaptive nonsingular practical predefined-time fault-tolerant control for hypersonic morphing aircraft

This paper develops a novel Neural Network (NN)-based adaptive nonsingular practical predefined-time controller for the hypersonic morphing aircraft subject to actuator faults. Firstly, a novel Lyapunov criterion of practical predefined-time stability is established. Following the proposed criterion, a tangent function based nonsingular predefined-time sliding manifold and the control strategy are developed. Secondly, the radial basis function NN with a low-complexity adaptation mechanism is incorporated into the controller to tackle the actuator faults and uncertainties. Thirdly, rigorous theoretical proof reveals that the attitude tracking errors can converge to a small region around the origin within a predefined time, while all signals in the closed-loop system remain bounded. Finally, numerical simulation results are presented to verify the effectiveness and improved performance of the proposed control scheme.

On Power-Efficient Low-Complexity Adaptation for D2D Resource Allocation with Interference Cancelation.

This paper presents a detailed framework for adaptive low-complexity and power-efficient resource allocation in decentralized device-to-device (D2D) networks. The adopted system model considers that active devices can directly communicate via specified signaling channels. Each D2D receiver attempts to allocate its D2D resources by selecting a D2D transmitter and one of its spectral channels that can meet its performance target. The process is performed adaptively over successive packet durations with the objective of limiting the transmit power on D2D links while reducing the processing complexity. The proposed D2D link adaptation scheme is modeled and analyzed under generalized channel conditions. It considers the random impact of potential D2D transmitters as well as the random number of co-channel interference sources on each D2D link. Interference cancelation schemes are also addressed to alleviate co-channel interference, which can ease the D2D resource allocation process. Generalized formulations for the statistics of the resulting signal-to-interference plus noise ratio (SINR) of the proposed adaptation scheme are presented. Moreover, generic analytical results were developed for some important performance measures as well as processing load measures. They facilitate tradeoff studies between the achieved performance and the processing complexity of the proposed scheme. Insightful results for the distributions of SINRs on individual D2D links under specific fading models are shown in this paper. The results herein add enhancements to some previous contributions and can handle various practical constraints.

Seeing Keratinocyte Proteins through the Looking Glass of Intrinsic Disorder

Epidermal keratinocyte proteins include many with an eccentric amino acid content (compositional bias), atypical ultrastructural fate (built-in protease sensitivity), or assembly visible at the light microscope level (cytoplasmic granules). However, when considered through the looking glass of intrinsic disorder (ID), these apparent oddities seem quite expected. Keratinocyte proteins with highly repetitive motifs are of low complexity but high adaptation, providing polymers (e.g., profilaggrin) for proteolysis into bioactive derivatives, or monomers (e.g., loricrin) repeatedly cross-linked to self and other proteins to shield underlying tissue. Keratohyalin granules developing from liquid–liquid phase separation (LLPS) show that unique biomolecular condensates (BMC) and proteinaceous membraneless organelles (PMLO) occur in these highly customized cells. We conducted bioinformatic and in silico assessments of representative keratinocyte differentiation-dependent proteins. This was conducted in the context of them having demonstrated potential ID with the prospect of that characteristic driving formation of distinctive keratinocyte structures. Intriguingly, while ID is characteristic of many of these proteins, it does not appear to guarantee LLPS, nor is it required for incorporation into certain keratinocyte protein condensates. Further examination of keratinocyte-specific proteins will provide variations in the theme of PMLO, possibly recognizing new BMC for advancements in understanding intrinsically disordered proteins as reflected by keratinocyte biology.

