Pulse Code Modulation Research Articles

New space vector pulse code modulation scheme for multilevel inverters with reduced zero sequence voltage and reduced switching losses

New space vector pulse code modulation scheme for multilevel inverters with reduced zero sequence voltage and reduced switching losses

Robustness Stability Analysis of Higher-Order DPCM Prediction Filters

This paper considers the robustness of the differential pulse-code modulation system with higher-order predictors. Special attention is paid to the robust parametric stability of the prediction filters with respect to the predictor coefficients. A generalisation of robustness in the classical sense is performed, and appropriate relations for calculating the probability of robustness are derived using Kharitonov principle. The proposed robustness estimation method is used for the third- and fourth-order prediction filters on speech signals, where the application of traditional methods is too difficult. For this reason, the Monte Carlo method is used to solve complex probability integrals. Verification and error analysis are performed for the previously considered second-order predictor. Satisfactory predetermined accuracy is achieved by increasing the number of samples. The results obtained could be very useful to design a system with suitable values for the predictor coefficients.

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

Минимизация вероятности ошибки при взвешенном приеме сообщения с двухпозиционной импульсно-кодовой манипуляцией в условиях межсимвольной интерференции

In order to increase the noise immunity of data transmission systems, it is proposed to reduce the level of intersymbol interference by means of orthogonalizing the channel symbols. Orthogonalization is done through determining the orthogonality weight. Presented below is a calculation of the E-criterion for assessing the level of intersymbol interference for the proposed data transmission systems using the pulse-code modulation. For both systems, it was assumed that normalized Butterworth low-pass filters would be used as the shaping filter. It is shown that when signal-to-noise ratios exceed a certain critical value, weighted signal processing provides a lower bit error probability compared to the conventional processing technique. A general analytical expression for the orthogonality weight of channel symbols is given, which ensures a minimum bit error probability.

Cipher-image compression by using compressive sensing and interweaving permutation

Recently, compressing cipher images by using compressive sensing (CS) has attracted a lot of attention. However, the previous CS-based works cannot provide satisfactory compression performance. In this article, a cipher-image compression scheme based on CS and interweaving permutation is proposed to address this concern. Before image compression, the nature image is down-sampled to generate a lot of patches by using interweaving permutation firstly and then each patch is encrypted by a scrambling matrix, where each patch is a down-sampled image. In this way, the encrypted patches are highly correlated from one to each other since they preserve the correlation of the original patches. Therefore, we can encode the cipher image by employing CS and differential pulse-code modulation (DPCM) in an efficient way. To reconstruct the image effectively, an iterative shrinkage-thresholding (IST) algorithm is developed. Compared with the previous CS-based works, our scheme achieves better ratio-distortion (R-D) performance.

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.

A State Table SPHIT Approach for Modified Curvelet-based Medical Image Compression

Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds.

Actuators linearization of a large volume environmental simulation chamber via pulse code modulation

Ultra-stable room-sized environmental chambers may involve significant power variations leading to hysteresis and nonlinearities of the stabilization system. This paper describes utilization of a pulse code modulation control which significantly reduces observed nonlinearity of the chamber actuation system. Stability of environmental conditions for temperature ΔT=±25 mK (15–35 °C dynamic range), relative humidity ΔRh=±0.1% (3%–95% dynamic range), and pressure ΔP=±12 Pa (±500 Pa dynamic range) was achieved within 27 m3 chamber volume, which is two orders of magnitude better than the corresponding stabilization achievable using a standard linear proportional–integral–derivative (PID) controller. Utilizing this chamber we have demonstrated two orders of magnitude improvement of the operational relative frequency stability of ultrastable quartz oscillators (USO), reaching 2×10−13 stability at 1-105 s averaging time.

Interactive Multimedia Association-Adaptive Differential Pulse Code Modulation Codec With Gated Recurrent Unit Predictor

Interactive Multimedia Association-Adaptive Differential Pulse Code Modulation Codec With Gated Recurrent Unit Predictor

Efficient Sigma-Delta Sensor Array Beamforming.

Nowadays, sensors with built-in sigma-delta modulators (ΣΔMs) are widely used in consumer, industrial, automotive, and medical applications, as they have become a cost-effective and convenient way to deliver data to digital processors. This is the case for micro-electro-mechanical system (MEMS), digital microphones that convert analog audio to a pulse-density modulated (PDM) bitstream. However, as the ΣΔMs output a PDM signal, sensors require either built-in or external high-order decimation filters to demodulate the PDM signal to a baseband multi-bit pulse-code modulated (PCM) signal. Because of this extra circuit requirement, the implementation of sensor array algorithms, such as beamforming in embedded systems (where the processing resources are critical) or in very large-scale integration (VLSI) circuits (where the power and area are crucial) becomes especially expensive as a large number of parallel decimation filters are required. This article proposes a novel architecture for beamforming algorithm implementation that fuses delay and decimation operations based on maximally flat (MAXFLAT) filters to make array processing more affordable. As proof of concept, we present an implementation example of a delay-and-sum (DAS) beamformer at given spatial and frequency requirements using this novel approach. Under these specifications, the proposed architecture requires 52% lower storage resources and 19% lower computational resources than the most efficient state-of-the-art architecture.

