Vectorcardiogram (VCG) signal compression is very much in demand in the present-day scenario due to the increasing number of cardiac patients. Hence, in this paper, a new technique is proposed that compresses VCG signal by optimizing the tunable quality wavelet transform (TQWT) parameters. The noise in VCG signal is firstly removed by applying a Savitzky-Golay filter, and then passing noise-free signal to an optimization algorithm that optimizes the TQWT parameters, and obtains the frequency domain signal. This signal is then quantized through dead-zone quantization and processed by a lossless compression mechanism: run-length encoding (RLE) to improve the compression ratio & encode the signal. This compressed signal is reconstructed by Inverse RLE to obtain the decoded signal. Inverse of TQWT is applied to get the reconstructed signal back from the transformed frequency domain to time domain. The parameters of TQWT, especially theQandR, are optimized to get the highestCRat lowest percent root-mean-square-difference(PRD)with best reconstruction quality and least distortions, along with acceptable values of signal-to-noise-ratio(SNR), quality score(QS), andSimilaritywith lowest mean-square-error(MSE). The comparative analysis of different optimization methods indicates that the sparse-particle swarm optimization is best among all the approaches for the tuning of parameters in TQWT for VCG signal compression and reconstruction achieving aCRof 48.18 at aPRDof 3.68,SNRof 29.39,QSof 15.71, similarity of 0.99845,MSEof 0.00016, withQvalue of 2.04307 andRvalue of 1.20568 withcomputational timeof 4.48508 s.

Neural-based lossy compression of noisy audio signals and their DCТ-based post-filtering

ABSTRACT Rapid and accurate assessment of postoperative wound infection is critical for timely adjustment of clinical treatment strategies, which requires real‐time tracking of infection progression without interfering with the wound‐healing process. To address this need, we developed a flexible threshold memristor based on a carbon nanotube‐TiO 2 heterostructure, enabling quantitative differentiation among non‐infected, mildly infected, moderately infected, and severely infected wound states through the systematic variation of its threshold voltage. The operating mechanism originates from the efficient adsorption of bacteria by CNTs and the acid‐promoted dissolution of the Ag electrode, which together accelerate the directional migration of Ag + ions and the formation of conductive filaments within the TiO 2 switching layer. Furthermore, by integrating the memristor with a leaky integrate‐and‐fire (LIF) neuron circuit, we constructed a bioinspired “infection‐sensing neuron” capable of directly converting infection‐induced analog signals into intuitive spike‐frequency patterns. This design not only enables on‐device processing and compression of biological signals, exhibiting the characteristics of near‐sensor computing and significantly reducing data transmission and processing burdens, but also successfully distinguishes different infection levels. Therefore, this study provides a new strategy for developing noninvasive infection‐monitoring systems with clinical potential and promotes the application of flexible neuromorphic electronics in wearable intelligent healthcare.

With the rise of telehealth and long-term cardiac surveillance via wearables, the need for effective ECG compression has intensified. These techniques must balance the reduction of spectral bandwidth with the preservation of critical clinical markers. This paper surveys the landscape of ECG compression, comparing traditional algorithmic approaches with modern neural network architectures. Through a comparative analysis of metrics like Percentage Root Mean Square Difference and Compression Ratio, we highlight the evolution of the field, address persistent implementation challenges, and suggest future trajectories for research. Keywords—ECG, Signal Compression, Wavelet Transform, Machine Learning, PRD, CR

In this paper we propose a new perspective related to Lucas sequence with statistical and summability, some considerations have been given in connection with their applications of signal processing. We define the notions of λ-Lucas statistically convergent and strongly λ-Lucas summability through modulus functions. The study of investigates on the Lucas transform and its embedding diagram into the representative form of Lucas numbers is studied in the context of convergence and summability problems. Furthermore, we provide inclusions as well as principles of equivalence, which lead to conditions guaranteeing the uniqueness of restrictions and generalize statistical convergence theory. Numerical simulations on a wide variety of signals such as noisy sinusoidal signals and blurred images demonstrate the flexibility of the proposed approach. These experiments show a steady decay to zero, which implies considerable noise immunity. In addition, by providing alternative perspectives of assessing signal quality, compression strategies and filtering techniques, those methods help in distinguishing the relevant signal information from background noise. Bringing together theoretical results and practical real-world signal analysis examples, the paper introduces classical and recent contributions to summability theory as well as new signal-processing procedures.