Identification and profiling of microRNAs in two developmental stages of the model cestode parasite Mesocestoides corti

MicroRNAs (miRNAs), a class of small non-coding RNAs, are key regulators of gene expression at post-transcriptional level and play essential roles in fundamental biological processes such as metabolism and development. The particular developmental characteristics of cestode parasites highlight the importance of studying miRNA gene regulation in these organisms. Here, we performed a comprehensive analysis of miRNAs in two developmental stages of the model cestode Mesocestoides corti. Using a high-throughput sequencing approach, we found transcriptional evidence of 42 miRNA loci in tetrathyridia larvae and strobilated worms. Tetrathyridium and strobilated worm-specific miRNAs were found, as well as differentialy expressed miRNAs between these developmental stages, suggesting miRNA regulation of stage-specific features. Moreover, it was shown that uridylation is a differential mechanism of post-transcriptional modification of M. corti miRNAs. The whole set of M. corti miRNAs represent 33 unique miRNA families, and confirm the remarkable loss of conserved miRNA families within platyhelminth parasites, reflecting their relatively low morphological complexity and high adaptation to parasitism. Overall, the presented results provide a valuable platform to studies aiming to identify and characterize novel miRNA-based molecular mechanisms of post-transcriptional gene regulation in cestodes, necessary for the elucidation of developmental aspects of the complex biology of these parasites.

MicroRNA profiling in the zoonotic parasite Echinococcus canadensis using a high-throughput approach.

BackgroundmicroRNAs (miRNAs), a class of small non-coding RNAs, are key regulators of gene expression at post-transcriptional level and play essential roles in fundamental biological processes such as development and metabolism. The particular developmental and metabolic characteristics of cestode parasites highlight the importance of studying miRNA gene regulation in these organisms. Here, we perform a comprehensive analysis of miRNAs in the parasitic cestode Echinococcus canadensis G7, one of the causative agents of the neglected zoonotic disease cystic echinococcosis.MethodsSmall RNA libraries from protoscoleces and cyst walls of E. canadensis G7 and protoscoleces of E. granulosus sensu stricto G1 were sequenced using Illumina technology. For miRNA prediction, miRDeep2 core algorithm was used. The output list of candidate precursors was manually curated to generate a high confidence set of miRNAs. Differential expression analysis of miRNAs between stages or species was estimated with DESeq. Expression levels of selected miRNAs were validated using poly-A RT-qPCR.ResultsIn this study we used a high-throughput approach and found transcriptional evidence of 37 miRNAs thus expanding the miRNA repertoire of E. canadensis G7. Differential expression analysis showed highly regulated miRNAs between life cycle stages, suggesting a role in maintaining the features of each developmental stage or in the regulation of developmental timing. In this work we characterize conserved and novel Echinococcus miRNAs which represent 30 unique miRNA families. Here we confirmed the remarkable loss of conserved miRNA families in E. canadensis, reflecting their low morphological complexity and high adaptation to parasitism.ConclusionsWe performed the first in-depth study profiling of small RNAs in the zoonotic parasite E. canadensis G7. We found that miRNAs are the preponderant small RNA silencing molecules, suggesting that these small RNAs could be an essential mechanism of gene regulation in this species. We also identified both parasite specific and divergent miRNAs which are potential biomarkers of infection. This study will provide valuable information for better understanding of the complex biology of this parasite and could help to find new potential targets for therapy and/or diagnosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-0686-8) contains supplementary material, which is available to authorized users.

Archaeogenomic insights into the adaptation of plants to the human environment: pushing plant–hominin co-evolution back to the Pliocene

The colonization of the human environment by plants, and the consequent evolution of domesticated forms is increasingly being viewed as a co-evolutionary plant–human process that occurred over a long time period, with evidence for the co-evolutionary relationship between plants and humans reaching ever deeper into the hominin past. This developing view is characterized by a change in emphasis on the drivers of evolution in the case of plants. Rather than individual species being passive recipients of artificial selection pressures and ultimately becoming domesticates, entire plant communities adapted to the human environment. This evolutionary scenario leads to systems level genetic expectations from models that can be explored through ancient DNA and Next Generation Sequencing approaches. Emerging evidence suggests that domesticated genomes fit well with these expectations, with periods of stable complex evolution characterized by large amounts of change associated with relatively small selective value, punctuated by periods in which changes in one-half of the plant–hominin relationship cause rapid, low-complexity adaptation in the other. A corollary of a single plant–hominin co-evolutionary process is that clues about the initiation of the domestication process may well lie deep within the hominin lineage.