Dual-layer optical encryption fluorescent polymer waveguide chip based on optical pulse-code modulation technique

Information encryption technique has broad applications in individual privacy, military confidentiality, and national security, but traditional electronic encryption approaches are increasingly unable to satisfy the demands of strong safety and large bandwidth of high-speed data transmission over network. Optical encryption technology could be more flexible and effective in parallel programming and multiple degree-of-freedom data transmitting application. Here, we show a dual-layer optical encryption fluorescent polymer waveguide chip based on optical pulse-code modulation technique. Fluorescent oligomers were doped into epoxy cross-linking SU-8 polymer as a gain medium. Through modifying both the external pumping wavelength and operating frequency of the pulse-code modulation, the sender could ensure the transmission of vital information is secure. If the plaintext transmission is eavesdropped, the external pumping light will be switched, and the receiver will get warning commands of ciphertext information in the standby network. This technique is suitable for high-integration and high-scalability optical information encryption communications.

Digital radio-over-fiber system based on differential pulse code modulation and space division multiplexing.

In this paper, we present and experimentally demonstrate a digital-radio-over-fiber (D-RoF) architecture based on differential pulse code modulation (DPCM) and space division multiplexing (SDM). At low quantization resolution, DPCM can effectively reduce quantization noise and obtain significant signal-to-quantization noise ratio (SQNR) gain. We experimentally study the 7-core and 8-core multicore fiber transmission of 64-ary quadrature amplitude modulation (64QAM) orthogonal frequency division multiplexing (OFDM) signals with a bandwidth of 100 MHz in a fiber-wireless hybrid transmission link. Compared to PCM-based D-RoF, the error vector magnitude (EVM) performance in the DPCM-based D-RoF is effectively improved when the quantization bits (QBs) are 3-5 bits. In particular, when the QB is 3 bits, the EVM of the DPCM-based D-RoF is 6.5% and 7% lower than that of the PCM-based system in 7-core- and 8-core-multicore fiber-wireless hybrid transmission links, respectively.

PVT panel efficiency enhancement with cooling methods and sun tracking

Solar energy is the most abundant and easily available renewable source of electricity. However, the installed solar panels may not always provide the optimal amount of energy because of the changing position of the sun from east to west. This study focused on extending the research and development of a model for a dual-axis solar panel tracking system with a cooling mechanism. The primary objective of this mechanism is to program the Arduino board in a way that ensures continuous alignment of the panel direction and Light-Dependent Resistor (LDR) sensors with the sun's rays. Direct Current (DC) motors were employed to adjust the panel direction accordingly. Sustained exposure to sunlight can lead to reduced efficiency when the panel is heated. To counteract overheating, an improved cooling system was implemented utilizing water and a pump arrangement to enhance the productivity of the panel. Water is sprayed or sprinkled using various Pulse Code Modulation (PCM) techniques, effectively maintaining the solar panel temperature at an optimal level. This mechanism can be enhanced by integrating a Real-Time Clock (RTC) to accurately track the direction of the sun. This ensures that the panel remains properly positioned even in the event of a power outage, enabling the utilization of solar energy for electrical purposes. This research focuses on developing an efficient solar panel tracking system with a cooling mechanism to maximize the energy output and address temperature-related efficiency issues. The integration of advanced features such as RTC further enhances the reliability and usability of the system.

Using Vocal Characteristics To Classify Psychological Distress in Adult Helpline Callers: Retrospective Observational Study

