Proposed Compression Algorithm Research Articles

Single-Frame-Based Data Compression for CAN Security

To authenticate a controller area network (CAN) data frame, a message authentication code (MAC) must be sent along with the CAN frame, but there is no space reserved for the MAC in the CAN frame. Recently, difference-based compression (DBC) algorithms have been used to create a space inside the frame. DBC has the advantage of being very efficient, but its drawback is that, if an error occurs in one frame, the effects of that error propagate to subsequent frames. In this paper, a CAN data compression algorithm is proposed that compresses the current frame without relying on previous frames. Therefore, an error generated in one frame cannot be propagated to subsequent frames. In addition, a CAN signal grouping technique is proposed based on entropy analysis. To efficiently authenticate CAN frames, the length of the compressed data must be 4 bytes or less (4BL). Simulation shows that the 4BL-compression ratio of a Kia Sorento vehicle is 99.36% in the DBC method, but 100% in the proposed method. In an LS Mtron tractor, the 4BL-compression ratio is 98.58% in the DBC method, but 100% in the proposed method. In addition, the execution time of the proposed compression algorithm is only 27.39% of that of the DBC algorithm. The results show that the proposed algorithm has better compression characteristics for CAN security than the DBC algorithms.

Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics.

Effective bitstream compression approaches for high speed digital systems

This paper describes the compression techniques to attain high bandwidth and memory requirements which is necessary in high speed embedded systems. The most commonly used bitstream compression technique is Dictionary based code compression (DCC) due to its high compression. In this technique, the bitstream which is not stored in the dictionaries generates it as an uncompressed format that needs large storage space. To mitigate this issue, two effective compression techniques named as Separated split LUT (SSL) and Bitmasked Separated split LUT (BSSL) are proposed to improve the compression ratio (CR) and bit saving rate (BSR).The proposed compression algorithms are coded in VHDL, synthesized using Xilinx ISE 14.2 and implemented in Kintex 7 Field Programmable Gate Array (FPGA).The proposed compression techniques are evaluated for various benchmark processors such as Sha, CRC, Dijkstra, FFT and Susan processor. Sha processor has the lowest bitstream entries and the CR is 59.75% and the CR for CRC, Dijkstra, FFT and Susan processor are 58, 45, 70 and 75, respectively.

Lightweight electrocardiogram signal compression

Cardiovascular diseases are the primary cause of death around the world. Cardiovascular ailments can be monitored continuously using wearable devices, which are resource-constrained and battery-operated. Efficient data compression is a promising method to reduce transmission energy cost and extend battery lifetime. In this paper, we first propose two novel pruning methods with pruning matrix’s entries taken from the set {−1, 0, 1}, which achieve similar reconstruction quality as compared to DCT pruning but with far less computational cost due to multiplierless operation. Reconstruction for these pruning methods is proposed using Gaussian elimination. Then we design two lightweight compression algorithms based on the pruning methods, i.e., sign compression algorithm and binary compression algorithm. The proposed compression algorithms and pruning methods are evaluated using performance metrics such as compression ratio, percentage root mean square difference, quality score, etc. Furthermore, The pruning methods are implemented in TelosB mote and execution time is evaluated. The experiments are conducted on the MIT-BIH arrhythmia database. The experimental results show that the proposed compression algorithms outperform the state-of-the-art methods.

Remote Sensing Image Compression Based on the Multiple Prior Information

Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000.

Image lossless compression algorithm optimization and FPGA implementation

In this thesis, a minimum redundant prefix coding with higher compression ratio and lower time complexity is proposed for lossless compression of HD images. The compression algorithm is based on Canonical Huffman coding, preprocesses the data source to be compressed according to the image data features, and then compresses the data in batches using the locally uneven features in the data, which improves the compression ratio by 1.678 times compared with the traditional canonical Huffman coding. During the implementation of the algorithm, the counting sorting method with lower time complexity and the code-length table construction method without relying on binary trees are used to reduce the complexity of the algorithm and achieve the purpose of real-time data processing by the system. Finally, the proposed compression algorithm is deployed in FPGA to improve the encoding rate by parallel hardware circuit and pipeline design.

Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.

Notice of Removal: A Triggering-and-Encoding Lossless Compression Scheme for Waveform Capturing Rate Enhancement of Android Bluetooth Oscilloscope

Android-based oscilloscopes have been developed for research and education purpose because they would be able to acquire, transmit, display and analyze any electrical signals and be mobile, operable easily. Due to the speed limitation of wireless transmission channel, Android Oscilloscopes have some limited performances such as bandwidth, waveform capturing rate (WCR) and so on. In this paper, we propose a real-time lossless data compression scheme to solve a bottleneck of continuous data flow from DAQ to smartphone. This compression scheme consists of triggering a signal and encoding differences between waveforms. Also, an advanced structure of Android Bluetooth Oscilloscope is proposed to implement the proposed compression algorithm with FPGA-based hardware and software. To evaluate WCR improvement, we firstly analyze relationship between WCR and compression ratio. And then the compression efficiency is verified by the MATLAB simulations using various waveforms such as simple sinusoidal, complex periodic, square, chirp waveforms. We finally study robustness of this instrument in the range of 0~15% noise amplitude and 0~2rad phase offset. All experimental results show that proposed scheme can enhance WCR and also be applied to the wireless transmission fields collecting and displaying electrical waveforms.