Growing industrial, environmental, and healthcare needs are accelerating the development of next-generation infrared systems with high detectivity, multifunctional sensing, and on-device intelligence. While traditional devices (e.g., HgCdTe, quantum wells) continue to dominate in terms of performance, they face limitations in cooling requirements, cost, and functionality. Recently, considerable advances have been made in materials, structures, and detection systems. As the foundation of IR systems, photodetectors based on traditional materials with band alignment engineering and emerging materials (e.g., two-dimensional materials and quantum dots) show high photodetectivity, low dark current, and room-temperature operation. Meanwhile, on-chip microstructures (e.g., plasmons, metasurfaces, and 3D-assembled architectures) integration enables manipulation of coupling and propagation of electromagnetic fields, which enhances polarization and wavelength-dependent light absorption. These developments empower infrared devices with multidimensional photodetection capabilities and tunable spectral response. Furthermore, advanced technologies like in-sensor computing, miniaturized spectrometers, and on-chip digitization merge sensing, storage, and computing into a single chip. The integration enables monolithic infrared systems with more compact architectures while possessing adaptive perception, data compression, and real-time signal processing capabilities. Finally, a comparative analysis containing material engineering, microstructure design, and integrated architecture is presented to outline the challenges and opportunities toward compact, intelligent, multifunctional infrared detection platforms.

Power efficient signal conversion and quality signal compression using LDS-ADC and hybrid DCT for biomedical signals

Predictors of insurance denial with and without prior authorization in patients undergoing spine surgery: A year-long, single-center cohort analysis.

Signal compression associated with Menzerath’s law affects the reproductive success of male frogs

Background: Spinal subdural hematomas (SSDHs) are rare and potentially disabling complications of neuraxial anesthesia. Cephalic migration of spinal hematomas from the lumbar puncture site extending to the cervical or thoracic regions is even less frequently encountered, especially in postpartum patients. Case Description: A 28-year-old postpartum female developed acute spastic paraparesis 1 day following spinal anesthesia for an emergency cesarean section. The spinal puncture was attempted twice at the L3-L4 interspace. The magnetic resonance imaging of the lumbar spine revealed hemorrhagic fluid-fluid levels at both the L5-S1 and S1-S2 levels, while the cervicodorsal imaging demonstrated a C6-D5 subdural hematoma resulting in significant cord compression and hyperintense cord signal changes. The patient underwent an emergent decompressive laminectomy for hematoma evacuation from C7 to D4 after which she made a progressive and complete recovery over 6 postoperative months. Conclusion: Cephalic migration of SSDH following spinal anesthesia is rare. It is critical to obtain complete spinal imaging in postpartum patients presenting with new neurological deficits following spinal anesthesia and to immediately perform operative decompression to maximize neurological recovery.

Electroencephalography (EEG) data comes with a large size due to the data's high sampling rate. Therefore, compressing EEG data is very important for storing the EEG files efficiently with less space and bandwidth capacity requirement. This research develops an efficient system for EEG data compression. The recorded EEG data are preprocessed and scaled using certain Resolution Factor and truncated to integer numbers, then the scaled EEG samples are classified into small and large vectors using a proposed adaptive thresholding which is based on using three computed factors: Standard deviation, Average of samples (Mean), and the multiplier factor (α). Then, each sample is passed through one of three procedures, then saved into the output file using multi-shift coding algorithm The best values are chosen as the tradeoff between the compression ratio and the processing time. The results indicated that the value of α parameter is significantly affects the threshold calculation, where the best-proven value for α is 1.30; the system achieves a compression gain of 65% while managing a reasonable processing time of 4.007 Second. The resolution factor affected the Mean Squared Error (MSE) and Mean Absolute Error (MEA) significantly, but it had a slight effect on the Compression Ratio (Cr). The α parameter has a great effect on Cr and a slight on MSE. The findings show a consistent trend whereby, as the resolution factor gradually decreases from 2 to 0.1, a concurrent decrease is observed in the MAE, MSE, Bitrate, Cr, and the overall processing time.

Efficient compression of electroencephalogram (EEG) signals is crucial for enabling real-time monitoring, storage, and transmission in various medical and non-medical applications.This paper presents a segment-wise processing approach using temporal modeling-based auto encoders for EEG signal compression.By leveraging models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Self-Attention, the proposed method effectively captures temporal dependencies in the EEG data.Segment-wise processing not only enhances compression efficiency but also significantly reduces the processing time of these sequence models.Extensive experiments demonstrate that GRU-based auto encoders offer the best performance, particularly at lower Data Reduction Factors (DRFs), achieving a minimal signal loss of 0.2% at a 50% compression ratio, making it suitable for medical applications.For non-medical scenarios, a higher compression ratio of 75% with a signal loss of 5.4% is found to be acceptable.The results indicate that the proposed approach achieves a favorable balance between compression efficiency, signal fidelity, and computational performance.

