Low-complexity Sampling Research Articles

Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization Reconstruction

Compressive sensing (CS), a breakthrough technology in image processing, provides a privacy-preserving layer and image reconstruction while performing sensing and recovery processes, respectively. Unfortunately, it still faces high-complexity, low-security, and low-quality reconstruction challenges during image processing. Therefore, this article presents a secure low-complexity CS scheme with preconditioning prior regularization reconstruction. More specifically, the original image is compressed by a low-complexity LFSR-based sparse circulant matrix to obtain measurements. It is worth noting that measurements achieve preliminary distribution equalization through the Tanh sequence to acquire processed measurements. Furthermore, the privacy-preserving edge processing for processed measurements can achieve high security. Finally, preconditioning prior regularization CS reconstruction is designed to improve reconstruction performance. Simulation results and analyses demonstrate that the proposed scheme can achieve low-complexity sampling, high security, and superior reconstruction performance.

Low-Complexity Samples Versus Symbols-Based Neural Network Receiver for Channel Equalization

Low-Complexity Samples Versus Symbols-Based Neural Network Receiver for Channel Equalization

An automated spray-capillary platform for the microsampling and CE-MS analysis of picoliter- and nanoliter-volume samples.

Capillary electrophoresis mass spectrometry (CE-MS) is an emerging analytical tool for microscale biological sample analysis that offers high separation resolution, low detection limit, and low sample consumption. We recently developed a novel microsampling device, "spray-capillary," for quantitative low-volume sample extraction (as low as 15 pL/s) and online CE-MS analysis. This platform can efficiently analyze picoliter samples (e.g., single cells) with minimal sample loss and no additional offline sample-handling steps. However, our original spray-capillary-based experiments required manual manipulation of the sample inlet for sample collection and separation, which is time consuming and requires proficiency in device handling. To optimize the performance of spray-capillary CE-MS analysis, we developed an automated platform for robust, high-throughput analysis of picoliter samples using a commercially available CE autosampler. Our results demonstrated high reproducibility among 50 continuous runs using the standard peptide angiotensin II (Ang II), with an RSD of 14.70% and 0.62% with respect to intensity and elution time, respectively. We also analyzed Ang II using varying injection times to evaluate the capability of the spray-capillary to perform quantitative sampling and found high linearity for peptide intensity with respect to injection time (R2 > 0.99). These results demonstrate the capability of the spray-capillary sampling platform for high-throughput quantitative analysis of low-volume, low-complexity samples using pressure elution (e.g., direct injection). To further evaluate and optimize the automated spray-capillary platform to analyze complex biological samples, we performed online CE-MS analysis on Escherichia coli lysate digest spiked with Ang II using varying injection times. We maintained high linearity of intensity with respect to injection time for Ang II and E. coli peptides (R2 > 0.97 in all cases). Furthermore, we observed good CE separation and high reproducibility between automated runs. Overall, we demonstrated that the automated spray-capillary CE-MS platform can efficiently and reproducibly sample picoliter and nanoliter biological samples for high-throughput proteomics analysis.

Can Water-Only DNA Extraction Reduce the Logistical Footprint of Biosurveillance and Planetary Health Diagnostics? Toward a New Method.

The coronavirus disease-2019 (COVID-19) pandemic has raised the stakes for planetary health diagnostics. Because pandemics pose enormous burdens on biosurveillance and diagnostics, reduction of the logistical burdens of pandemics and ecological crises is essential. Moreover, the disruptive effects of catastrophic bioevents impact the supply chains in both highly populated urban centers and rural communities. One "upstream" focus of methodological innovation in biosurveillance is the footprint of Nucleic Acid Amplification Test (NAAT)-based assays. We report in this study a water-only DNA extraction, as an initial step in developing future protocols that may require few expendables, and with low environmental footprints, in terms of wet and solid laboratory waste. In the present work, boiling-hot distilled water was used as the main cell lysis agent for direct polymerase chain reactions (PCRs) on crude extracts. After evaluation (1) in blood and mouth swabs for human biomarker genotyping, and (2) in mouth swabs and plant tissue for generic bacterial or fungal detection, and using different combinations of extraction volume, mechanical assistance, and extract dilution, we found the method to be applicable in low-complexity samples, but not in high-complexity ones such as blood and plant tissue. In conclusion, this study examined the doability of a lean approach for template extraction in the case of NAAT-based diagnostics. Testing our approach with different biosamples, PCR settings, and instruments, including portable ones for COVID-19 or dispersed applications, warrant further research. Minimal resources analysis is a concept and practice, vital and timely for biosurveillance, integrative biology, and planetary health in the 21st century.

