Compression Methodology Research Articles

Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques

Electrocardiogram (ECG) signals are the biomedical signals commonly used in the prognosis of cardiovascular diseases. ECG recordings need to be stored and transferred when telemedicine-based healthcare systems are required. These data are stored in a digitized format at higher bits per sample that requires ample space for storage. Therefore, this motivated us to develop efficient compression methodologies for ECG signals. In this regard, this work proposes compression techniques using the optimized tunable-Q wavelet transform (TQWT). For this purpose, TQWT parameters are optimized using several meta-heuristic optimization algorithms such as variants of PSO, ABC and its hybrid with PSO, GWO and its hybrid with PSO, and Sparse PSO. These hybrid methods and Sparse PSO have been utilized for the first time to optimize TQWT. Subsequently, thresholding and quantization are performed by using a dead-zone quantizer (DZQ). The quantized coefficients are encoded by utilizing a lossless compression technique run-length encoding (RLE). The proposed algorithms have been examined on the MIT-BIH arrhythmia database. It is clear from the results that significant compression has been achieved when compared to existing techniques. The performance of the proposed algorithms has been evaluated in terms of various evaluation parameters that are compression ratio (CR), percent-root-mean square difference (PRD), signal-to-noise ratio (SNR), and quality score (QS).

Automated Versus Manual Cardiopulmonary Resuscitation in Flight: Are We Being Safe?

ObjectiveThe primary purpose of this study was to compare the percentage of return of spontaneous circulation of in-flight cardiac arrest (IFCA) patients on admission to the emergency department (ED) who received in-flight standard cardiopulmonary resuscitation (s-CPR) versus automated cardiopulmonary resuscitation (a-CPR). SettingEMS helicopter (HEMS) service in Midwest USA. MethodsThis was a prospective, consecutive case series of adult patients who had IFCA of any cause managed with a-CPR between October 1, 2012, and February 8, 2016 (40 months), at a helicopter emergency medical service (HEMS) in the Midwestern United States. The series was compared with a historical control of patients who had IFCA managed by s-CPR between June 1, 2009, and September 30, 2012 (40 months). ResultsNinety-five runs (39 s-CPR and 54 a-CPR) were included. There was no significant difference in survival between the 2 groups upon HEMS leaving the ED. Cardiopulmonary resuscitation was performed for a significantly longer period of time in the a-CPR cohort than in the s-CPR cohort, and a significantly higher percentage of patients were undergoing active compressions upon loading into the aircraft in the a-CPR cohort. ConclusionThere was no difference in return of spontaneous circulation on ED admission between the 2 compression methodologies. In-flight use of a-CPR allows HEMS providers to be safe and compliant with Federal Aviation Administration regulations. It also meets the public and medical profession's expectations of the treatment of IFCA with high-quality cardiopulmonary resuscitation by HEMS.

Fragmented Huffman-Based Compression Methodology for CNN Targeting Resource-Constrained Edge Devices

Fragmented Huffman-Based Compression Methodology for CNN Targeting Resource-Constrained Edge Devices

Deep reinforcement learning approach to predict head movement in 360° videos

The popularity of 360° videos has grown immensely in the last few years. One probable reason is the availability of low-cost devices and ease in capturing them. Additionally, users have shown interest in this particular type of media due to its inherent feature of being immersive, which is completely absent in traditional 2D videos. Nowadays such powerful 360° videos have many applications such as generating various content-specific videos (gaming, knowledge, travel, sports, educational, etc.), during surgeries by medical professionals, in autonomous vehicles, etc. A typical 360° video when seen through a Head Mounted Display (HMD) gives an immersive feeling, where the viewer perceives standing within the real environment in a virtual platform. Similar to real life, at any point in time, the viewer can view only a particular region and not the entire 360° content. Viewers adopts physical movement for exploring the total 360° content. However, due to the large volume of 360° media, it faces challenges during transmission. Adaptive compression techniques have been incorporated in this regard, which is in accordance with the viewing behaviour of a viewer. Therefore, with the growing popularity and usage of 360° media, the adaptive compression methodologies are in development. One important factor in adaptive compression is the estimation of the natural field-of-view (FOV) of a viewer watching 360° content using a HMD. The FOV estimation task becomes more challenging due to the spatial displacement of the viewer with respect to the dynamically changing video content. In this work, we propose a model to estimate the FOV of a user viewing a 360° video using an HMD. This task is popularly known as the Virtual Cinematography. The proposed FOVSelectionNet is primarily based on a reinforcement learning framework. In addition to this, saliency estimation is proved to be a very powerful indicator for attention modelling. Therefore, in this proposed network we utilise a saliency indicator for driving the reward function of the reinforcement learning framework. Experiments are performed on the benchmark Pano2Vid 360° dataset, and the results are observed to be similar to human exploration