Elevated psychological distress has demonstrated impacts on individuals' health. Reliable and efficient ways to detect distress are key to early intervention. Artificial intelligence has the potential to detect states of emotional distress in an accurate, efficient, and timely manner. The aim of this study was to automatically classify short segments of speech obtained from callers to national suicide prevention helpline services according to high versus low psychological distress and using a range of vocal characteristics in combination with machine learning approaches. A total of 120 telephone call recordings were initially converted to 16-bit pulse code modulation format. Short variable-length segments of each call were rated on psychological distress using the distress thermometer by the responding counselor and a second team of psychologists (n=6) blinded to the initial ratings. Following this, 24 vocal characteristics were initially extracted from 40-ms speech frames nested within segments within calls. After highly correlated variables were eliminated, 19 remained. Of 19 vocal characteristics, 7 were identified and validated as predictors of psychological distress using a penalized generalized additive mixed effects regression model, accounting for nonlinearity, autocorrelation, and moderation by sex. Speech frames were then grouped using k-means clustering based on the selected vocal characteristics. Finally, component-wise gradient boosting incorporating these clusters was used to classify each speech frame according to high versus low psychological distress. Classification accuracy was confirmed via leave-one-caller-out cross-validation, ensuring that speech segments from individual callers were not used in both the training and test data. The sample comprised 87 female and 33 male callers. From an initial pool of 19 characteristics, 7 vocal characteristics were identified. After grouping speech frames into 2 separate clusters (correlation with sex of caller, Cramer's V =0.02), the component-wise gradient boosting algorithm successfully classified psychological distress to a high level of accuracy, with an area under the receiver operating characteristic curve of 97.39% (95% CI 96.20-98.45) and an area under the precision-recall curve of 97.52 (95% CI 95.71-99.12). Thus, 39,282 of 41,883 (93.39%) speech frames nested within 728 of 754 segments (96.6%) were classified as exhibiting low psychological distress, and 71455 of 75503 (94.64%) speech frames nested within 382 of 423 (90.3%) segments were classified as exhibiting high psychological distress. As the probability of high psychological distress increases, male callers spoke louder, with greater vowel articulation but with greater roughness (subharmonic depth). In contrast, female callers exhibited decreased vocal clarity (entropy), greater proportion of signal noise, higher frequencies, increased breathiness (spectral slope), and increased roughness of speech with increasing psychological distress. Individual caller random effects contributed 68% to risk reduction in the classification algorithm, followed by cluster configuration (23.4%), spectral slope (4.4%), and the 50th percentile frequency (4.2%). The high level of accuracy achieved suggests possibilities for real-time detection of psychological distress in helpline settings and has potential uses in pre-emptive triage and evaluations of counseling outcomes. ANZCTR ACTRN12622000486729; https://www.anzctr.org.au/ACTRN12622000486729.aspx.

Web-Based Networked Music Performances via WebRTC: A Low-Latency PCM Audio Solution

Web-Based Networked Music Performances via WebRTC: A Low-Latency PCM Audio Solution

Control and Monitoring Subsystem of an Unmanned Aerial Vehicle

The object of the research is the unmanned aerial vehicle monitoring system, the data exchange protocol and the unmanned aerial vehicle control line. A study was conducted, based on the results of which the unmanned aerial vehicle protection protocol and criteria were selected. An algorithm has been developed that ensures the establishment of the fact that the received radio radiation belongs to the class of radio signals of unmanned aerial vehicle remote control systems with pulse-position and pulse-code modulations. The results of practical testing of possible solutions are presented and the results of the functioning of the developed communication components are given. The user interface was developed, and the hybrid data exchange protocol was implemented in software.

Design of Intelligent Speech Translation System Based on Deep Learning

In order to solve the problem of low translation accuracy caused by complex sentence parameters in traditional machine translation systems, a method based on deep learning was proposed. First, MCU SPCE061A is used to study the problem of complex digital signal. The training data in the synchronous translation server support the translation services of a large number of users, and the translation results were displayed through the session interface of the user terminal. The PMDL model is used to detect the keyword signal, record the PCM audio data, and slice the collected pulse code modulation signal, so as to wake up the artificial intelligence voice service. Then, this study establishes a speech recognition process that accurately outputs the speech-related semantics. In this paper, a manual interactive synchronous translation program is designed with the input text as the search criterion, and the set is trimmed to obtain the best translation effect. The experimental results show that the sentence translation accuracy of the system is 0.9 ∼ 1.0. It is proved that the method based on deep learning solves the problem of low accuracy of the traditional translation system.

Neuro-fuzzy image compression using differential pulse code modulation and probabilistic decision making

Neuro-fuzzy image compression using differential pulse code modulation and probabilistic decision making

Wave File Compression Analysis and Implementation using The Huffman Method

One of the sound file formats that are widely used in the Windows operating system is the Wave format (*. WAV). This format is widely used for gaming and multimedia purposes. Wave is actually a raw format where the voice signal is directly recorded and quantized into digital data. The basic format of this file by default does not support compression and is known as PCM (Pulse Code Modulation). If you record a CD Audio quality song using a sampling rate of 44.1 kHz, 16 bits per sample, 2 channels (stereo), then the total media required to store this audio data per second is 176,400 bytes, so for a duration of 1 minute it takes 10,584 MB. If the average duration of one song is 5 minutes, it takes more than 50 MB of space to store the audio data of the song. This is of course very wasteful of storage media such as hard disks even though there are currently large hard disk capacities available.

Pixel Based Techniques for Gray Image Compression: A review

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution.
 This review is concerned with Differential pulse code modulation (DPCM) and pixel-based techniques, where the spatial domain is exploited to compress images efficiently in terms of compression performance and preserving quality. The new pixel-based method overcomes predictive coding constraints with fewer residues and higher compression ratios.