Privacy-preserving data analysis is essential in health care applications to safeguard sensitive patient information while enabling medical monitoring and diagnostics. However, existing solutions generally separate security from analysis modules and memory from computation units, creating hardware and energy overheads that constrain their use in resource-limited medical devices. Here, we introduce the memristor-based colocated authentication and processing (CLAP) system, which achieves security-analysis integration through embedding physical unclonable functions within compute-in-memory architecture. To resolve the incompatibilities between these two features, we propose a differential stochastic mapping method by applying information theory principles. We demonstrate CLAP on a 130-nanometer memristor chip, validating its versatility across diverse information processing tasks. In an electrocardiogram data collection task, CLAP achieves device authentication with an area under the curve of 99.46% and efficient signal compression with a software-level percentage root mean square difference. CLAP demonstrates 146.0-fold energy efficiency gain and 17.6-fold area reduction, providing intrinsically secure hardware solutions that enhance both privacy preservation and computational efficiency for health care applications.

Context . Processing radio interferometric data often requires storing forward-predicted model data. In direction-dependent calibration, these data may have a volume an order of magnitude larger than the original data. Existing lossy compression techniques work well for observed, noisy data, but cause issues in calibration when applied to forward-predicted model data. Aims . To reduce the volume of forward-predicted model data, we present a lossless compression method called Simulated Signal Compression (Sisco) for noiseless data that integrates seamlessly with existing workflows. We show that Sisco can be combined with baseline-dependent averaging for further size reduction. Methods . Sisco decomposes complex floating-point visibility values and uses polynomial extrapolation in time and frequency to predict values, groups bytes for efficient encoding, and compresses residuals using the DEFLATE algorithm. We evaluated Sisco on diverse LOFAR, MeerKAT, and MWA datasets with various extrapolation functions. Implemented as an open-source Casacore storage manager, it can directly be used by any observatory that makes use of this format. Results . We find that a combination of linear and quadratic prediction yields optimal compression, reducing noiseless forward-predicted model data to 24% of its original volume on average. Compression varies by dataset, ranging from 13% for smooth data to 38% for less predictable data. For pure noise data, compression achieves just a size of 84% due to the unpredictability of such data. With the current implementation, the achieved compression throughput is with 534 MB/s mostly dominated by I/O on our testing platform, but occupies the processor during compression or decompression. Finally, we discuss the extension to a lossy algorithm.

The development of an intelligent system for automatic recognition of musical compositions from a short audio fragment using deep learning methods is aimed at addressing the complex problem of identifying melodies when textual information, tags, or metadata are unavailable. This task is particularly relevant in modern digital environments, where users frequently encounter unknown music through streaming platforms, social media, or real-life audio recordings. The proposed approach relies on convolutional neural networks (CNNs) as the core mechanism for extracting and classifying high-level audio representations. In the course of the study, various factors influencing the performance and reliability of the recognition system were systematically examined. These included the choice of audio format (WAV versus MP3), the optimal length of analyzed fragments, the selection of spectral features (mel-spectrogram, chroma, constant-Q transform (CQT), and chroma energy normalized statistics (CENS)), as well as the effect of data augmentation techniques such as adding white noise or pitch shifting. Experimental evaluation demonstrated that the best balance between recognition accuracy and computational efficiency was achieved using one-second segments encoded in MP3 format and represented by mel-spectrograms. This configuration provided high robustness to common distortions while maintaining moderate resource consumption during training and inference. The resulting deep learning model was successfully integrated into a Telegram bot that enables end users to send audio or voice messages for identification. Upon receiving an audio fragment, the system analyzes it and returns both the most probable match and five alternative predictions, offering flexibility in cases of ambiguous input. During testing, particular attention was paid to the influence of recording methods and data transmission quality. It was observed that recordings obtained through Telegram’s built-in voice messaging feature tend to produce lower recognition accuracy, primarily due to signal compression and the introduction of background noise. The research outcomes confirm the feasibility of further enhancement of the system through the use of recurrent or hybrid architectures such as LSTM or GRU networks, expansion of the reference audio database, and training on synthetically distorted data to improve noise tolerance.

Introduction Since the onset of the COVID-19 pandemic, extensive research has focused on developing non-invasive diagnostic approaches of respiratory syndrome using biomedical signals, particularly cough and speech audio. Time-frequency representations combined with Machine Learning models have shown potential in identifying acoustic biomarkers associated with respiratory conditions. Although many existing approaches demonstrate high performance, their use may be limited in resource-constrained environments due to processing or implementation demands. Methods In this study, we propose an end-to-end approach for COVID-19 inference based on compressed time-domain audio signals. The method combines temporal signal compression strategies - Downsampling (DS) and Compressive Sensing (CS) - with a Convolutional Neural Network (CNN) trained directly on the waveforms. This design eliminates the need for handcrafted features or spectrograms, aiming to reduce computational complexity while preserving classification performance. Results To evaluate the proposed structure, we used data from two open-access datasets, one for coughing and one for speech. Experimental results, assessed using accuracy and F1-score metrics, indicate that CS outperformed DS in most scenarios, particularly under high compression rates (e.g., 200 Hz and 100 Hz). Discussion These findings support the use of compressed audio-based classification in real-world embedded and mobile health systems, where computational efficiency is essential.

