Partial Update Research Articles

Distributed Estimation Over an Adaptive Diffusion Network Based on the Family of Affine Projection Algorithms

This paper utilizes the family of affine projection algorithms (APAs) for distributed estimation in the adaptive diffusion networks. The diffusion APA (DAPA), the diffusion selective partial update (SPU) APA (DSPU-APA), the diffusion selective regressor (SR) APA (DSR-APA), and the diffusion dynamic selection (DS) APA (DDS-APA) are introduced in a unified way. In DSPU-APA, the weight coefficients are partially updated at each node during the adaptation. Therefore, the DSPU-APA has lower computational complexity in comparison to the DAPA. In addition, the convergence speed of the DSPU-APA is close to the DAPA. In DSR-APA, a subset of input regressors is optimally selected at each node during the adaptation. The dynamic selection of input regressors is performed in the DDS-APA. These strategies improve the performance of the conventional DAPA in terms of the steady-state error and computational complexity features. Also, by combining these algorithms, the DSPU-SR-APA and the DSPU-DS-APA are established, which are computationally efficient. The mean-square performance of the proposed algorithms is analyzed in the nonstationary environment and the generic relations for the theoretical learning curve and the steady-state error are derived. The analysis is based on the spatial-temporal energy conservation relation. The validity of the theoretical results and the good performance of the introduced algorithms are demonstrated by several computer simulations in diffusion networks.

A Study on Ajax in Web Applications to improve Usability of Online Systems

In olden days the web technology was using HTML (Hyper Text Markup Language) for creating web pages. The AJAX has changed the traditional paradigm of Web development by giving partial page update facility. Ajax is short-form of Asynchronous JavaScript and XML. It is a bundle of technologies that combined together to create new, dynamic, responsive and powerful web applications. Most of the giant internet-based companies such as Google, Yahoo, Microsoft and Amazon etc. are developing web applications based on Ajax. Even though major internet based companies working with Ajax, there is still ignorance about this technology among many developers. Many developers find it difficult to handle those bundle of technologies to build Ajax application.

A Fractional Variable Partial Update Least Mean Square Algorithm (FVPULMS) for communication channel estimation

The study presents a study on communication channel estimation algorithms. Fractional Variable Partial Update Least Mean Square algorithm (FVPULMS) is proposed. The model consists of input signal, unknown channel, an adaptive filter and an adaptation algorithm. The adaptive filter is a finite impulse response transversal adaptive filter. The adaptation algorithm is a FVPULMS algorithm and the filter update uses coefficient with index factors of three and five. The proposed algorithm was compared with Variable Partial Update Least Mean Square (VPULMS) and Full Update Least Mean Square (FULMS) algorithms. The simulation was carried out using fixed step sizes and variable step sizes of ( ). The result showed that FVPULMS algorithm has enhanced average performance efficiency in terms of estimated signal error reduction at the receiver station compared with VPULMS and FULMS algorithms, and can help to achieve improved signal error at the receiver station. KEYWORDS: Adaptation, Channel, step size, convergence speed, computational complexity, error state.

Modelling the energy consumption for over-the-air software updates in LPWAN networks: SigFox, LoRa and IEEE 802.15.4g

The recent trend towards the use of low-power wide-area-networks (LPWAN) communication technologies in the Internet of Things such as SigFox, Lora and Weightless gives rise to promising applications in smart grids, smart city, smart logistics, etc. where tens of thousands of sensors in a large area are connected to a single gateway. However, to manage such a sheer number of deployed devices, solutions to provide over-the-air firmware updates are required. This paper analyses the feasibility of over-the-air (partial) software updates for three LPWAN technologies (LoRa, SigFox and IEEE-802.15.4g) and discusses the best suited update method for different scenarios: full system updates, application updates and network stack updates.The results indicate that full firmware upgrades consume a substantial amount of energy, especially for the lowest bit-rate LPWAN technologies such as SigFox which drains a single AA battery with 2% when performing a version update. However, technologies with a similar range (i.e. LoRa SF12) require only 0.12%. The trade-off between range and energy (or bit-rate) becomes clear when considering that the least sensitive technology (IEEE-802.15.4g-OFDM) consumes only 0.0001%. Partial updates require significantly less energy for all technologies. Adding a single application uses 6 to 38 times less energy compared to a firmware update, depending on the update method and LPWAN technology. Even partial network stack updates (i.e. MAC) cost 3 to 8 times less energy, making over-the-air updates feasible.

Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking

In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers.

