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This article presents a voltage-controlled oscillator (VCO)-based analog front end (AFE) for ExG readout applications with both a wide dynamic range (DR) and high linearity. By using a differential pulse code modulation (DPCM) technique, VCO non-linearity is mitigated by operating the VCO in the small-signal linear regime. To minimize power consumption from the power-hungry gain error calibration, the effect of VCO gain variation within the DPCM loop is investigated, leading to a calibration-free DPCM via a $\Delta \Sigma $ truncator. At the circuit level, a coarse–fine phase decoding scheme is proposed to increase signal-to-quantization-noise ratio (SQNR) while avoiding metastability issues. Fabricated in 65 nm, the AFE occupies an area of 0.056 mm2. It achieves 94.2-dB SNDR and 95.1-dB DR in 500 Hz of bandwidth while dissipating 1.68- $\mu \text{W}$ power from a single 0.8-V supply, yielding a Schreier FoM of 178.9 dB. Importantly, the techniques enable a virtually distortion-less spectrum with a noise-floor limited spurious-free dynamic range (SFDR) of 128 dB. In addition, the AFE is validated with ECG and EOG recording, demonstrating complete signal capturing in the presence of motion/stimulation artifacts.

This article proposes the thermometer code converter, which eliminates the requirement of binary code converter to generate gray codes in different digital modulation techniques like pulse code modulation. The nanoscale faster low-power circuit for these thermometer code-to-gray code converter has been achieved with quantum-dot cellular automata (QCA). The proposed converter circuit is made up with new QCA 2:1 multiplexer, which dominates the other existing designs in terms of QCA cells and device density. The circuits are evaluated based on area and operating speed. The design consistency is verified through theoretical values. The dissipated energy explores that the designs have lower energy dissipation. Stuck-at-fault effect analysis on the circuits has been performed. Besides, defect analysis caused by single missing cells, single extra added cells and misplaced cells is also explored. Test vectors are proposed to achieve 100% defect coverage. As encoders, these circuits can be widely employed in those high-performance functions that impose extraordinary design constraints with respect to high frequency, minimal area and low energy consumption.

Music rhythm detection and tracking is an important part of the music comprehension system and visualization system. The music signal is subjected to a short-time Fourier transform to obtain the frequency spectrum. According to the perception characteristics of the human auditory system, the spectrum amplitude is logarithmically processed, and the endpoint intensity curve and the phase information of the peak value are output through half-wave rectification. The Pulse Code Modulation (PCM) characteristic value is extracted according to the autocorrelation characteristic of the endpoint intensity curve. This article proposes a rhythm detection algorithm based on multipath search and cluster analysis; that is, based on the clustering algorithm, it absorbs the idea of multipath tracking and proposes its own detection and tracking algorithm. It overcomes the weakness of the clustering algorithm that needs to use Musical Instrument Digital Interface (MIDI) auxiliary input to achieve the desired effect. This algorithm completely uses the PCM signal as the input, which is more robust than the clustering algorithm. The whole process is carried out in the time domain, and the amount of calculation is much smaller than the frequency domain calculation of multipath tracking, and the linear relationship with the rhythm of the music is much better than the filter bank algorithm. This algorithm can successfully detect the rhythm of the music with a strong sense of rhythm and can track the specific position of the rhythm point.

This article presents a high-dynamic-range (DR) voltage-controlled oscillator (VCO)-based front end for sensor readout applications. Unlike conventional VCO-based quantizers, which suffer from large voltage-to-frequency non-linearities, the proposed design leverages differential pulse-code modulation (DPCM) from compression theory to substantially reduce the amplitude of the signal incident to the VCO quantizer, thereby achieving an ultra-low total harmonic distortion (THD) of −112 dB. In addition, background digital gain calibration is employed to overcome gain deviation of the VCO, thus ensuring a robust design. Together with dynamic element matching (DEM), the techniques enable a high DR using only the first-order noise shaping inherent in VCO-based quantizers and a moderate $32\times $ oversampling ratio. Fabricated in 65 nm, the sensor front end consumes 3.2- $\mu \text{W}$ power and achieves an SNDR of 89 dB and a DR of 94 dB in 500 Hz of bandwidth. Together with a 1.18- $\mu \text{V}~_{\text {rms}}$ integrated input-referred noise, it achieves a noise efficiency factor (NEF) of 4 and a Schreier FoM of 171 dB.