Frequency domain channel estimation for MIMO SC-FDMA systems with CDM pilots

In this paper, we investigate the frequency domain channel estimation for multiple-input multiple-output (MIMO) single-carrier frequency-division multiple-access (SC-FDMA) systems. In MIMO SC-FDMA, code-division multiplexed (CDM) pilots such as cyclic-shifted Zadoff-Chu sequences have been adopted for channel estimation. However, most frequency domain channel estimation schemes were developed based on frequency-division multiplexing of pilots. We first develop a channel estimation error model by using CDM pilots, and then analyze the mean-square error (MSE) of various minimum MSE (MMSE) frequency domain channel estimation techniques. We show that the cascaded one-dimensional robust MMSE (C1D-RMMSE) technique is complexity-efficient, but it suffers from performance degradation due to the channel correlation mismatch when compared to the two-dimensional MMSE (2D-MMSE) technique. To improve the performance of C1D-RMMSE, we design a robust iterative channel estimation (RITCE) with a frequency replacement (FR) algorithm. After deriving the MSE of iterative channel estimation, we optimize the FR algorithm in terms of the MSE. Then, a low-complexity adaptation method is proposed for practical MIMO SC-FDMA systems, wherein FR is performed according to the reliability of the data estimates. Simulation results show that the proposed RITCE technique effectively improves the performance of C1D-RMMSE, thus providing a better performance-complexity tradeoff than 2D-MMSE.

Spectrum Sensing for Cognitive Radios Based on Space-Time FRESH Filtering

In this paper, we consider the problem of spectrum sensing of cyclostationary signals for cognitive radios. It is shown that the detection performance in this case may be improved by enhancing the cyclostationary features of the signal of interest. The optimal filter for cyclostationary features is revisited, following which an adaptive space-time structure exploiting the spatial, temporal and spectral coherence of a cyclostationary signal incident upon an antenna array is proposed for enhancing the signal. A low complexity adaptation algorithm for this structure is also proposed. The performance of this detector is evaluated and compared with standard spectrum sensing methods. Simulation results show that a suitable space-time FRESH filtering configuration may be used to yield gains of up to 10 dB over both the standard energy detector and the traditional cyclostationary detector.

Adaptive Frequency-Domain Equalization in Mode-Division Multiplexing Systems

Long-haul mode-division multiplexing (MDM) employs adaptive multi-input multi-output (MIMO) equalization to compensate for modal crosstalk and modal dispersion. MDM systems must typically use MIMO frequency-domain equalization (FDE) to minimize computational complexity, in contrast to polarization-division-multiplexed systems in single-mode fiber, where time-domain equalization (TDE) has low complexity and is often employed to compensate for polarization effects. We study two adaptive algorithms for MIMO FDE: least mean squares (LMS) and recursive least squares (RLS). We analyze tradeoffs between computational complexity, cyclic prefix efficiency, adaptation time and output symbol-error ratio (SER), and the impact of channel group delay spread and fast Fourier transform (FFT) block length on these. Using FDE, computational complexity increases sublinearly with the number of modes, in contrast to TDE. Adaptation to an initially unknown fiber can be achieved in ~3-5 μs using RLS or ~15-25 μs using LMS in fibers supporting 6-30 modes. As compared to LMS, RLS achieves faster adaptation, higher cyclic prefix efficiency, lower SER, and greater tolerance to mode-dependent loss, but at the cost of higher complexity per FFT block. To ensure low computational complexity and fast adaptation in an MDM system, a low overall group delay spread is required. This is achieved here by a family of graded-index graded depressed-cladding fibers in which the uncoupled group delay spread decreases with an increasing number of modes, in concert with strong mode coupling.