A Novel Image Processing Methodology for X-ray Image Compression and Enhancement

Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.

Quality Evaluation of Holographic Images Coded With Standard Codecs

Recently, a larger interest in the different plenoptic formats, including digital holograms, has emerged. Aside from other challenges that several steps of the holographic pipeline, from digital acquisition to display, have to face, visual quality assessment of compressed holograms is particularly demanding due to the distinct nature of this 3D image modality when compared to regular 2D imaging. There are few studies on holographic data quality assessment, particularly with respect to perceptual effects of lossy compression. This work aims to study the quality evaluation of digital hologram reconstructions, presented on regular 2D displays, in the presence of compression distortions. As there is no established or generally agreed compression methodology for digital holograms compression on the hologram plane with available implementations, a set of state-of-the-art compression codecs, namely HEVC, AV1, and JPEG2000, were used for compression of the digital holograms on the object plane. Both computer generated and optically generated holograms were considered. Two subjective tests were conducted to evaluate distortions caused by compression. The first subjective test was conducted on the reconstructed amplitude images of central views, while the second test was conducted on pseudo-videos generated from the reconstructed amplitudes of different views. The subjective quality assessment was based on mean opinion scores. A selection of objective quality metrics was evaluated, and their correlations with mean opinion scores were computed. The VIFp metrics appeared to have the highest correlation.

Methodology of neural network compression for multi-sensor transducernetwork models based on edge computing principles

This paper focuses on the development of a methodology to compress neural networks thatis based on the mechanism of prun-ingthe hidden layer neurons. The aforementioned neural networks are created in order to process the data generated by numerous sensors present in a transducer network that would be employed in a smart building. The proposed methodology implements a single approach for the compression of both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) that are used for the tasks of classification and regression. The main principle behind this method is based on the dropout mechanism, which is employed as a regulation mechanism for the neural networks. The idea behind the method proposed consists of selecting optimal exclusion probability of a hidden layer neuron, based on the redundancy of the said neuron. The novelty of this method is theusage of a custom compression network thatis based on an RNN, which allows us to determine the redundancy parameter not just in a sin-gle hidden layer, but across severallayers. The additional novelty aspect consists of an iterative optimization of the network-optimizer, to have continuous improvement of the redundancy parameter calculator of the input network. For the experimental evalu-ation of the proposed methodology, the task of image recognition with a low-resolution camera was chosen, the CIFAR10 dataset was used to emulate the scenario. The VGGNet Convolutional Neural Network, that contains convolutional and fully connected lay-ers, was used as the network under test for the purposes of this experiment. The following two methods were taken as the analogous state of the art, the MagBase method, which is based on the sparcification principle as well as the method which is based on rarefied representation by employing the approach of rarefied encoding SFAC. The results of the experiment demonstrated that the amount of parameters in the compressed model is only 2.56% of the original input model. This has allowed us to reduce the logical output time by 93.7% and energy consumption by 94.8%. The proposed method allows to effectively usingdeep neural networks in transducer networks that utilize the architecture of edge computing. This in turn allows the system to process the data in real time, reduce the energy consumption and logical output time as well as lower the memory and storage requirements of real-world applications.