Secure frequency-domain image compressed sensing with matrix-inversion-free recovery

Image encryption should be performed prior to image compression in many real-world applications. This results in the requirement of compressing the encrypted image. Compressed sensing (CS) is an attractive tool for compressing encrypted images. Unfortunately, most existing CS-based image encryption-then-compression (ETC) schemes face several critical challenges, such as low security, high-complexity sampling and recovery. Therefore, this paper presents a secure frequency-domain image CS scheme with matrix-inversion-free (MIF) recovery for ETC applications to address the above challenges. More specifically, the bilateral permutation and nonzero entry diffusion encryption operations are utilized to encrypt the frequency-domain image orderly. Surprisingly, the above two encryption operations do not compromise recovery performance with proper parameters. Then, the encrypted frequency-domain image is simultaneously compressed and sampled with low complexity by an untrusted bandwidth-constrained channel provider. Additionally, we design an accompanying access password as a defence layer, and it can further enhance security. Finally, an extension orthogonal matching pursuit (OMP) recovery algorithm is investigated by avoiding pseudo-inverse and multiple encryption and decryption to reduce computational complexity, named MIF OMP (MIFOMP). Theoretical analyses and simulation results demonstrate the proposed scheme can achieve high security, low-complexity sampling and recovery, and satisfy the expected recovery performance.

Quantitative assessment of enzymatic processes applied to flavour and fragrance standard compounds using gas chromatography with flame ionisation detection

The performance of different enzymes towards the bioprocessing of aroma-related compounds was investigated and a strategy based on GC-FID analysis was developed to facilitate assessment of the stages of characterisation, screening and optimisation, including chiral ratio determination. Characterisation included activity assays (UV–Vis and GC-FID), protein quantification (NanoDrop spectrophotometry) and molar mass estimation (SDS-PAGE electrophoresis). Screening experiments assessed different enzymes, substrates, solvents, acyl donors or mediators. Aroma-related substrates comprised terpene and phenolic compounds. The enzymes tested included the lipases CALA (Sigma-Aldrich), NZ-435, LZ-TLIM, NC-ADL, LZ-CALBL and the laccases NZ-51003 and DL-IIS (all from Novozymes). Among those, NZ-435 and NZ-51003 had the highest activities in the characterisation stage and, along with CALA, achieved conversions above 70% for citronellol (lipases) or 50% for eugenol (laccases) at the screening stage. The lipases had preference for the primary alcohol and laccases for phenolic compounds, among the tested substrates. The transesterification reaction between the lipase CALA and the standards mixture (citronellol, menthol, linalool) was used to demonstrate the optimisation stage, where the best levels of temperature, enzyme and acyl donor concentrations were investigated. Optimum conditions were found to be 37–40 °C, 3–4 mg/mL of enzyme and 58–60% (v/v) vinyl acetate. Additional confirmation experiments using the same terpene standards mixture and citronella oil sample, gave a conversion of > 95% for citronellol after 1 h (for both, standards mixture and sample), and 20% or 74% for menthol after 1 h or 24 h, respectively. None of the tested enzymes demonstrated significant enantioselectivity under the tested conditions. The GC-FID approach demonstrated here was suitable to determine the reaction profiles and chiral ratio variations for biocatalysed reactions with aroma compounds in low complexity samples. Advanced separations will be applied to more complex samples in the future.

Low-Complexity Sampling Frequency Offset Estimation and Compensation Scheme for OFDM-Based UWOC System

In this paper, a simple sampling frequency offset (SFO) estimation and compensation scheme based on two phase-conjugated pilots is proposed and experimentally demonstrated in an OFDM-based underwater wireless optical communication (UWOC) system. The phase shift is obtained by simple multiplication for phase-conjugated pilots, and the results are averaged to perform more accurate phase estimation. The experimental results show that the estimation offset is limited within ±3 ppm when the SFO ranges from −1000 ppm to +1000 ppm over a 1 m tap water channel. Moreover, with the help of the proposed scheme, up to ±300 ppm SFO can be well-compensated with error vector magnitude (EVM) penalties below 1 dB after 1 m underwater transmission. In addition, the results demonstrate that, compared with the ideal case without SFO, our proposed SFO compensation scheme can provide nearly negligible bit error rate (BER) penalties in saltwater with the highly scattering property.

A User-Friendly Sample Preparation Alternative for Manual and Automated LC-MS/MS Quantification of Testosterone.