Iterative Channel Estimation with Frequency Replacement for SC-FDMA Systems

Single-carrier frequency division multiple access (SC-FDMA) is an attractive air interface scheme for broadband wireless communications. For the coherent demodulation of SC-FDMA systems, the receiver performs channel estimation to obtain the channel frequency response by using the reference signals multiplexed in the transmitted signal. In this paper, we propose an iterative frequency domain channel estimation scheme for SC-FDMA systems. To prevent severe noise enhancements due to the Gaussianity of the frequency domain data signal, we consider two low complexity algorithms, namely, hard and soft frequency replacements. We first analytically derive the mean square error (MSE) to characterize the effects of frequency replacement and post iterative filtering. By analyzing MSE performance, we present a design framework to develop the optimal frequency replacement. Based on the design framework, hard and soft frequency replacement algorithms are optimized in the MSE sense. Then, their low complexity adaptation methods are proposed for practical SC-FDMA systems, where the proposed frequency replacement is performed according to the reliability of the data estimates. Simulation results show that the proposed iterative channel estimation techniques effectively compensate for noise enhancements, and thus give good MSE and frame error rate performances.

Low complexity adaptation for SISO channel shortening equalizers

Since the days of Digital Subscriber Links (DSL), time domain equalizers (TEQ's) have been used to combat time dispersive channels in Multicarrier Systems. In this paper, we propose computationally inexpensive techniques to recompute TEQ weights in the presence of changes in the channel, especially over fast fading channels. The techniques use no extra information except the perturbation to the channel itself, and provide excellent approximations to the new TEQ weights. Adaptation methods for two existing Channel shortening algorithms are proposed and their performance over randomly varying, randomly perturbed channels is studied. The proposed adaptation techniques are shown to perform admirably well for small changes in channels for OFDM systems.

Transcoding From Hybrid Nonscalable to Wavelet-Based Scalable Video Codecs

Scalable video coding (SVC) enables low complexity adaptation of compressed video, providing an efficient solution for content delivery through heterogeneous networks and to diverse displays. However, legacy video and most commercially available content capturing devices use conventional nonscalable coding, e.g., H.264/AVC. This paper proposes an efficient transcoder from H.264/AVC to a wavelet-based SVC. It aims at exploiting the advantages offered by fine granularity SVC technology when dealing with conventional coders and legacy video. The proposed transcoder was developed to cope with important functionalities of H.264/AVC, such as flexible reference frame (RF) selection. It is able to work with different coding configurations of H.264/AVC, including IPP or IBBP with multiple RFs. Moreover, many of the techniques presented in this paper are generic in the sense that they can be used for transcoding with many popular wavelet-based and hybrid-based video coding architectures. To reduce the transcoder's complexity, motion information and residual data extracted from a compressed H.264/AVC stream are exploited. Experimental results show a very good performance of the proposed transcoder in terms of decoded video quality and system complexity.

Improved Network-Coded Cooperative Transmission with Low-Complexity Adaptation to Wireless Channels

The relay employs network coding to transmit the packets from the source nodes simultaneously, for increasing spectral efficiency in wireless environments. The cooperative transmission based on network coding usually works on decode-and-forward (DF) protocols. However, detection errors at the relay cause error propagation, which degrades the performance of cooperative communications. To overcome this problem, we model the error propagation effect of the DF-based system at the destination as the addition of virtual noise, and then design a low complexity detection method. We derive the achievable diversity gain to evaluate the proposed model and corresponding detection scheme. To extend the proposed model to network-coded systems, we first express the channel conditions between the sources and relay as a single equivalent channel gain. Then, we develop low complexity detection schemes for the network-coded systems. From the error propagation model, we propose a dual mode network coding technique, which exploits different network coding schemes adaptively according to channel qualities. Simulation results show that the proposed model and detection scheme effectively reduce the error propagation effects. Also, the proposed dual mode network coding has gains for all channel conditions and thus gives better BER performance than conventional methods.

On spectral efficiency of low-complexity adaptive MIMO systems in Rayleigh fading channel

Adaptive MQAM modulation is used to maximize spectral efficiency of multiple-input multiple-output (MIMO) systems while keeping bit error rate (BER) under a target level. Closed-form expressions of the average spectral efficiency, coined as discrete-rate spectral efficiency (DRSE), are derived for adaptive modulation MIMO systems using different algorithms. To further enhance the spectral efficiency, a low complexity adaptation scheme is suggested to switch across different algorithms based on the DRSE. In the current letter, we investigate the adaptation scheme that switches between orthogonal space-time block codes (OSTBC) and spatial multiplexing with zero-forcing (ZF) detection for MIMO systems with two transmit antennas. Two types of operating environment are considered: flat Rayleigh fading channel without spatial correlation and spatially correlated Rayleigh fading channel with transmit correlation.