Fixed Dose Formulation Development and Evaluation of Bilastine and Montelukast Sodium Tablets

Aim: The present research work was carried out to formulate stable fixed dose combination tablets of Bilastine and Montelukast Sodium, used to treat allergic rhinitis associated with asthma and rhino-conjunctivitis on basis of pre and post post-compression parameters evaluation and drug-drug-excipients compatibility studies.
 Methods: Direct compression methodology was used for tablet production and final composition of drugs and excipients was optimized by evaluating pre and post compression evaluations of blend and tablets respectively. The chemical instability and stability studies were carried out using HPLC method.
 Results: The Evaluation of pre-compression parameters of batch F1 to F5 shows that as we increase the amount of sodium starch glycolate and colloidal silicon dioxide from F1 to F5, bulk density and tapped density increases slightly whereas the compressibility index and hausner’s ratio of tablets was shifted from excellent to good. Angle of repose shows excellent flow property from F3-F5. After evaluation of post-compression parameters from F1 to F5, there is no significant difference in diameter, thickness and average weight of tablets. The hardness of tablets was decreased slightly from F1 to F5 therefore, the % friability was found to be increased from F1 to F5 and disintegration time was found to be decreased from F1 to F5. Dissolution studies shows % release of Bilastine and Montelukast was increased towards F1 to F5 as the percentage of Sodium Starch Glycolate increases. The drug-drug-excipients compatibility shows that there is no physical and chemical incompatibility between the drug-drug-excipients at accelerated conditions. The stability studies show that % assay of long term and accelerated samples are within 100±2%.
 Conclusion: The optimized composition found in order to scale up the production of tablets.

Proving Rural and Urban Telemedicine with Improved Diacom Image Compression Using EXI Mechanism

Digital Imaging and Communication (Diacom) is a medical image format used in telemedicine. Telemedicine is the core of the modern age. A physician in one area uses a telecommunications device to provide a patient with treatment at a distant site. It's a substitute horizon that the globe publicly pays attention to. Throughout telemedicine sessions, access to personal patient health records and health trackers is on the market, helping you diagnose and efficiently manage patients. It is the main task to reduce each measure of storage and transmission data required for communication. Diacom Image Compression (DIC) is primarily based on the new Efficient Extensible Interchange (EXI) mechanism to realize this goal. Efficient Extensible Interchange is a very lightweight, high-performance XML representation that was designed to function well in a wide variety of applications. It boosts performance while slashing bandwidth requirements, all while preserving other resources including battery life, code size, computing power, and memory. The proposed work focuses on advance compression methodology for patient’s historical data, electronic medical records of clinical information, and diagnostic test record. The results of the simulation recommends that the outcome of the proposed mechanism is better than the current approach. The present work compares the proposed methods with existing methodology and experimental analysis depicts that EXI based compression methodology is far better than traditional SVD compression methodology.

Process Modeling and Simulation of Tableting-An Agent-Based Simulation Methodology for Direct Compression.

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

On The Implementation of Densely Packed Decimal Number System Based Adder: Prospects and Challenges

Decimal digit number computation, through bit compression methodology, offers space and time saving, which can be incurred by the Chen-Ho and Densely Packed Decimal (DPD) coding techniques. Such coding techniques have a property of bit compression, like, three decimal digits can be represented by 10 bits instead of 12 bits in binary coded decimal (BCD) format. The compression has been obtained through the elimination of the redundant 0’s from BCD representation. This manuscript reports the pros and cons of the techniques mentioned above. The logic level functionalities have been examined through MATLAB, whereas circuit simulation has been erified through Cadence Spectre. Performance parameters (such as delay, power consumption) have been evaluated through CMOS gpdk45 nm technology. Furthermore, the best design has been chosen from them, and the decimal adder design technique has been incorporated in this paper.

Compressing PDF sets using generative adversarial networks

We present a compression algorithm for parton densities using synthetic replicas generated from the training of a generative adversarial network (GAN). The generated replicas are used to further enhance the statistics of a given Monte Carlo PDF set prior to compression. This results in a compression methodology that is able to provide a compressed set with smaller number of replicas and a more adequate representation of the original probability distribution. We also address the question of whether the GAN could be used as an alternative mechanism to avoid the fitting of large number of replicas.