New efficient two channel forward set-membership partial-update NLMS algorithms for blind speech enhancement and acoustic noise reduction

This paper addresses the problem of speech enhancement by partial adaptive algorithms combined with the forward blind source separation (FBSS) structure. We propose three new two-channel forward partial update algorithms: (i) The first algorithm is based on the use of the correlation properties to adapt and control the variable step-size parameter of adaptive cross-filters of the FBSS structure, this algorithm is called the two channel correlated set membership partial update NLMS (TC-SM-PU-NLMScor). (ii) The second algorithm is based on the use of a new coefficients update relations to control the normalization energy of the adaptive NLMS cross filters of the FBSS structure. This algorithm is called the two channel set member ship partial update regularized NLMS (TC-SM-PU-RNLMS). (iii) The third algorithm is a combination of proposed 1 (TC-SM-PU-NLMScor) and proposed 2 (TC-SM-PU-RNLMS), this algorithm is called the two channel correlated set membership partial update regularized NLMS (TC-SM-PU-RNLMScor). In this paper, we show the obtained results of the comparison between the three proposed algorithms and the original two channel correlated set membership partial NLMS (TC-SM-PU-NLMS) algorithm. This comparison is expressed in terms of objective criteria as the System Mismatch (SM), the Segmental Mean Square Error (SegMSE) and the Segmental SNR (SegSNR).

Computationally Efficient Self-Tuning Controller for DC–DC Switch Mode Power Converters Based on Partial Update Kalman Filter

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a dc–dc switch mode power converter. The proposed estimation algorithm is based on a novel combination between the classical Kalman filter (KF) and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the KF, with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Banyasz/Keviczky proportional, integral, derivative (PID) controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a precalculated average model.

An updated checklist of the Tenebrionidae sec. Bousquet et al. 2018 of the Algodones Dunes of California, with comments on checklist data practices.

Generating regional checklists for insects is frequently based on combining data sources ranging from literature and expert assertions that merely imply the existence of an occurrence to aggregated, standard-compliant data of uniquely identified specimens. The increasing diversity of data sources also means that checklist authors are faced with new responsibilities, effectively acting as filterers to select and utilize an expert-validated subset of all available data. Authors are also faced with the technical obstacle to bring more occurrences into Darwin Core-based data aggregation, even if the corresponding specimens belong to external institutions. We illustrate these issues based on a partial update of the Kimsey et al. 2017 checklist of darkling beetles - Tenebrionidae sec. Bousquet et al. 2018 - inhabiting the Algodones Dunes of California. Our update entails 54 species-level concepts for this group and region, of which 31 concepts were found to be represented in three specimen-data aggregator portals, based on our interpretations of the aggregators' data. We reassess the distributions and biogeographic affinities of these species, focusing on taxa that are precinctive (highly geographically restricted) to the Lower Colorado River Valley in the context of recent dune formation from the Colorado River. Throughout, we apply taxonomic concept labels (taxonomic name according to source) to contextualize preferred name usages, but also show that the identification data of aggregated occurrences are very rarely well-contextualized or annotated. Doing so is a pre-requisite for publishing open, dynamic checklist versions that finely accredit incremental expert efforts spent to improve the quality of checklists and aggregated occurrence data.

An accurate estimation algorithm for big data streams

Sketch is a memory-efficient data structure, and is used to store and query the frequency of any item in a given multiset. As it can achieve fast query and update, it has been applied to various fields. Different sketches have different advantages and disadvantages. Sketches are originally proposed for estimation of flow size in network measurement. The key factor of sketches for network measurement is the insertion speed and accuracy. In this paper, we propose a new sketch, which can significantly improve the insertion speed while improving the accuracy. Our key methods include on-chip/off-chip separation and partial update algorithm. Extensive experimental results show that our sketch significantly outperforms the state-of-the-art both in terms of accuracy and speed.

Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.

Secure sustainable storage auditing protocol (SSSAP) with efficient key updates for cloud computing

The urgent requirement of the sustainable computing determines that many security assurance protocols are designed with low computational cost. The design of the sustainable cloud storage auditing protocol is an attractive research filed in the security community. As we all know, secret keys of the data storage auditing in the cloud system are very sensitive. To enhance the security of the cloud data storage auditing, some studies focused on the research of key updates. In this paper, we propose a secure sustainable storage auditing protocol that can support key updates for clients in cloud computing. In order to alleviate the high overhead of key updates at the local side, the partial key update tasks are outsourced to the TPA. Moreover, clients can verify the validity of the new updated keys by using the technology of the BLS signature. The security analysis shows that the proposed protocol can provide the security properties of the correctness, the verifiability and the accountability. Compared to previous studies, the proposed protocol is more efficient which is very suitable for the real-world sustainable cloud storage auditing.