In this paper, a simple image compression algorithm is proposed for wireless capsule endoscopy. The proposed algorithm consists of new simplified YUV colour space, corner clipping, uniform quantization, subsampling, differential pulse code modulation and Golomb Rice code. Simplified YUV colour space is proposed based on special nature of endoscopic images and provide good results. The quantization and subsampling are used as lossy compression techniques and fixed Golomb-Rice code is used to encode residual value obtained after differential pulse code modulation operation. Here performance of different combination of quantization and subsampling techniques are analyzed based combination along with the proposed compression algorithm provides compression ratio of 89.3% and peak signal noise ratio of 45.1. the proposed algorithm provided better results as compared to various reported algorithms in literature in term of CR and PSNR.

This letter presents a voltage-controlled oscillator (VCO)-based analog-to-digital converter (ADC) for audio applications using differential pulse code modulation (DPCM) to mitigate VCO voltage-to-frequency nonlinearity. To overcome VCO gain variation, a split-ADC architecture is employed where two sub-ADCs share one calibration unit, thereby enabling a quasideterministic background gain calibration with small delay and fast convergence speed. In addition, a hybrid dynamic element matching (DEM) is employed, which offers a lower delay and a smaller in-band mismatch noise compared to the segmented-tree DEM. Fabricated in 65 nm, the ADC achieves 94.2-dB SNDR, 97.3-dB SNR, and 100.3-dB DR in 20-kHz bandwidth while dissipating 142.6-μW power, yielding a DR-based FoM of 181.8 dB.

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

In the last years there has been a growing interest for nonlinear speech models. Several works have been published revealing the better performance of nonlinear techniques, but little attention has been dedicated to the implementation of the nonlinear model into real applications. This work is focused on the study of the behaviour of a nonlinear predictive model based on neural nets, in a speech waveform coder. Our novel scheme obtains an improvement in SEGSNR between 1 and 2 dB for an adaptive quantization ranging from 2 to 5 bits., Comment: 4 pages, published in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) Seattle, WA, USA. arXiv admin note: text overlap with arXiv:2203.01818

With the development of remote sensing technology, spatial and spectral resolutions of hyperspectral images have become increasingly dense. In order to overcome difficulties in the storage, transmission, and manipulation of hyperspectral images, an effective compression algorithm is requisite. The clustered differential pulse code modulation (C-DPCM), which is a prediction-based hyperspectral image lossless compression algorithm, can achieve a relatively high compression ratio, but its efficiency still requires improvement. This paper presents a parallel implementation of the C-DPCM algorithm on graphics processing units (GPUs) with the compute unified device architecture, which is a parallel computing platform and programming model developed by NVIDIA. Three optimization strategies are utilized to implement the C-DPCM algorithm in parallel, including a version that uses shared memory and registers, a version that employs multistream, and a version that uses multi-GPU. In addition, we studied how to assign all classes to each GPU to minimize the processing time. Finally, we reduced the compression time from approximately half an hour to an hour to several seconds, with almost no loss in accuracy.

In today’s world of information exchange, digital imaging of pathological information has grown very fast and hence plays an essential role in disease diagnosis. Radiological modalities such as Computed Tomography (CT) scanners, Magnetic Resonance Imaging (MRI), X-Ray are increasing massively. The demand for handling images in digital form has increased dramatically in recent years for efficient archiving and transmission that raises the need of efficient compression. In this paper, a near-lossless predictive based coding technique is proposed that address the low coding efficiency of lossless coding and low image quality of lossy coding. The proposed technique removes inter-pixel redundancy and physco-visual redundancy by employing Resolution Independent Gradient Edge Detector (RIGED) at optimal threshold value and optimal q-level for quantization respectively. Coding redundancy is removed by employing block based encoding at optimal fixed size block. Volumetric MR image datasets of 8 bit and 16 bit are utilized for implementation of the proposed technique. The proposed technique is compared with the state-of-the art techniques and outperforms Context Adaptive Lossless Image Compression (CALIC), Low Complexity Lossless Compression for Images (LOCO-I) and Differential Pulse Code Modulation (DPCM) with context switching NN predictor by 34.33%, 39.80% and 30.35% respectively.

