TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data.In this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery.Existing geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of generalization.To address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data.The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing rates.Concurrently,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing data.Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.

2.5Gb/s 0.18μm CMOS Clock and Data Recovery Circuit

A 2.5Gb/s clock and data recovery （CDR） circuit is designed and realized in TSMC＇s standard 0.18/μm CMOS process. The clock recovery is based on a PLL. For phase noise optimization,a dynamic phase and frequency detector （PFD） is used in the PLL. The rms jitter of the recovered 2.5GHz clock is 2.4ps and the SSB phase noise is - 111dBc/Hz at 10kHz offset. The rms jitter of the recovered 2.5Gb/s data is 3.3ps. The power consumption is 120mW.

2.488 Gbit/s clock and data recovery circuit in 0.35 μm CMOS

The design of a 2. 488 Gbit/s clock and data recovery （CDR） If for synchronous digital hierarchy （SDH） STM-16 receiver is described. Based on the injected phase-locked loop （IPLL） and D-flip flop architectures, the CDR IC was implemented in a standard 0. 35 μan complementary metal-oxide-semiconductor （CMOS） technology. With 2^31 -1 pseudorandom bit sequences （PRBS） input, the sensitivity of data recovery circuit is less than 20 mV with 10^-12 bit error rate （BER）. The recovered clock shows a root mean square （rms） jitter of 2. 8 ps and a phase noise of - 110 dBc/Hz at 100 kHz offset. The capture range of the circuit is larger than 40 MHz. With a 5 V supply, the circuit consumes 680 mW and the chip area is 1.49 mm × 1 mm.

Analysis and Design of a Phase Interpolator for Clock and Data Recovery

In this paper,a detailed analysis of a phase interpolator for clock recovery is presented. A mathematical model is setup for the phase interpolator and we perform a precise analysis using this model. The result shows that the output amplitude and linearity of phase interpolator is primarily related to the difference between the two input phases. A new encoding pattern is given to solve this problem. Analysis in the circuit domain was also undertaken. The simulation results show that the relation between RC time-constant and time difference of input clocks affects the linearity of the phase interpolator. To alleviate this undesired effect, two adjustable-RC buffers are added at the input of the PI. Finally,a 90nm CMOS phase interpolator,which can work in the frequency from 1GHz to 5GHz,is proposed. The power dissipation of the phase interpolator is lmW with a 1.2V power supply. Experiment results show that the phase interpolator has a monotone output phase and good linearity.

2.5Gb/s/ch 0.18μm CMOS Data Recovery Circuit

A 2.5Gb/s/ch data recovery （DR） circuit is designed for an SFI-5 interface. To make the parallel data bit-synchronization and reduce the bit error rate （BER） ,a delay locked loop （DLL） is used to place the center of the data eye exactly at the rising edge of the data-sampling clock. A single channel DR circuit was fabricated in TSMC＇s standard 0. 18μm CMOS process. The chip area is 0. 46mm^2. With a 2^32 - 1 pseudorandom bit sequence （PRBS） input,the RMS jitter of the recovered 2.5Gb/s data is 3.3ps. The sensitivity of the single channel DR is less than 20mV with 10-12 BER.

Data Recovery Based on Intelligent Pattern Matching

To solve the problem of data recovery on free disk sectors, an approach of data recovering based on intelligent pattern matching is proposed in this paper. Different from the methods based on the file directory, this approach utilizes the consistency among the data on the disk. A feature pattern library is established based on different types of fries according to the internal constructions of text. Data on sectors will be classified automatically by data clustering and evaluating. When the conflict happens on data classification, the digestion will be initiated by adopting context pattern. Based on this approach, the paper achieved the data recovery system aiming at pattern matching of txt, word and PDF fries. Raw and formatting recovery tests proved that the system works well.