We describe an LC-MS/MS method for serum testosterone using a novel extraction media, AC Extraction Plate™ (AC Plate,Tecan Schweiz). The AC Plate principle is essentially that of a liquid-liquid extraction (LLE) but employs a stationary nonpolar phase coated on the wells of 96-well plates instead of a nonmiscible organic solvent for partitioning testosterone out of serum, leaving interfering substances behind. This low complexity sample preparation protocol has been validated for and used in production in our laboratory with both manual and automated liquid handling. The primary advantage of this method is the highly reproducible nature of an extraction method that does not require LC-MS/MS expertise or specialized extraction equipment. We modified the existing vendor application and validated the method for matrix effect, recovery, precision, trueness [accuracy relative to certified reference material (CRM)], specificity, reportable range, sample stability, various sample containers, and correlation with other methods.Method performance is excellent, with a reportable range of 4-750ng/dL, between-day quality control coefficient of variation (CV) over 12months of <8%, mean accuracy of <4.0% bias against CRM, no interference from hemolysis, icterus, lipemia, serum separator tube gel, or common steroids/metabolites, and mean bias of 1.3% versus 4 other LC-MS/MS testosterone methods. An investigation of calibration stability and robustness supports sparse (3 versus 6 calibrators) and/or historical calibration for routine use.

SICNet—Low Complexity Sample Adaptive Neural Network-Based Self-Interference Cancellation in LTE-A/5G Mobile Transceivers

The limited transmitter-to-receiver stop-band isolation of the duplexers in long term evolution (LTE) and 5G/NR frequency division duplex transceivers induces leakage signals from the transmitter(s) (Tx) into the receiver(s) (Rx). These leakage signals are the root cause of a multitude of self-interference (SI) problems in the receiver path(s) diminishing a receiver&#x2019;s sensitivity. Traditionally, these effects are counteracted by the use of various different SI cancellation (SIC) architectures which typically solely target one specific problem. In this paper, we propose two novel neural networks based architectures that can handle a variety of different SI effects without the need for a different architecture for each effect. We additionally show the suitability of the proposed architecture on SI effects occurring in in-band full duplex transceivers. Further, we introduce two novel low-cost training algorithms to enable online adaptation (as opposed to offline training currently proposed in literature). The combination of these two concepts is shown to not only beat existing algorithms in their cancellation performance, but also to provide sufficiently low computational complexity allowing on-chip implementations.

Internal Fragment Ions Disambiguate and Increase Identifications in Top-Down Proteomics.

A large fraction of observed fragment ion intensity remains unidentified in top-down proteomics. The elucidation of these unknown fragment ions could enable researchers to identify additional proteoforms and reduce proteoform ambiguity in their analyses. Internal fragment ions have received considerable attention as a major source of these unidentified fragment ions. Internal fragments are product ions that contain neither protein terminus, in contrast with terminal ions that contain a single terminus. There are many more possible internal fragments than terminal fragments, and the resulting computational complexity has historically limited the application of internal fragment ions to low-complexity samples containing only one or a few proteins of interest. We implemented internal fragment ion functionality in MetaMorpheus to allow the proteome-wide annotation of internal fragment ions. MetaMorpheus first uses terminal fragment ions to identify putative proteoforms and then employs internal fragment ions to disambiguate similar proteoforms. In the analysis of mammalian cell lysates, we found that MetaMorpheus could disambiguate over half of its previously ambiguous proteoforms while also providing up to a 7% increase in proteoform-spectrum matches identified at a 1% false discovery rate.

Two‐dimensional adaptive beamforming for large planar array antennas based on weight matrix reconstruction

Adaptive beamformers at the element level usually require a great number of training samples and the computational cost for calculating the weight vector for large phased array antennas is very high, which make it difficult for real-time applications. To address this problem, a two-dimensional (2-D) adaptive beamformer applicable to large planar array antennas that have low computational complexity and low training sample requirement is proposed. In the proposed method, the weight matrix is first reconstructed as a matrix that has the same or close columns and rows by utilising the special Kronecker property of the array steering matrix. Then, the weight vector is determined by adopting a bi-quadratic cost function and a bi-iterative algorithm. Experimental results show that the proposed method can achieve fairly good performance even when the training samples are small.