In a computing environment, higher resolutions generally require more memory bandwidth, which inevitably leads to the consumption more power. This may become critical for the overall performance of mobile devices and graphic processor units with increased amounts of memory access and memory bandwidth. This paper proposes a lossless compression algorithm with a multiple differential pulse-code modulation variable sign code Golomb-Rice to reduce the memory bandwidth requirement. The efficiency of the proposed multiple differential pulse-code modulation is enhanced by selecting the optimal differential pulse code modulation mode. The experimental results show compression ratio of 1.99 for high-efficiency video coding image sequences and that the proposed lossless compression hardware can reduce the bus bandwidth requirement.

Compressive Video Sensing (CVS) is the video coding framework that uses compressive sensing (CS) theory into video codec to reduce the burden of encoding. Various existing methods are developed for CVS, but enhancing reconstruction speed and reconstruction quality remains a great challenge in video compression. Hence, an optimal method is developed using the proposed Differential Pulse Code Modulation+Conditional Autoregressive-Salp Swarm (DPCM+CA-SS) model for CVS system by considering the characteristics of CVS frame. The spatial redundancy of video frames is explored by partitioning the video frames into several blocks such that each block is measured using the measurement matrix. The measurement vectors are quantized using DPCM. The key frames are compressed into bits using Huffman coding, and the packet is transmitted to the decoder side. The temporal redundancy of CVS model is explored by encoding the frames using CA-SS-based prediction model to achieve Motion Aligned (MA) optimal reconstruction. However, the proposed CA-SS is designed by the integration of Conditional Autoregressive Value at Risk (CAViaR) and Salp Swarm Algorithm (SSA), respectively. However, the proposed DPCM+CA-SS obtained maximum PSNR and SSIM of 39.6649 db and 0.9120, and the minimum total time of 4.9523 s, respectively.

Audio file size is relatively larger when compared to files with text format. Large files can cause various obstacles in the form of large space requirements for storage and a long enough time in the shipping process. File compression is one solution that can be done to overcome the problem of large file sizes. Arithmetic coding is one algorithm that can be used to compress audio files. The arithmetic coding algorithm encodes the audio file and changes one row of input symbols with a floating point number and obtains the output of the encoding in the form of a number of values greater than 0 and smaller than 1. The process of compression and decompression of audio files in this study is done against several wave files. Wave files are standard audio file formats developed by Microsoft and IBM that are stored using PCM (Pulse Code Modulation) coding. The wave file compression ratio obtained in this study was 16.12 percent with an average compression process time of 45.89 seconds, while the average decompression time was 0.32 seconds.

With the rapid development of hyperspectral remote sensing technology, the spatial resolution and spectral resolution of hyperspectral images are continually increasing, resulting in a continual increase in the scale of hyperspectral data. At present, hyperspectral lossless compression technology has reached a bottleneck. Simultaneously, the rise of deep learning has provided us with new ideas. Therefore, this paper examines the use of deep learning for the lossless compression of hyperspectral images. In view of the differential pulse code modulation (DPCM) method being insufficient for predicting spectral band information, the proposed method, called C-DPCM-RNN, uses a deep recurrent neural network (RNN) to improve the traditional DPCM method and improve the generalization ability and prediction accuracy of the model. The final experimental result shows that C-DPCM-RNN achieves better compression on a set of calibrated AVIRIS test images provided by the Multispectral and Hyperspectral Data Compression Working Group of the Consultative Committee for Space Data Systems in 2006. C-DPCM-RNN overcomes the limits of traditional methods in its performance on uncalibrated AVIRIS test images.

Screen content sequences are ubiquitous type of video data in numerous multimedia applications like video conferencing, remote education, and cloud gaming. These sequences are characterized for depicting a mix of computer generated graphics, text, and camera-captured material. Such a mix poses several challenges, as the content usually depicts multiple strong discontinuities, which are hard to encode using current techniques. Differential pulse code modulation (DPCM)-based intra-prediction has shown to improve coding efficiency for these sequences. In this paper we propose sample-based edge and angular prediction (SEAP), a collection of DPCM-based intra-prediction modes to improve lossless coding of screen content. SEAP is aimed at accurately predicting regions depicting not only camera-captured material, but also those depicting strong edges. It incorporates modes that allow selecting the best predictor for each pixel individually based on the characteristics of the causal neighborhood of the target pixel. We incorporate SEAP into HEVC intra-prediction. Evaluation results on various screen content sequences show the advantages of SEAP over other DPCM-based approaches, with bit-rate reductions of up to 19.56% compared to standardized RDPCM. When used in conjunction with the coding tools of the screen content coding extensions, SEAP provides bit-rate reductions of up to 8.63% compared to RDPCM.

