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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

This paper applies a data compression algorithm to designing a two-way intelligent self-service system for an electric power business hall and proposes a multiwavelet embedded zero-tree coding method and compression of electric power data. Based on the multiwavelet transform, a multiwavelet threshold power data compression algorithm is proposed, and the decomposition reconstruction comparison of the multiwavelet transform, and the implementation process of the compression algorithm are discussed. The implementation effect of the electric power intelligent business hall is discussed through the evaluation analysis of the service before and after the intelligent business hall. The results show that the platform operation effect score of the electric power intelligent service management platform project of company H is 0.8723. This paper's platform design and implementation provide useful research references for improving the service quality and efficiency of the electric power business hall.

Monitoring system is widely used to detect the environment parameters such as temperature, humidity and position information in cold chain logistic, modern agriculture, hospital and so on. Poor position precision, high communication cost, high packet loss rate are the main problems in current monitoring system. To solve these problems, the paper presents a new monitoring system based on Narrow Band Internet of Things (NB-IoT) and BeiDou system/Global System Position (BDS/GPS) dual-mode positioning. Considering the position precision, a dual-mode positioning circuit based on at6558 is designed, and the calculation formula of the positioning information of the monitored target has been derived. Subsequently, a communication network based on wh-nb75-ba NB-IoT module is designed after compared with the LoRa technology. According to the characteristics of high time correlation of sensor data, an adaptive optimal zero suppression (AOZS) compression algorithm is proposed to improve the efficiency of data transmission. Experiments prove the feasibility and effectiveness of the system from the aspects of measurement accuracy, positioning accuracy and communication performance. The temperature and humidity error are less than 1 °C and 5% RH respectively with the selected sensor chips. The position error is decided by several factors, including the number of satellites used for positioning, the monitored target moving speed and NB-IoT module lifetime period. When the monitored target is stationary, the positioning error is about 2 m, which is less than that of the single GPS or BDS mode. When the monitored target moves, the position error will increase. But the error is still less than that of the single GPS or BDS mode. Then the AOZS compression algorithm is used in actually experiment. The compression ratio (CR) of it is about 10% when the data amount increasing. In addition, the packet loss rate test experiment proves the high reliability of the proposed system.

Based on the development history of wavelet transformation’s applications in data compression, this paper studies thee wavelets’ property of being continuous and discrete, establishes a power quality data compression model of wavelet transformation, discusses the threshold coefficient compression algorithm after the wavelet transformation, and makes an improvement with the low-frequency, high-frequency and self-adaptive power quality of the threshold compression algorithm. In the end, this paper verifies through a simulation experiment that the algorithm is adaptable to the compression of power quality data and signals are able to maintain a relatively low distortion.

Wireless sensor network technology has the potential to reveal fine-grained, dynamic changes in monitored variables of outdoor landscape. But there are significant problems to be overcome in order to realize the vision in working systems, such as effective utilize of energy, prolong network life and improve sensor accuracy. This paper describes the design and evaluation of a sensor network with an effective data aggression algorithm applied in orchard microclimate monitor. A novel feature of the solution is its data compression algorithm design, in which all sensors were encoded with Morton code and a logical multi-lays cluster was constructed among the nodes. Making use of the similarity of the output of the sensors, the algorithm reduces network data amount and power cost significantly to prolong life of the network. Tests and experiments results are shown in diagrammatic form. The system was field tested over one month in Nankou farm located in Beijing province of China. The experimental results demonstrate that effective collecting of environmental information can be achieved by using our proposed system.

A statistics-oriented data compression method based on unconventional timer encryption is developed. Necessary complexity estimates are made. The method can be used to compress sms messages. A probabilistic analysis is performed and its efficiency is evaluated. The theoretical data compression rate is the world’s highest. The time complexity is determined, which can be used to find the archiving time taking into account the frequency of the text generator.

In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the back-end processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index (index compressor), and the second layer resides after the query parser for query compression (query compressor) to enable bit-level compressed index-query search. The data compression algorithm used in this model is the Hamming codes-based data compression (HCDC) algorithm, which is an asymmetric, lossless, bit-level algorithm permits CIQ search. The different components of the new Web model are implemented in a prototype CIQ test tool (CIQTT), which is used as a test bench to validate the accuracy and integrity of the retrieved data and evaluate the performance of the proposed model. The test results demonstrate that the proposed CIQ model reduces disk space requirements and searching time by more than 24%, and attains a 100% agreement when compared with an uncompressed model.

Using a time delay spectrometry system as an example, we consider ways of compactly archiving discrete signals with distinct spectral peaks in the low-frequency range.[PUBLICATION ABSTRACT]

Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing. An optical accelerator is designed to leverage a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a constant low power (~21 mW), providing a new avenue for optical computing.