Modified Chorological Coded Data Compression Methodology for Wireless Sensor Networks

Wireless Sensor Network (WSN) is a fast evolving current technology which is being employed in various applications. Despite its wide usage, WSNs have a few challenges to overcome to be called as an ideal technology. Some of the challenges are battery life time, memory storage and deployment issues. Batteries are the primary source of power supply to WSN and one of the major challenges is the energy constraint. This paper aims to propose a few techniques to better the energy efficiency of the sensor networks by saving sensor energy using data compression methodology. In this paper, a modified chorological coded data compression methodology is proposed (MCDC). This algorithm deals with assigning the sequence value to the given input information. If the assigned sequence value is a double digit number, it is converted in to single digit number. Double digit numbers and single digit numbers are combined. Separate location tables are generated for all double digit and single digit numbers. This procedure continues until all the sequence values are changed in to single digit number. Then the final single digit is assign with an equivalent Sequence Code (SC). MCDC algorithm is compared with DELTA compression and RUNLENGTH compression and a better compression ratio was achieved when compared with DELTA compression and RUNLENGTH compression algorithms.

WITHDRAWN: A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

For the advanced digital pictures an innovative joint data concealing and compression methodology is proposed in our system. This is achieved by utilizing side match vector quantization (SMVQ) and image inpainting. Information stowing away and compression are the two parts which can be integrated into a single module cleanly. Apart from the squares within the furthest left and the top of the image, all the remaining squares in the raster-examining request can be inserted with mystery data and compacted all the while by SMVQ or image encoding adaptively as per the present inserting bit on the sender facet. To regulate the visual twisting and fault dispersion vector quantization is additionally used for mind boggling squares. These two are brought by the liberal compression. The receiver can accomplish the extraction of mystery pieces and picture decompression effectively as per the index estimates in the segmented areas. This is subsequent to dividing the image packed codes into a progression of segments by the marker bits.

Low bit-rate compression of underwater image based on human visual system

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.

SuperSlash: A Unified Design Space Exploration and Model Compression Methodology for Design of Deep Learning Accelerators With Reduced Off-Chip Memory Access Volume

Deploying deep learning (DL) models on resource-constrained embedded devices is a challenging task. The limited on-chip memory on such devices results in increased off-chip memory access volume, thus limiting the size of DL models that can be efficiently realized in such systems. Design space exploration (DSE) under memory constraint, or to achieve minimal off-chip memory access volume, has recently received much attention. Unfortunately, DSE alone cannot reduce the amount of off-chip memory accesses beyond a certain point due to the fixed model size. Model compression via pruning can be employed to reduce the size of the model and the associated off-chip memory accesses. However, in this article, we demonstrate that pruned models with even the same accuracy and model size may require a different number of off-chip memory accesses depending upon the pruning strategy adopted. Thus, mainstream pruning techniques may not be closely tied to the design goals, and thereby hard to be integrated with existing DSE techniques. To overcome this problem, we propose SuperSlash, a unified solution for DSE and model compression. SuperSlash estimates off-chip memory access volume overhead of each layer of a DL model by exploring multiple design candidates. In particular, it evaluates multiple data reuse strategies for each layer, along with the possibility of layer fusion. Layer fusion aims at reducing the off-chip memory access volume by avoiding the intermediate off-chip storage of a layer's output and directly using it for processing of the subsequent layer. SuperSlash then guides the pruning process via a ranking function, which ranks each layer according to its explored off-chip memory access cost. We demonstrate that SuperSlash not only offers an extensive design space coverage but also provides lower off-chip memory access volume (up to 57.71%, 25.83%, 47.73%, and 29.02% reduction for VGG16, ResNet56, ResNet110, and MobileNetV1, respectively) as compared to the state-of-art.