PCM system telemetry is the most widespread system in the field of aerospace telemetry in the day, it is a kind of telemetry system which is widely used in the current space telemetry. In this paper, real-time IMU sensor data transmission through Pulse Code Modulation (PCM) concept based on telemetry design is proposed. Telemetry using smartphone IMU sensor real-time data which is using Xbee radio has been carried out. Xbee radio which acts as a transmitter is connected to a smartphone using OTG. While the Xbee Radio which acts as a receiver is connected to the PC. Data transmission uses the PCM format where data is sent in the form of a combination of high and low signals with a certain resolution. The test result proves that real-time IMU sensor data transmission through the PCM concept based on telemetry design was successfully carried out without any data losses.

The lossless intra-prediction coding modality of the High Efficiency Video Coding standard provides high coding performance while allowing frame-by-frame basis access to the coded data. This is of interest in many professional applications, such as medical imaging, automotive vision, and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have been proposed recently, most of them based on sample-wise prediction using differential pulse code modulation (DPCM). Other recent proposals aim at further reducing the energy of intra-predicted residual blocks. However, the energy reduction achieved is frequently minimal due to the difficulty of correctly predicting the sign and magnitude of residual values. In this paper, we pursue a novel approach to this energy-reduction problem using piecewise mapping (pwm) functions. In particular, we analyze the range of values in residual blocks and apply accordingly a pwm function to map specific residual values to unique lower values. We encode the appropriate parameters associated with the pwm functions at the encoder, so that the corresponding inverse pwm functions at the decoder can map values back to the same residual values. These residual values are then used to reconstruct the original signal. This mapping is, therefore, reversible and introduces no losses. We evaluate the pwm functions on $4\times 4$ residual blocks computed after DPCM-based prediction for lossless coding of a variety of camera-captured and screen content sequences. Evaluation results show that the pwm functions can attain the maximum bit-rate reductions of 5.54% and 28.33% for screen content material compared with DPCM-based and block-wise intra-prediction, respectively. Compared with intra-block copy, piecewise mapping can attain the maximum bit-rate reductions of 11.48% for a camera-captured material.

We propose and experimentally demonstrate a hybrid digital-analog radio-over-fiber technique based on cascaded digital PCS-121-QAM modulation and analog pulse code modulation for each radio waveform sample, achieving 12.8 dB SNR gain over A-RoF and 1.38% EVM.

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Audio data has special and unique properties: e.g. while it is useful to occasionally drop a video frame from a stream, we simply cannot do the same with audio information or all sense will be lost from that dimension. Therefore, how to sensibly compress sound information is an important question. We begin with a discussion of just what makes up sound, and consider the digitization of sound information. We introduce the Nyquist Theorem as a fundamental property of sampling. Signal-to-Noise Ratio (SNR) is defined and adopted as a useful measure of audio (and in general, signal) quality, including the effect of quantization noise. Linear and nonlinear quantization, including companding for audio data, are discussed. Synthetic sounds are introduced, and we then go on to a thorough introduction to the use of MIDI as an enabling technology to capture, store, and play back musical notes. We look at some details of audio quantization, and give introductory information on how digital audio is dealt with for storage and transmission. This entails a first discussion of how subtraction of signals from predicted values yields numbers that are close to zero, and hence easier to deal with Pulse Code Modulation (PCM) is introduced, followed by differential coding of audio and lossless predictive coding. Finally, Differential Pulse Code Modulation (DPCM) and Adaptive DPCM are introduced, and we take a look at encoder/decoder schema.

The telemetry data of the sounding rocket when flight in the space is very important to known. This data will be used for next mission or evaluation the last rocket flight. Commonly the data of the sensor in the sounding rocket are accelerometer, gyroscope, magnetometer, GPS and so on. As much data as possible than can be sent to the ground control system when the rocket flies. The problem is when the baud rate is low so not much data can be transmitted. Another problem is when the using serial bit standard communication such as the baud rate is 115.200 which mean that the maximum data in one second is 14,400 characters. It is less than when using the PCM bit rate. Application of PCM in the telemetry for sounding rocket LAPAN is new and need development to reach the optimal. With the PCM method the communication data can transmit around 1.25Mbps or 156,250 characters based on the TR FM02-S-2 full S-band transmitter. In this research, focus to the implementation the PCM method on the FPGA for sending and receiving the data via cable. Two FPGA boards can work together for sending and receiving data with PCM method with the total bit is 1.25Mbps.