Effectiveness of the National Quality Control Program in Mycology of Argentina and performance evaluation of participating laboratories

The National Quality Control Program in Mycology (PNCCM) of Argentina was established in 1996 to improve the quality of the mycological diagnosis, to help establish and to set up standardized procedures and continuous training of laboratory staff. The aim of this study was to assess the effectiveness of the PNCCM in the 1996–2018 period. Data from the National Mycology Laboratory Network (NMLN) and PNCCM database was used to estimate the increase in the number of controlled laboratories and jurisdictions, the percentage of participation, the improvement in the quality of results and the adherence to the program. Satisfaction surveys were performed to assess user satisfaction. The number of controlled laboratories increased from 29 to 146; participation increased from 49% to 93% and general adherence was 72% in the evaluated period (1996–2018). Improvement in the quality of the results was 15% for low complexity samples; 7% for intermediate complexity samples and 14% for the identification of high complexity strains. Up to 84% of the users consider the PNCCM to be “very good” and 16% “satisfactory”. These results show the importance of the PNCCM, which is widely accepted by mycological diagnostic laboratories from Argentina.

Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing

Index hopping is the main cause of incorrect sample assignment of sequencing reads in multiplexed pooled libraries. We introduce a statistical model for estimating the sample index-hopping rate in multiplexed droplet-based single-cell RNA-seq data and for probabilistic inference of the true sample of origin of hopped reads. We analyze several datasets and estimate the sample index hopping probability to range between 0.003–0.009, a small number that counter-intuitively gives rise to a large fraction of phantom molecules — the fraction of phantom molecules exceeds 8% in more than 25% of samples and reaches as high as 85% in low-complexity samples. Phantom molecules lead to widespread complications in downstream analyses, including transcriptome mixing across cells, emergence of phantom copies of cells from other samples, and misclassification of empty droplets as cells. We demonstrate that our approach can correct for these artifacts by accurately purging the majority of phantom molecules from the data.

A High-Precision Time Skew Estimation and Correction Technique for Time-Interleaved ADCs

This paper presents an all-digital background calibration technique for the time skew mismatch in time-interleaved ADCs (TIADCs). The technique jointly estimates all of the time skew values by processing the outputs of a bank of correlators. A low-complexity sampling sequence intervention technique, suitable for successive approximation register (SAR) ADC architectures, is proposed to overcome the limitations associated with blind estimation. A two-stage digital correction mechanism based on the Taylor series is proposed to satisfy the target high-precision correction. A quantitative study is performed regarding the requirements imposed on the digital correction circuit in order to satisfy the target performance and yield, and a corresponding filter design method is proposed, which is tailored to meet these requirements. Mitchell’s logarithmic multiplier is adopted for the implementation of the principal multipliers in both the estimation and correction mechanisms, leading to a 25% area and power reduction in the estimation circuit. The proposed calibration is synthesized using a TSMC 28-nm HPL process targeting a 2.4-GHz sampling frequency for an eight-sub-ADC system. The calibration block occupies 0.03 mm2 and consumes 11 mW. The algorithm maintains the SNDR above 65 dB for a sinusoidal input within the target bandwidth.

A large class of chaotic sensing matrices for compressed sensing

Compressed sensing is a revolutionary sampling framework at a sub-Nyquist rate, which relies potentially on sensing matrix. In this paper, a large class of chaotic sensing matrices with low complexity, hardware-friendly implementation and desirable sampling efficiency is proposed based on topologically conjugate chaotic systems (TCcSs). Specifically, we first elaborate an independently and identically distributed chaotic stream, which is generated from a TCcS via our customized zone matching algorithm. Then, the chaotic stream is employed to construct the novel chaotic sensing matrix. Our framework encompasses various families of TCcSs for establishing sensing matrices, such as TCcSs of Tent chaotic system. Moreover, the mutual coherence of the proposed sensing matrices is investigated, and it shows that this kind of chaotic sensing matrices has similar sampling efficiency to that of the state-of-the-art sensing matrices. Experimental performances verify the correctness of the theoretical analysis and illustrate that the proposed matrices can provide comparable results against the existing ones.

2.38 Kbits/frame WDM transmission over a CVLC system with sampling reconstruction for SFO mitigation.

The camera-based visible light communication (CVLC) exploiting rolling shutter effect (RSE) is a cost-efficient technique that ensures secured data transmission. By controlling the on-off state of the light emitting diode (LED), data can be modulated onto the emitting visible light. The corresponding data recovery can be realized by exploiting the RSE of the mobile-phone camera. In this paper, based on a commercial RGB-LED and a single mobile-phone camera, a wavelength division-multiplexing (WDM) CVLC system exploiting RSE is experimentally demonstrated. For the generation and reception of the WDM signals, a structure of three parallel-independent channels is applied to the system. To mitigate the sampling frequency offset (SFO) effect, a low-complexity sampling reconstruction (SR) scheme is proposed and used in each channel of the system. Experimental results show that with the help of the proposed SR scheme, the system can achieve a theoretical data rate up to 2.38 Kbits/frame, with the bit error rate (BER) lower than 3.8 × 10-3.