An efficient compression technique for Foetal phonocardiogram signals in remote healthcare monitoring systems

Remote Healthcare Monitoring Systems (RHMs) that employ fetal phonocardiography (fPCG) signals are highly efficient technologies for monitoring continuous and long-term fetal heart rate. Wearable devices used in RHMs still face a challenge that decreases their efficacy in terms of energy consumption because these devices have limited storage and are powered by batteries. This paper proposes an effective fPCG compression algorithm to reduce RHM energy consumption. In the proposed algorithm, the Discrete Orthogonal Charlier Moment (DOCMs) is used to extract features of the signal. The householder orthonormalization method (HOM) is used with the Charlier Moment to overcome the propagation of numerical errors that occur when computing high-order Charlier polynomials. The proposed algorithm’s performance is evaluated in terms of CR, PRD, SNR, PSNR, and QS and provides the average values 18.33, 0.21, 48.85, 68.86, and 90.88, respectively. The results of the comparison demonstrate the proposed compression algorithm’s superiority over other algorithms. It also tested in terms of compression speed and computational efficiency. The results indicate that the proposed algorithm has a high Compression speed (218.672 bps) and high computational efficiency (21.33). Additionally, the results reveal that the proposed algorithm decreases the energy consumption of a wearable device due to the transmission time decreasing for data by 3.68 s.

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

In today’s world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node; after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-II systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

An IoT Method for Telemedicine: Lossless Medical Image Compression Using Local Adaptive Blocks

Image compression gains prominence in picture archiving and communication systems for storage and transmission of data. The advancements in technology enable the usage of computer-assisted algorithms for disease diagnosis and therapeutic planning. In this research work, a prediction-based compression algorithm was proposed to compress DICOM images. The local adaptive block-based predictor compression technique utilizes neighborhood pixel gray values for prediction and the Huffman coder was employed for the encoding of predicted coefficients. The compression results were considered effective, in contrast with the JPEG lossless, Kernel Least Mean Square, Context Adaptive Block-Based Prediction, and Least Mean square Prediction approaches. The performance validation by metrics reveals the effectiveness of the proposed local adaptive block prediction-based approach. The proposed compression algorithm was implemented in Raspberry Pi B+ embedded processor as IoT application. The compressed images are transferred through the cloud to other nodes in the network, thus facilitating the teleradiology for disease diagnosis by physicians.

An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

Porting deep neural networks on the edge via dynamic K-means compression: A case study of plant disease detection

Cyber Physical Systems (CPS) totally revolutionized the way we interact with the world providing useful services that can support the human being in many aspects of his life. Artificial Intelligence (AI) is another important player for bringing intelligence to CPS and allows the realization of Intelligent Cyber Physical Systems where smart applications can run. However, the constrained hardware of these devices in terms of memory and computing power makes challenging the deployment and execution of powerful algorithms (e.g., deep neural networks). To address this problem, modern solutions involve the use of compression techniques to reduce the memory footprint of deep learning models while saving the accuracy performance. The proposed work focuses on plant disease detection which represents one of the biggest challenges in smart agriculture; in such a context, the possibility to perform a timely diagnosis on crops suspected to be infected can avoid the spread of diseases, thus saving a lot of time and money during the plantation works. In this paper, we realized an intelligent CPS on top of which we implemented an AI application, called Deep Leaf that exploits Convolutional Neural Networks to detect the main biotic stresses affecting crops. To meet the hardware requirements of the Edge device running our application, we propose a novel dynamic compression algorithm based on K-Means for the reduction of models footprint. Experimental results show that our detector is able to correctly classify the plant health condition with an accuracy of 95% and demonstrate the effectiveness of the proposed compression algorithm which is able to maintain the same accuracy of the original 32 bit float model, with an overall memory size reduction of about 85.2%.

High compression efficiency image compression algorithm based on subsampling for capsule endoscopy

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.

IoT BASED SMART AND EFFICIENT WIND TURBINE MONITORING SYSTEM

Wind Turbine industry has the improved latest generation of Wind turbines with bigger flexible blades, high tower, Good efficiency & low cost repairing in all platforms of wind turbines from Small wind mills to Ocean wind turbines. The Control centre is responsible for Monitoring and Controlling wind turbines in wind power farms. Various parameters like Oil level, Gas leakage, air pressure, vibrations & linear velocity, environmental condition like rain & humidity are to be monitored and controlled for proper working of the wind turbines. In the proposed work, smart and efficient turbine network architecture is designed to automate this process. The aim of the proposed work is to monitor the different parameters of the turbine using respective sensors. The acquired sensor data are uploaded to the cloud via WiFi module for online monitoring and further data analysis. IFTTT Server of Adafruit io cloud is used to send the warning notification of the critical sensor value to the concerned person. Also the sensor node life time is taken care by implementing a proposed compression algorithm in each node that reduces the amount of data transmitted and thereby the energy consumed during transmission.