In this paper, we present an approach for investigating the pulse code modulation for signals with various shape forms. We have developed an algorithm as well as its course of implementation in MATLAB and Octave environments. The software we have implemented makes it possible to investigate the Pulse Code Modulation of various analog signals along with the signal parameter influence and parameters of PCM on the discrete and digital signals. It is possible to use the presented approach in education, in the field of engineering, to study this process.

A speech signal has a total duration of 10 s. It is sampled at a rate of 8 kHz and then encoded. The \(\text{ SNR}_Q\) must be greater than 40 dB. Calculate the minimum storage capacity required to accommodate this digitized speech signal.

A 24-bit 96kHz sampling rate DAC (Digital-to-Analog Converter) achieves 106dB Dynamic Range. A 3rd order 6bit ∑-∆ modulator is included (SDM). To reduce the sensitivity of the DAC and the non-linearity error we use DWA (Data weighted Averaging) in Dynamic Element Matching Technique (DEM). A 3-bit Quantizer and 3-7 Binary to Thermometric Decoder is introduced in the proposed 24-bit audio DAC. This paper presents stereo input, stereo output and a programmable sampling frequency ranging from 8 kHz to 48 kHz with the help of Verilog Modeling and has done some optimization before implementation. The DAC accepts 16/18/24 bit PCM (Pulse-Code Modulation) data at a sampling rate of 32/44.1/96kHz for applications in CD and DVD audio.

This paper presents a VCO-based sensor front-end utilizing differential pulse-code modulation (DPCM) to substantially reduce the amplitude of the input signal to the VCO quantizer so that high linearity is achieved. Together with a background digital gain calibration and DEM, the techniques enable a high dynamic range (DR) using only 1st order noise-shaping and 32× oversampling. Fabricated in 65nm, the AFE consumes 3.2μW and achieves 1.18μV RMS input-referred noise in 500Hz BW. It also achieves an SNDR/DR/THD of 88/94/-105dB respectively with a NEF of 4 and Schreier FoM of 170dB.

The sensor nodes in IoT are usually supplied by energy using batteries with limited capacity. Therefore, saving energy as much as possible is important for increasing their lifetime and thus allowing their use in real applications. Because radio communication is usually the major reason of energy consumption, among the most commonly adopted energy-saving methods is to reduce data transmission / reception, for example, by compressing data. Taking advantage of the high correlation which usually occurs between successive samples captured by IoT sensor nodes and using the entropy principle in compression, a simple lossless algorithm for compression built on Differential Pulse Code Modulation (DPCM) was suggested in this paper, that is especially useful for IoT sensor nodes that characterized by reduced memory and computational resources. Compared to the state-of-the-art approaches, the findings of our simulation experiments showed that the suggested solution substantially decreased the consumption of energy and enhanced the network lifetime for all sensor nodes.

A unique method of improving energy efficiency in fluid power systems is called digital flow control. In this paper, binary coding control is utilized. Although this scheme is characterized by a small package size and low energy consumption, it is influenced by higher pressure peaks and larger transient uncertainty than are other coding schemes, e.g., Fibonacci coding and pulse number modulation, consequently resulting in poor tracking accuracy. This issue can be solved by introducing a delay in the signal opening/ closing of the previous or subsequent valve, thus providing sufficient time for state alteration and valve processes. In a metering-in velocity control circuit, a feedforward neural network controller was used to create artificial delays according to the pressure difference over the digital flow control unit (DFCU) valves. The delayed signal samples fed to the controller were acquired through the genetic algorithm method, and the analysis was performed with MATLAB software.

Binaural sound coding strategies can improve speech intelligibility for cochlear implant (CI) users. These require a signal transmission between two CIs. As power consumption needs to be kept low in CIs, efficient coding or bit-rate reduction of the signals is necessary. In this work, it is proposed to code the electrical signals or excitation patterns (EP) of the CI instead of the audio signals captured by the microphones. For this purpose we designed a differential pulse code modulation based codec with zero algorithmic delay to code the EP of the advanced combination encoder (ACE) sound coding strategy for CIs. Our EP codec was compared to the G.722 64 kbit/s audio codec using the signal-to-noise ratio (SNR) as objective measure of quality. On two audio-sets the mean SNR was 0.5 to 13.9 dB higher when coding the EP with the proposed coding method while achieving a mean bit-rate between 34.1 and 40.3 kbit/s.