A new approach for high fidelity seismic data recovery by fractal interpolation

Recovering accurate data is important for both earthquake and exploration seismology studies when data are sparsely sampled or partially missing. We present a method that allows for precise and accurate recovery of seismic data using a localized fractal recovery method. This method requires that the data are self- similar on local and global spatial scales. We present examples that show that the intrinsic structure associated with seismic data can be easily and accurately recovered by using this approach. This result, in turn, indicates that seismic data are indeed self-similar on local and global scales. This method is applicable not only for seismic studies, but also for any field studies that require accurate recovery of data from sparsely sampled datasets with partially missing data. Our ability to recover the missing data with high fidelity and accuracy will qualitatively improve the images of seismic tomography.

Harmonic Data Recovery Method Based on Multivariate Norm Matrix

During state perception of a power system, fragments of harmonic data are inevitably lost owing to the loss of synchronization signals, transmission delays, instrument failures, or other factors. A harmonic data recovery method is proposed based on multivariate norm matrix in this paper. The proposed method involves dynamic time warping for correlation analysis of harmonic data, normalized cuts for correlation clustering of power-quality monitoring devices, and adaptive alternating direction method of multipliers for multivariable norm joint optimization. Compared with existing data recovery methods, our proposed method maintains excellent recovery accuracy without requiring prior information or optimization of the power-quality monitoring device. Simulation results on the IEEE 39-bus and IEEE 118-bus test systems demonstrate the low computational complexity of the proposed method and its robustness against noise. In addition, the application of the proposed method to field data from a real-world system provides consistent results with those obtained from simulations.

A method for extracting the preseismic gravity anomalies over the Tibetan Plateau based on the maximum shear strain using GRACE data

The occurrence of earthquakes is closely related to the crustal geotectonic movement and the migration of mass,which consequently cause changes in gravity.The Gravity Recovery And Climate Experiment(GRACE)satellite data can be used to detect gravity changes associated with large earthquakes.However,previous GRACE satellite-based seismic gravity-change studies have focused more on coseismic gravity changes than on preseismic gravity changes.Moreover,the noise of the north–south stripe in GRACE data is difficult to eliminate,thereby resulting in the loss of some gravity information related to tectonic activities.To explore the preseismic gravity anomalies in a more refined way,we first propose a method of characterizing gravity variation based on the maximum shear strain of gravity,inspired by the concept of crustal strain.The offset index method is then adopted to describe the gravity anomalies,and the spatial and temporal characteristics of gravity anomalies before earthquakes are analyzed at the scales of the fault zone and plate,respectively.In this work,experiments are carried out on the Tibetan Plateau and its surrounding areas,and the following findings are obtained:First,from the observation scale of the fault zone,we detect the occurrence of large-area gravity anomalies near the epicenter,oftentimes about half a year before an earthquake,and these anomalies were distributed along the fault zone.Second,from the observation scale of the plate,we find that when an earthquake occurred on the Tibetan Plateau,a large number of gravity anomalies also occurred at the boundary of the Tibetan Plateau and the Indian Plate.Moreover,the aforementioned experiments confirm that the proposed method can successfully capture the preseismic gravity anomalies of large earthquakes with a magnitude of less than 8,which suggests a new idea for the application of gravity satellite data to earthquake research.

SA-RSR:a read-optimal data recovery strategy for XOR-coded distributed storage systems

To ensure the reliability and availability of data,redundancy strategies are always required for distributed storage systems.Erasure coding,one of the representative redundancy strategies,has the advantage of low storage overhead,which facilitates its employment in distributed storage systems.Among the various erasure coding schemes,XOR-based erasure codes are becoming popular due to their high computing speed.When a single-node failure occurs in such coding schemes,a process called data recovery takes place to retrieve the failed node’s lost data from surviving nodes.However,data transmission during the data recovery process usually requires a considerable amount of time.Current research has focused mainly on reducing the amount of data needed for data recovery to reduce the time required for data transmission,but it has encountered problems such as significant complexity and local optima.In this paper,we propose a random search recovery algorithm,named SA-RSR,to speed up single-node failure recovery of XOR-based erasure codes.SA-RSR uses a simulated annealing technique to search for an optimal recovery solution that reads and transmits a minimum amount of data.In addition,this search process can be done in polynomial time.We evaluate SA-RSR with a variety of XOR-based erasure codes in simulations and in a real storage system,Ceph.Experimental results in Ceph show that SA-RSR reduces the amount of data required for recovery by up to 30.0%and improves the performance of data recovery by up to 20.36%compared to the conventional recovery method.