A Comparative Analysis of Performance of Several Wavelet Based ECG Data Compression Methodologies

Compression of an electrocardiogram (ECG) signal has given much consideration to the researchers since the computer-aided analysis of ECG has come into being. In some critical cases, viz., astronauts, a person under cardiac surveillance, ambulatory patients and in Holter monitoring system, continuous ECG data recording and transmitting from one location to other location is required. However, the size of the recorded data becomes so voluminous, that its transmission of data becomes practically impossible. In this paper, ECG data compression using wavelet-based techniques are presented, such that: a) wavelet packet transform with run-length encoding (RLE), b) wavelet transform with Huffman encoding, c) wavelet transform with RLE and d) wavelet transform and Lempel ZivWelch (LZW). The results have been tested using MIT-BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia databases. The performances of these methodologies are examined in the quantitative and qualitative manner. From Tabular results, it can be observed that the methodology based on WT and LZW provides efficient results in terms of compression ratio (CR∼=20 to 30) and peak root mean square difference (PRD∼=0.01 to 1.8) both, hence the overall QS value is improved.

Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications

Even though the Convolutional Neural Networks (CNN) has shown superior results in the field of computer vision, it is still a challenging task to implement computer vision algorithms in real-time at the edge, especially using a low-cost IoT device due to high memory consumption and computation complexities in a CNN. Network compression methodologies such as weight pruning, filter pruning, and quantization are used to overcome the above mentioned problem. Even though filter pruning methodology has shown better performances compared to other techniques, irregularity of the number of filters pruned across different layers of a CNN might not comply with majority of the neural computing hardware architectures. In this paper, a novel greedy approach called cluster pruning has been proposed, which provides a structured way of removing filters in a CNN by considering the importance of filters and the underlying hardware architecture. The proposed methodology is compared with the conventional filter pruning algorithm on Pascal-VOC open dataset, and Head-Counting dataset, which is our own dataset developed to detect and count people entering a room. We benchmark our proposed method on three hardware architectures, namely CPU, GPU, and Intel Movidius Neural Computer Stick (NCS) using the popular SSD-MobileNet and SSD-SqueezeNet neural network architectures used for edge-AI vision applications. Results demonstrate that our method outperforms the conventional filter pruning methodology, using both datasets on above mentioned hardware architectures. Furthermore, a low cost IoT hardware setup consisting of an Intel Movidius-NCS is proposed to deploy an edge-AI application using our proposed pruning methodology.

Tensor Tucker Decomposition based Geometry Compression on Three Dimensional LiDAR Point Cloud Image

Data Visualization in static images is still dynamically growing and changing with time in recent days. In visualization applications, memory, time and bandwidth are crucial issues when handling the high resolution three dimensional (3D) Light Detection and Ranging (LiDAR) data and they progressively demand efficient data compression strategies. This shortage is strongly motivating us to develop an efficient 3D point cloud image compression methodology. This work introduces an innovative lossless compression algorithm for a 3D point cloud image based on higher-order singular value decomposition (HOSVD) technique. This algorithm starts with the preprocessing method which removes the unreliable 3D points and then it combines the HOSVD together with the normalization, predictive coding followed by Run Length encoding to compress the HOSVD coefficients. This work accomplished lower mean square error (MSE), high (infinitive) Peak signal noise ratio (PSNR) to produce the lossless decompressed 3D point cloud image. The storage size has been reduced to one by fourth of its original 3D LiDAR point cloud image size.

Developement and Characterization of Floating Tablets of Nizatidine for Peptic Ulcer

The objective of the present research work is to develop an ideal floating drug delivery system of nizatidine to increase the gastric residence time in stomach. To overcome the short half life and lower bioavailability of drug in tablet form we developed the drug in the form of effervescent floating tablet containing HPMC K100 and sodium bicarbonate by direct compression methodology. The prepared effervescent floating tablets were characterized by thickness, weight variation, hardness, friability, drug content uniformity, in vitro buoyancy time, swelling test, in vitro study and stability study and found that all formulations showed satisfactory results with enhanced half life and bioavailability that is among all formulations F1 formulation exhibited good drug release of 95.03% & has shown floating lag time 55 sec. Finally, it was concluded that formulations of nizatidine floating tablet were successfully prepared and found prolonged drug release for 12 hours thereby getting enhanced bioavailability, patient compliance by reducing dose frequency and gastric residence time.