Pulse code modulation is one of the techniques in data or voice telecommunication. Commonly, the PCM telemetry data is useful in aerospace communication, such as in aircraft, satellite, rocket, and UAV. Today, the research about PCM in Rocket Technology Center, LAPAN is very important to improve the telemetry data on the rocket to the ground station and vice versa. This paper proposes the design of PCM encoder with FPGA Cyclone V. The input of the ADC is analog signal like potentiometer and the output will be monitored by an oscilloscope to see the PCM signal. The PCM signal and clock generator is shown on the oscilloscope as the information that the preliminary design of PCM encoder has been done.

Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially crucial in fields such as remote sensing healthcare network, security and military applications as the quality of images needs to be maintained to avoid any errors during analysis or diagnosis. In this paper, a hybrid prediction lossless image compression algorithm is proposed to address these issues. The algorithm is achieved by combining predictive Differential Pulse Code Modulation (DPCM) and Integer Wavelet Transform (IWT). Entropy and compression ratio calculation are used to analyze the performance of the designed coding. The analysis shows that the best hybrid predictive algorithm is the sequence of DPCM-IWT-Huffman which has bits sizes reduced by 36%, 48%, 34% and 13% for tested images of Lena, Cameraman, Pepper and Baboon, respectively.

Paired carrier multiple access (PCMA) is one of the most common single-channel mixtures. It is still a great challenge to recover the transmitted bits from non-cooperative received PCMA signals, due to the high complexity of existing single-channel blind source separation (SCBSS) algorithms. Hence, a double-direction delayed-decision-feedback sequence estimation (DD-DDFSE) algorithm for non-causal high-order channel is proposed to realise the separation of PCMA signals. The proposed algorithm employs the Viterbi algorithm (VA) with low-order channel twice instead of conventional VA with high-order channel once. The symbols estimated by the initial VA are utilised to equalise the causal and non-causal taps of the channel beyond the trellis state in the second VA. The relationship between the decision-feedback error from the initial VA and the performance of the second VA is also derived. Compared with state-of-the-art maximum likelihood sequence estimation algorithms, DD-DDFSE algorithm can not only decrease the computation complexity but also improve the separation performance.

This paper presents new detectors developed to handle the severe nonlinear distortion introduced by 32 kb/s Adaptive Differential Pulse Code Modulation (ADPCM). These detectors are tested against previously developed detector named system-B using data transmission at 9.6 kb/s over ADPCM link in cascade with telephone channel. Simulation results show that the error rates of the proposed detectors approach negligible values at certain signal-to-noise-ratio (SNR), while, the error rate of system-B remains constant at certain value.

Sounds recorded by a dashboard camera presents essential evidence on a vehicle's driving condition for analyzing car accidents. In reality, when a direct impact of a vehicle equipped with a dashboard camera occurs, the power supply to the dashboard camera is abruptly terminated, and the digital video being recorded at that time may be incompletely stored; thus, there is no information such as the location or size to be read for the image and audio decoding. In this paper, we propose a method for recovering the linear pulse code modulation (PCM) audio from the impaired MP4 file partially stored in a video event data recorder (VEDR), known as a dashboard camera, during road vehicle accidents. This method is based on the principle of extracting an actual audio signal using the energy of each frame in the pseudo audio, which is generated from all data in the “mdat” area of the impaired MP4 file. For performance evaluation, the various video files with linear PCM audio format in the MP4 container were obtained through the different dashboard cameras. As the objective evaluation, we measured the audio recovery ratio, distance based on dynamic time warping (DTW) and comparison of the waveform and spectrogram. Moreover, the performance of the proposed method was evaluated using the multiple stimuli with a hidden reference and anchor (MUSHRA) test as the subjective assessment. As a result, it is shown from the evaluation that the proposed method can recover significantly better the linear PCM audio signal than the conventional methods from an impaired MP4 file.

In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.