A 10 Gb/s burst-mode clock and data recovery circuit

We introduce a gated oscillator based on XONR/XOR cells and illustrate its working process. A halfrate BM-CDR circuit based on the proposed oscillator is designed, and the design is implemented in SMIC 0.13 μm CMOS technology occupying an area of 675 ×25 μm2. The measured results show that this circuit can recover clock and data from each 10 Gbit/s burst-mode data packet within 5 bits, and the recovered data pass eye-mask test defined in IEEE standard 802.3av.

A Star-Style Trust Model with the Ability of Data Recovery for Trusted Computing Platform

Varieties of trusted computing products usually follow the mechanism of liner-style chain of trust according to the specifications of TCG.The distinct advantage is that the compatibility with the existing computing platform is preferable,while the shortcomings are obvious simultaneously.A new star-style trust model with the ability of data recovery is proposed in this paper.The model can enhance the hardware-based root of trust in platform measurement,reduce the loss of trust during transfer process,extend the border of trust flexibly,and have the ability of data backup and recovery.The security and reliability of system is much more improved.It is proved that the star-style trust model is much better than the liner-style trust model in trust transfer and boundary extending etc.using formal methods in this paper.We illuminate the design and implementation of a kind of trusted PDA acting on star-style trust model.

A 750 MHz semi-digital clock and data recovery circuit with 10^(-12) BER

A semi-digital clock and data recovery （CDR） is presented. In order to lower CDR trace jitter and decrease loop latency, an average-based phase detection algorithm is adopted and realized with a novel circuit. Implemented in a 0.13 μm standard 1PSM CMOS process, our CDR is integrated into a high speed serial and de-serial （SERDES） chip. Measurement results of the chip show that the CDR can trace the phase of the input data well and the RiMS jitter of the recovery clock in the observation pin is 122 ps at 75 MHz clock frequency, while the bit error rate of the recovery data is less than 10 × 10^-12.

Wide-range tracking technique for process-variation-robust clock and data recovery applications

A wide-range tracking technique for clock and data recovery(CDR) circuit is presented. Compared to the traditional technique, a digital CDR controller with calibration is adopted to extend the tracking range. Because of the use of digital circuits in the design, CDR is not sensitive to process and power supply variations. To verify the technique, the whole CDR circuit is implemented using 65-nm CMOS technology. Measurements show that the tracking range of CDR is greater than ±6×10-3 at 5 Gb/s. The receiver has good jitter tolerance performance and achieves a bit error rate of <10–12. The re-timed and re-multiplexed serial data has a root-mean-square jitter of 6.7 ps.

Garbage Collection and Data Recovery for N2DB

Non-Volatile Memory(NVM) offers byte-addressability and persistency. Because NVM can be plugged into memory and provide low latency, it offers a new opportunity to build new database systems with a single-layer storage design. A single-layer NVM-Native DataBase(N2 DB) provides zero copy and log freedom. Hence, all data are stored in NVM and there is no extra data duplication and logging during execution. N2 DB avoids complex data synchronization and logging overhead in the two-layer storage design of disk-oriented databases and in-memory databases. Garbage Collection(GC) is critical in such an NVM-based database because memory leaks on NVM are durable. Moreover, data recovery is equally essential to guarantee atomicity, consistency, isolation, and durability properties. Without logging, it is a great challenge for N2 DB to restore data to a consistent state after crashes and recoveries. This paper presents the GC and data recovery mechanisms for N2 DB. Evaluations show that the overall performance of N2 DB is up to 3:6 higher than that of InnoDB. Enabling GC reduces performance by up to 10%,but saves storage space by up to 67%. Moreover, our data recovery requires only 0:2% of the time and half of the storage space of InnoDB.