High-Resolution Microbiome Profiling for Detection and Tracking of Salmonella enterica.

16S rRNA community profiling continues to be a useful tool to study microbiome composition and dynamics, in part due to advances in next generation sequencing technology that translate into reductions in cost. Reliable taxonomic identification to the species-level, however, remains difficult, especially for short-read sequencing platforms, due to incomplete coverage of the 16S rRNA gene. This is especially true for Salmonella enterica, which is often found as a low abundant member of the microbial community, and is often found in combination with several other closely related enteric species. Here, we report on the evaluation and application of Resphera Insight, an ultra-high resolution taxonomic assignment algorithm for 16S rRNA sequences to the species level. The analytical pipeline achieved 99.7% sensitivity to correctly identify S. enterica from WGS datasets extracted from the FDA GenomeTrakr Bioproject, while demonstrating 99.9% specificity over other Enterobacteriaceae members. From low-diversity and low-complexity samples, namely ice cream, the algorithm achieved 100% specificity and sensitivity for Salmonella detection. As demonstrated using cilantro and chili powder, for highly complex and diverse samples, especially those that contain closely related species, the detection threshold will likely have to be adjusted higher to account for misidentifications. We also demonstrate the utility of this approach to detect Salmonella in the clinical setting, in this case, bloodborne infections.

Optimal Selection of Fourier Coefficients for Compressed Sensing-Based UWB Channel Estimation

In this letter, we propose a compressed sensing-based approach for channel estimation in wireless ultra-wideband (UWB) communication systems. The channel is estimated from a minimum number of received signal Fourier coefficients, using a measurement matrix dependent on the transmitted waveform. The optimal subset of coefficient locations, which lowers the coherence of this matrix and maximizes the reconstruction performance, is derived. Furthermore, additional constraints are considered on the subset, to allow the corresponding coefficients to be recovered with a low-complexity sub-Nyquist sampling scheme, as well as to enhance the reconstruction stability to noise. The performance improvement of the UWB channel estimation is demonstrated and the proposed approach is validated using both simulated and measured signals from an experimental setup.

Delay-Aware Wireless Powered Communication Networks—Energy Balancing and Optimization

In a wireless powered communication network, where user equipments (UEs) harvest radio frequency energy from an access point (AP) and send data to the AP, there exists the near-far problem with respect to energy harvesting efficiency due to UEs’ random locations. In this paper, we introduce the concept of delay-aware energy balancing by minimizing the average transmission delay while taking into account the issue of unbalanced harvested energy distribution. In particular, we propose an adaptive harvest-then-cooperate protocol, where every UE first harvests the energy emitted by the AP and then sends data to the AP directly or via other UEs acting as relays in a time-division multiplexing manner. In this protocol, the AP selects the combination of transmission power and routing topology by matching load and energy distributions in the network while minimizing the average transmission delay. Furthermore, we develop a method generating scheduling schemes for this protocol to avoid data overflow in the UE relay. To determine the combination with minimum delay, we approximate the average delay as a Markov decision process and propose a low-complexity sample path-based algorithm to obtain a near-optimal solution. Simulation results demonstrate that the proposed protocol is able to balance the energy distribution while minimizing the transmission delay.

STAGE-diging: A novel in-gel digestion processing for proteomics samples

Proteomics based on high-resolution mass spectrometry has become a powerful tool for the analysis of protein abundance, modifications and interactions. New generation mass spectrometers and UPLC are able to cover approximately an entire cell proteome in one run, but sample preparation, in terms of time and sample recovery is still a critical step. Here we present a modification of the in-gel digestion method, called STAGE-diging. This approach was compared with the well-established procedures for sample preparation, both on high and low complexity samples, on quantitative SILAC-based experiments and on two different mass spectrometers. The results show that STAGE-diging reduces sample handling, decreases the analysis time and improves protein identification and quantification. Moreover, shorter instrument time allows performing multiple replicates that produce wider proteome coverage and more accurate quantitation. SignificanceIn our work we detailed the set-up of a novel in-gel digestion processing for proteomics samples, called STAGE-diging. This new method can be applied to samples of different complexity both for qualitative and quantitative proteomics studies. We proved that STAGE-diging streamlines sample preparation as it is easy to use, reduces sample handling and improves protein identification and quantification all with a decreased analysis time. All these benefits make this new method appealing for laboratories handling a large number of samples, where time and reproducibility play a substantial role.