In this paper, we specifically design an efficient compressive sensing video (CSV) coding framework for the CSV system, by considering the distribution characteristics of the CSV frame. To explore the spatial redundancy of the CSV, the CSV frame is first divided into blocks and each block is modeled by a Gaussian mixture model (GMM), and then it is compressed by a product vector quantization. We further explore the temporal redundancy of the CSV by encoding the adjacent CSV frames by the differential pulse code modulation technique and the arithmetic encoding technique. Experiment results show that the proposed CSV coding solution maintains low coding complexity, which is required by the CSV system. Meanwhile, it achieves significant BD-PSNR improvement by about 7.1311.41dB (or equivalently 51.2366.96% bitrate savings) compared with four existing video coding solutions, which also have low computational complexity and suit for the CSV system. A new efficient coding framework for compressive sensing video (CSV) is proposed.The proposed framework is based on GMM and product vector quantizer.The proposed framework achieves a significant coding rate-distortion improvement.The computational complexity of the proposed CSV encoding algorithm is low.

A digital class-D amplifier (CDA) converts an audio digital stream into sound directly and power-efficiently. It first encodes the pulse-code-modulated audio input into a digital pulse-width-modulated (PWM) signal. It needs a digital-to-pulse converter (DPC) to translate this digital PWM signal into a series of analog binary pulses accurately. We report a 5-3 segmented DPC that includes both a counter and a delay line for pulse width conversion. The timing skews along the delay line are detected using a zero-crossing detection scheme and corrected in the digital domain. This calibration can operate continuously in the background. A digital CDA prototype was fabricated using a 65-nm CMOS technology. It includes the aforementioned PWM modulator and DPC. It also integrated an open-loop switching driver to deliver the DPC’s output to a speaker. This digital CDA consumes $875~ {\mu }\text {W}$ under a 1-V supply when the input is zero and no output power is transferred to the external load. It can deliver 13.3 mW to a $32~ {\Omega }$ resistive load in the H-bridge topology with 89% power efficiency. For a 1-kHz sine-wave input, it achieves 95 dBA dynamic range, 93.6 dBA peak SNR, 86.4 dBA peak SNDR, and 0.006% THD at -2-dBFS input level. The core area of the chip is $ {0.87\times 0.5\,\,\text {mm}^{2}}$ .

A new modular and programmable wireless capsule endoscope is presented in this paper. The capsule system consumes low power and has small physical size. A new image compression algorithm is presented in this paper to reduce power consumption and silicon area. The compression algorithm includes color space transform, uniform quantization, sub-sampling, differential pulse code modulation (DPCM) and Golomb-Rice code. The algorithm is tested in a field programmable gate array (FPGA) development board, and the final result achieves 80% compression rate at 40 dB peak signal to noise ratio (PSNR). The algorithm has high image compression efficiency and low power consumption, compared to other existing works. The system is composed of the following three parts: image capsule endoscope, portable wireless receiver and host computer software. The software and hardware design of the three parts are disscussed in details.

In this paper, the limitations of digital-to-analog (D/A) conversion by Digital Pulse Width Modulation (DPWM) are addressed and the novel Dyadic Digital Pulse Modulation (DDPM) technique for all-digital, low cost, high resolution, Nyquist-rate D/A conversion is proposed. Thanks to the spectral characteristics of the new modulation, in particular, the requirements of the filter needed to extract the baseband component of DPWM signals can be significantly released so that to be suitable to inexpensive integration on silicon in analog interfaces for nanoscale integrated systems. After the new DDPM technique and its properties are introduced on a theoretical basis, the implementation of a D/A converter (DAC) based on the proposed modulation is addressed and its performance in terms of noise and linearity is discussed. A 16-bit DDPM-DAC prototype is finally synthesized on a field-programmable gate array (FPGA) and experimentally characterized.

A fixed rate speech coder based on the filter bank and the non-uniform sampling technique is proposed. The non-uniform sampling is achieved by the detection of inflection points (IPs). A speech block is band passed by the filter bank, and the subband signals are processed by the IP detector, and the detected IP patterns are compared with entries of the IP database. For each subband signal, the address of the closest member of the database and the energy of the IP pattern are transmitted through channel. In the receiver, the decoder recovers the subband signals using the received addresses and the energy information, and reconstructs the speech via the filter bank summation. As results, the coder shows fixed data rate contrary to the existing speech coders based on the non-uniform sampling. Through computer simulation, the usefulness of the proposed technique is confirmed. The signal-to-noise ratio (SNR) performance of the proposed method is comparable to that of the uniform sampled pulse code modulation (PCM) below 20 kbps data rate.