A Low-Complexity Circuit for On-Sensor Concurrent A/D Conversion and Compression

A low-complexity circuit for on-sensor compression is presented. The proposed circuit achieves complexity savings by combining a single-slope analog-to-digital converter with a Golomb-Rice entropy encoder and by implementing a low-complexity adaptation rule. The adaptation rule monitors the output codewords and minimizes their length by incrementing or decrementing the value of the Golomb-Rice coding parameter k. Its hardware implementation is one order of magnitude lower than existing adaptive algorithms. The compression circuit has been fabricated using a 0.35 muM CMOS technology and occupies an area of 0.0918 . Test measurements confirm the validity of the design.

Adaptive noise-predictive maximum likelihood detection using tentative decision and partial path selection

We present a noise-predictive maximum likelihood (NPML) detection scheme considering both low complexity and effective adaptation. First, for achieving low complexity, we exploit the modified Viterbi decoding method that partially selects the survival paths. The partial path selection method limits the number of selected paths among all survival paths at the Viterbi trellis and selects a path with minimum metric among the selected paths while the original Viterbi algorithm considers all paths and decides the best path. Next, for effective adaptation, we propose an adaptive NPML scheme exploiting a tentative decision value of the Viterbi decoding process.

Rate-distortion hint tracks for adaptive video streaming

We present a technique for low-complexity rate-distortion (R-D) optimized adaptive video streaming based on the concept of rate-distortion hint track (RDHT). RDHTs store the precomputed characteristics of a compressed media source that are crucial for high performance online streaming but difficult to compute in real time. This enables low-complexity adaptation to variations in transport conditions such as available data rate or packet loss. An RDHT-based streaming system has three components: 1) information that summarizes the R-D attributes of the media; 2) an algorithm for using the RDHT to predict the distortion for a feasible packet schedule; and 3) a method for determining the best packet schedule to adapt the streaming to the communication channel. A family of distortion models, denoted distortion chains, are presented which accurately predict the distortion produced by arbitrary packet loss patterns. Two distortion chain models are examined which lead to two RDHT-based techniques. We evaluate the proposed techniques for two canonical problems in streaming media, adaptation to available data rate and to packet loss. Experimental results demonstrate that for the difficult case of nonscalably coded H.264 video, the proposed systems provide significant performance gains over conventional low-complexity streaming systems, and achieve this gain with a comparable level of complexity making them suitable for online R-D optimized streaming.

MPEG-21 digital item adaptation: enabling universal multimedia access

The access devices of today are becoming increasingly sophisticated. Thanks to multimedia, communication is much widespread and therefore more powerful. However, we face a serious problem of heterogeneity in our terminals, in our networks, and in the people who ultimately consume and interact with the information presented to them. In this article, we focus on Part 7 of the MPEG-21 standard (ISO/IEC 21000-7), which we refer to as Digital Item Adaptation (DIA). At the time of this writing, the DIA specification is at the penultimate stage of Final Committee Draft (2003); final approval is scheduled for December 2003. The general DIA concept is that Digital Item is subject to both a resource adaptation and a descriptor adaptation engine, which together produce the adapted Digital Item. Note that the standard specifies only the tools that assist with the adaptation process, not the adaptation engines themselves. Digital Item Adaptation (DIA) specifies the following natural environment description tools: location and time and audiovisual environment. The DIA offers a rich set of tools to assist with the adaptation of Digital Items. It offers standardized tools for the description of usage environments, tools to create high-level descriptions of the bitstream syntax to achieve format-independent adaptation, tools that assist in making tradeoffs between feasible adaptation operations and constraints, tools that enable low-complexity adaptation of metadata, and tools for session mobility. Moving forward, the MPEG-21 committee is considering amendments to the specification-for instance, tools that provide further assistance with modality conversion and tools that relate more specifically to the adaptation of audio and graphics media. Furthermore, also being actively considered is how to express the rights that a User has to perform adaptation and how this expression fits into a system that governs those rights.