Retro-odontoid pseudotumor is a soft tissue mass that compresses cervicomedullary neural structures at the craniovertebral junction, causing severe neurological deficits. Optimal management remains controversial. We report exoscope-assisted intradural far-lateral resection of a retro-odontoid pseudotumor without fusion using the lateral position. A 69-year-old male presented with progressive motor weakness and gait disturbance. MRI showed a retro-odontoid pseudotumor with severe spinal cord compression and intramedullary high signal intensity at C1-C2. Radiographs showed minimal atlantoaxial instability. We performed pseudotumor resection through the left intradural far-lateral approach with the patient in the right lateral decubitus position. Head fixation without rotation maintained consistent surgical orientation. This positioning allowed gravity-assisted spontaneous spinal cord displacement, avoiding active neural retraction. The exoscope provided clear oblique visualization of the ventral spinal cord from the posterolateral corridor. We sectioned the dentate ligaments and the left C2 posterior nerve root to expand operating space. The ventral dura was incised and internal debulking achieved partial resection with adequate decompression. Neuroendoscopic examination confirmed sufficient decompression. The patient was discharged without complications. Histopathology confirmed inflammatory tissue consistent with retro-odontoid pseudotumor. At three-month follow-up, neurological function improved significantly. At two years postoperatively, no symptom recurrence was noted. Partial resection achieved effective neural decompression with favorable neurological recovery while preserving spinal mobility. The combination of lateral positioning for gravity-assisted minimal retraction, head fixation without rotation, and exoscopic visualization enabled precise joint-sparing tumor debulking. This strategy offers a viable option for symptomatic retro-odontoid pseudotumors without significant instability.

Digital communication technologies form the backbone of contemporary information exchange across wired and wireless networks. Information processing techniques embedded within these technologies determine how data are acquired, encoded, transmitted, stored, and interpreted at different layers of communication systems. This article provides an overview of widely used information processing techniques associated with common digital communication technologies, including modulation and coding schemes, signal compression, error control mechanisms, multiplexing strategies, and packet processing operations. Emphasis is placed on how these techniques support reliable, efficient, and scalable data transmission in modern applications such as broadband networks, mobile communication systems, multimedia streaming platforms, and internet-based services. The paper outlines fundamental processing operations at the physical, data link, and network layers, highlighting the interaction between signal-level processing and higher-level data handling functions. Key challenges related to bandwidth efficiency, latency, noise resilience, and interoperability are discussed to illustrate the practical constraints faced by digital communication systems. By synthesizing concepts drawn from established communication models and current technology trends, this overview aims to provide a clear conceptual framework for understanding how information processing techniques enable seamless digital communication. The discussion is intended for students, researchers, and practitioners seeking a structured introduction to the role of information processing in digital communication technologies, while also offering insights relevant to system design, performance evaluation, and future technological evolution. It also highlights standardization efforts, architectural trade-offs, and implementation considerations that influence the selection of processing techniques in real-world deployments, thereby bridging theoretical principles with applied engineering practice and encouraging informed decision-making in the development and optimization of next-generation digital communication systems. Such an integrated perspective supports academic learning, interdisciplinary research, and practical innovation across diverse communication environments characterized by increasing data volumes, heterogeneous devices, and evolving user requirements in both centralized and distributed network infrastructures worldwide under contemporary operational constraints and policies.

Due to the Discrete Wavelet Transform (DWT)-OFDM technology giving better frequency localization with respect to FFT-OFDM, it has been adopted in 5G systems, particularly in nonstationary channels, as it is more robust against multipath fading effects.Moreover, if the system can utilize beamforming, which offers significant benefits in terms of system performance, energy efficiency, and cost reduction, then it would be more effective at improving signal quality as noise decreases.In this paper, we combined the latter two features in one system to cope with the challenges of 5G.This system offers a comprehensive analysis useful for applications that require both perspectives, such as signal processing for communication systems, denoising, or feature extraction.DWT MIMO-OFDM and with high-level digital modulation such as different amplitude shift phase key (DAPSK), when performing code to construct this system using MATLAB, this code implements beamforming as diversity due to using the MIMO system.The results show better BER performance despite the increasing data rate, capacity, efficiency, energy, and coverage.The improvements obtained from the suggested system can be adopted in signal compression, denoising, or pattern recognition applications in 5G.

Development of a comprehensive treatment algorithm for tandem spinal stenosis: decision making and surgical strategy.