A Brief Discussion of Data Storage Principle and Data Recovery Technology

According to relevant data statistics, each year, nearly 70% of users have disc data loss because of misuse, viral damage, physical damage and hardware failure, bringing irreparable damage to enterprises, institutions and individuals. So data recovery technology has attracted wide attention of users and how to use data recovery technology to help to recover lost data and to minimize the loss has become an urgent need. This paper starts from the software and hardware failure to analyze two aspects of hard disk data loss, data storage structure theory, and combined with practical experience, it elaborates data damage type and related recovery methods.

Clastic compaction unit classification based on clay content and integrated compaction recovery using well and seismic data

Compaction correction is a key part of paleogeomorphic recovery methods. Yet, the influence of lithology on the porosity evolution is not usually taken into account. Present methods merely classify the lithologies as sandstone and mudstone to undertake separate porositydepth compaction modeling. However, using just two lithologies is an oversimplification that cannot represent the compaction history. In such schemes, the precision of the compaction recovery is inadequate. To improve the precision of compaction recovery, a depth compaction model has been proposed that involves both porosity and clay content. A clastic lithological compaction unit classification method, based on clay content, has been designed to identify lithological boundaries and establish sets of compaction units. Also, on the basis of the clastic compaction unit classification, two methods of compaction recovery that integrate well and seismic data are employed to extrapolate well-based compaction information outward along seismic lines and recover the paleo-topography of the clastic strata in the region. The examples presented here show that a better understanding of paleo-geomorphology can be gained by applying the proposed compaction recovery technology.

Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data resources.Generally speaking,EHRs are widely used in blockchain-based medical data platforms.EHRs are valuable private assets of patients,and the ownership belongs to patients.While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit doctors.In order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain.This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted EHRs.At the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical institutions.In addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records.Under the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the process.System analysis and security analysis illustrate the completeness and feasibility of the scheme.

A 28/56 Gb/s NRZ/PAM-4 dual-mode transceiver with 1/4 rate reconfigurable 4-tap FFE and half-rate slicer in a 28-nm CMOS

A 28/56 Gb/s NRZ/PAM-4 dual-mode transceiver(TRx)designed in a 28-nm complementary metal-oxide-semiconduc-tor(CMOS)process is presented in this article.A voltage-mode(VM)driver featuring a 4-tap reconfigurable feed-forward equal-izer(FFE)is employed in the quarter-rate transmitter(TX).The half-rate receiver(RX)incorporates a continuous-time linear equal-izer(CTLE),a 3-stage high-speed slicer with multi-clock-phase sampling,and a clock and data recovery(CDR).The experimen-tal results show that the TRx operates at a maximum speed of 56 Gb/s with chip-on board(COB)assembly.The 28 Gb/s NRZ eye diagram shows a far-end vertical eye opening of 210 mV with an output amplitude of 351 mV single-ended and the 56 Gb/s PAM-4 eye diagram exhibits far-end eye opening of 33 mV(upper-eye),31 mV(mid-eye),and 28 mV(lower-eye)with an output amplitude of 353 mV single-ended.The recovered 14 GHz clock from the RX exhibits random jitter(RJ)of 469 fs and deterministic jitter(DJ)of 8.76 ps.The 875 Mb/s de-multiplexed data features 593 ps horizontal eye opening with 32.02 ps RJ,at bit-error rate(BER)of 10-5(0.53 UI).The power dissipation of TX and RX are 125 and 181.4 mW,respectively,from a 0.9-V sup-ply.

Electrical Data Matrix Decomposition in Smart Grid

As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry high-speed and real time data,data losses and data quality degradation may happen constantly. For this problem,according to the strong spatial and temporal correlation of electricity data which isgenerated by human’s actions and feelings, we build a low-rank electricity data matrixwhere the row is time and the column is user. Inspired by matrix decomposition, we dividethe low-rank electricity data matrix into the multiply of two small matrices and use theknown data to approximate the low-rank electricity data matrix and recover the missedelectrical data. Based on the real electricity data, we analyze the low-rankness of theelectricity data matrix and perform the Matrix Decomposition-based method on the realdata. The experimental results verify the efficiency and efficiency of the proposed scheme.