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode’s Bjontegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

The output of pulse code modulation (PCM) encoder is statistically described; this description is used at the receiver to reduce the error of the received signal. Recently, PCM is the most important form of pulse modulation. Bit errors occur at the transmission of information through the communication system and the amount of error depends on the characteristics of the channel. In this paper, the voice message is sampled greater than Nyquist rate and quantized with scalar quantizer, then the quantized levels are encoded with resolution B bits assigned to each level. The binary data are mapped and transmitted over Gaussian noise channel. The system was designed without channel coding. At the receiver the statistical description is exploited to conceal the errors. The results show that a significant improvement gained in bit error rate that was reduced from 0.078 to 0.052 at 0 dB of Eb N0⁄ by soft decoding, compared to hard decoding.

A codec is a device which helps to encode or decode digital data signals, it encodes a data stream or encrypted form and a decoder operates a reverse process of encoder. This codec's are applied in signal editing, video editing; signal conferencing, video conferencing etc. The codec's have two major principles like pulse code modulation method and delta modulation method. In this paper, we proposed a survey of various effective codec implementation and performance analyzing methods with different applications. The various effective codec's are implemented such as CELP codec, MELP codec, SPEEX speech codec, and OPUS speech codec. A CELP codec is code excited linear prediction method which helps to increase bit detection rate, high efficient process in speech coding, and peak quality in low bit rates. A CELP codec is to be applied in real time advanced technologies such as automatic watermarking process, communication field, Zigbee applications, filtering process, power spectrum analyzing fields, quantization methods, Gaussian processing fields, ARM processors etc. A MELP is a mixed excitation liner prediction method which helps to eliminate the voicing error obtains in multiple state excitation method. A SPEEX method helps to analyze speech quality and bit rate and an OPUS is high flexible speech codec technique which plays a vital role in real time applications. These methods are implemented and performances are analyzed based on different real world applications.

In this paper, new parameterized differential pulse-code modulation interpolator is proposed. It uses the correspondence of signal samples in the processed sample vicinity to select the interpolation method. Interpolation error of proposed interpolator is minimized during the optimization procedure. Before the optimization, the parameter range dimension is reduced in order to lower the computational complexity. Experimental research shows that proposed interpolator outperforms the non-parameterized one

In this paper, a Hermitian symmetry based orthogonal frequency division multiplexing (HS-OFDM) signal transmitter is proposed. It can be used in a digitized radio-over-fiber (DRoF) system to reduce the system complexity by transmitting a real digital sequence. In this HS-OFDM transmitter, differential pulse code modulation (DPCM) schemes are used instead of the traditional pulse code modulation (PCM) to quantify the bandpass sampled signals, which can improve the signal-to-quantization noise ratio (SQNR) by 4~7 dB. Moreover, a high-order pulse amplitude modulation (PAM) is utilized to increase the spectral efficiency of the DRoF system. Simulation results show that the digital code with PAM- 16 can improve the spectral efficiency by 3 times with the same error vector magnitude (EVM) performance.

This work presents a fully digital implementation of an audio front-end for portable applications developed in a low-power perspective. Such platform acquires audio samples through a four digital Pulse Digital Modulation (PDM) microphones array and implements two-channel (left and right) beamformers to perform speech enhancement. The system also features a low-power Voice Activity Detector (VAD) for the implementation of source localization or noise reduction algorithms. Finally, the processed data can be converted into a 16-bit resolution Pulse Code Modulation (PCM) format and then transmitted through a standard Integrated Interchip Sound (I2S) interface. The front-end has been implemented onboard a Microsemi IGLOO nano Field Programmable Gate Array (FPGA), reaching a power consumption of only 2.264 mW. The low-power architecture and the implemented functionalities make the system a promising front-end for audio speech portable applications.

Demand for wireless capsule endoscopy is increasing rapidly due to its simplicity and comfortable procedure. However, the wireless capsule endoscopy lack in complete diagnosing of gastrointestinal tract due to its limited power supply and size. Low complexity image compression algorithm plays vital role in saving power and size by reducing the data as transmitter consume 60% of capsule power. A high efficiency and lossless image compression algorithm is proposed, which is a combination of uniform quantization, simple predictive coding and Golomb Rice code. In the proposed algorithm, RGB colour image is quantized using uniform quantization. Then, differential pulse code modulation is applied, where current pixel value is subtracted with previous pixel value to provide a difference error value. The difference error value is encoded using Golomb Rice code. Several endoscopic images are considered for evaluating the performance and efficiency of proposed algorithm. The proposed algorithm provided the compression ratio of 72.5 with less computational complexity and memory usage.