Speeding up Natural Language Text Search using Compression

Text search is a well-known problem in computer science where the valid shifts of a pattern P in a text string T are found. This paper shows how to speed up text search by searching for P in a compressed version of T. A fast compression algorithm was designed for this aim. This algorithm is based on the assumption that T is restricted to the letters of a single natural language. Relying on this assumption, a letter, in T or P, is encoded into a single byte instead of the two-byte unicode which shortens the string on which a text search algorithm works. The main disadvantage of this approach is the restriction of the alphabet of T to be from a single natural language. However, wide range of text documents comply to this assumption. Another issue is the overhead that is required to compress P and T, but it was found that the proposed compression algorithm is so fast such that its run-time can be paid for and still save text search time. Different approaches to store compressed T are also explored. The conducted experimental study showed that this approach does actually reduce the text search time.

English

Three-dimensional (3D) modeling is applied in numerous fields such as medical imaging, archaeology, industrial fabrication of machine parts and tools, gaming, animation, architecture, and interior design. In several such applications, the 3D model is developed based on an initial two-dimensional (2D) image of the model. An important aspect of converting a 2D image into a 3D model is the preservation and representation of the associated color information. With the advent of 3D printing technology, colored 3D models can be developed from their corresponding 2D images and can then be printed with the aid of 3D slicing software. Although several file formats are available for supporting generic 3D printing, only a few of them support colored 3D printing. Additionally, if the 3D model is complex, the associated color data will be huge, which will lead to longer processing times and greater storage requirements for both the 3D modeling software and the 3D slicing software. Hence, in this study, a lossy compression algorithm is proposed for representing the color information associated with OBJ and MTL file formats. The proposed compression algorithm has been designed to reduce the memory storage required for representing a 3D model's color information without significantly affecting the model's visual color appearance.

Two-dimensional data-block compression of information-measuring system algorithms

The authors suggest a new approach to develop measurement data compression algorithms. According to that a data frame is considered as a bit sequence formed into a two-dimensional well-ordered linear structure. It is supposed that there is no explicit bind to the source data / specifications width within the collected structure. The researchers propose block compression algorithms. They take into account not only the relationship between one sensor readings in adjacent samples, but also the connection of the samples in one frame so that it is expected to contribute to higher compression efficiency. The studies of the proposed compression algorithms have shown that they can be effectively used for compressing data frames of information-measuring systems. The mid-compression ratio of the proposed block compression algorithms falls in the range 8.0 to 9.5, and a simple adaptive algorithm observed in the paper provides the maximum value of the average compression ratio.

Exhaustive Crisp Parameter Modification in Quantization Table for Effective Image Compression

In recent times, transmission of information over wireless channels is increasing at an exponential rate. Internet is major source of information; it can be in the form of video or images which alone size up to 72% of the global traffic. In order to tackle such immense data, available channel may not be enough for transmission or reception, in this regard it is imperative to use efficient compressions techniques to reduce its size. Compressed image quality depends on the Quantization Table performed after spatial transformation like Discrete Cosine Transform. Size of a raw image captured by Digital Single-Lens Reflex, having all of its traits can easily exceed 20 Mega Bytes. In proposed compression algorithm, a Crisp parameter p modification step is introduced for effective compression of an image by utilizing standard Joint Picture Expert Group Quantization Table as a baseline model. After implementation of the proposed algorithm, Mean Opinion Score is obtained from the masses through an online survey and it provide the scores of 47.633 at p = 1, 62.74 at p = 8, and 83.252 at p = 16. According to Mean opinion score, best trade-off between quality and size of an image is between the values of p ranges from 11-20, this is also proved by Mean Squared Error and Peak Signal to Noise Ratio, as their ranges are 0.00038-0.000301 and 34.09-34.92 dB respectively. Compression Ratio which is from 6.49-5.76 is also acceptable for the given range.

Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications

Advanced telemedicine requires the gathering of big data through wireless body area network or internet of things based applications. These networks perform several tasks that consume maximum energy while transmitting the big data, which in turn discharges the battery and would require a battery replacement frequently. Also, the big data to be transferred and stored would require a significant amount of storage space. The above problem is rectified by compressing the big data acquired from the sensors before transmission that would reduce the consumption of power as well as use the storage space efficiently. In this paper, a hybrid compression algorithm (HCA) based on Rice Golomb Coding is proposed. The efficiency of the proposed compression algorithm is tested on ECG data from the physionet ATM database and real-time data acquired from the ECG sensor. The proposed HCA comprises of both lossy and lossless compression. The real-time implementation of the proposed compression algorithm is carried out using NI myRIO hardware and LabVIEW graphical tool. The compressed data then stored in the Google cloud, and the analysis of storage space using the HCA shows a reduction of 70% storage space for 10 minutes of ECG data.