Efficient attribute-based encryption with attribute revocation for assured data deletion

Cloud storage allows customers to store their data on remote cloud servers. With the advantage of reducing the burden of data management and storage, an increasing number of users prefer to store their data on the cloud. While secure data deletion is a crucial, it is a challenging issue in cloud storage. Logically deleted data may be easily exposed to un-authorized users in the cloud storage scenario thanks to its salient features such as multi-tenancy, virtualization and elasticity. Moreover, cloud servers might not delete customers’ data as instructed for hidden business interest. Hence, assured deletion is highly sought after. It helps preserve cloud users’ data privacy and is a necessary component of data retention regulations in cloud storage. In this paper, we first investigate the goals of assured data deletion and formalize its security model.Then, we propose a key-policy attribute-based encryption scheme for assured deletion (AD-KP-ABE) of cloud data. Our construction makes use of the attribute revocation cryptographic primitive and Merkle Hash Tree to achieve fine-grained access control and verifiable data deletion. The proposed AD-KP-ABE enjoys desirable properties such as no secret key update, partial ciphertext update and assured data deletion. The detailed security proof and implementation results demonstrate the security and practicality of our proposal.

Efficient Partial Condensing Algorithms for Nonlinear Model Predictive Control with Partial Sensitivity Update

Abstract In Nonlinear Model Predictive Control(NMPC), an optimal control problem (OCP) is solved repeatedly at every sampling instant. To satisfy the real-time restriction, modern methods tend to convert the OCP into structured Nonlinear Programming problems (NLP), which are approximately solved on-line. Real-Time Iteration is one of the promising NMPC algorithms that solves the NLP using a single Sequential Quadratic Programming (SQP) iteration. To solve Quadratic Programming (QP) problems efficiently, recently proposed partial condensing techniques reformulate large and sparse QP problems into smaller but still sparse ones. In this paper, we propose two tailored partial condensing algorithms by combining partial sensitivity updating schemes, that are recently proposed to update part of the sensitivities of dynamics. We show that such a combination can significantly reduce the computational time for partial condensing and for the full RTI step.

Online Product Quantization

Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behaviour of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.

Mimir: a term-distributed retrieval system for secret documents

In order to access sensitive documents shared over government, army and enterprise intranets, users rely on an indexing facility where they can quickly locate relevant documents they are allowed to access: 1) without leaking information about the remaining documents; 2) with a balanced load on the index servers. To address this problem, we propose Mimir, a distributed cipher retrieval system for sensitive documents. Mimir constructs the distributed indexes based on load balanced term distribution for better search efficiency and load balanced query. Mimir utilises encryption with random key, partial key update to protect sensitive data and improve query efficiency. Our experiments show that Mimir can effectively protect secret data and answer queries nearly as fast as an ordinary inverted index.

A Network Coding and DES Based Dynamic Encryption Scheme for Moving Target Defense

Unlike prior efforts in cybersecurity research, a dynamic defense theory, called moving target defense, increases the complexity and costs for attacks by effectively restricting the vulnerability exposure and the attack opportunities through various continually-changing evaluation, development mechanisms and strategy. Data encryption standard (DES) was the classical scheme of the traditional symmetric-key encryption schemes. Now it has been gradually replaced by the triple DES or advanced encryption standard (AES) so that the encoder has a larger key space. However, both the triple DES and AES cannot meet the dynamic security requirements of dynamic defense due to their static extension to the key space. In this paper, we propose a dynamic three-layer encryption scheme based on DES and network coding, with a low-complexity partial key update mechanism. Based on the theoretical analysis, the new scheme is shown to have the benefit to achieve a dynamic transition between efficiency and security, which increases its adaptability to various cyber conditions. The simulation results also show that the running ratio of the new scheme is relatively lower than or comparable to the triple DES.

Dynamic data source selection for humanistic culture integrated information retrieval

One of the core technologies of data source selection is to construct summary. When the data source content updates, it will inevitably lead to changes in the data source summary. The calculation work load to completely update the summary is huge, so a partial update strategy is used to improve the efficiency. In order to enhance the efficiency of tourism cultural information integration, we propose a new dynamic data source selection strategy. Firstly, we build a summary based on the relationship between people and characteristic words. Then, we design a local summary updating algorithm based on sampling technique. Finally, we propose a scoring method of data source base on the gain of cultural content of a single or synthetic character for integration purposes. We conduct a number of experiments based on field data collection and the results showed that compared with the existing update algorithms, our method has higher accuracy.

Dynamic data source selection for humanistic culture integrated information retrieval

One of the core technologies of data source selection is to construct summary. When the data source content updates, it will inevitably lead to changes in the data source summary. The calculation work load to completely update the summary is huge, so a partial update strategy is used to improve the efficiency. In order to enhance the efficiency of tourism cultural information integration, we propose a new dynamic data source selection strategy. Firstly, we build a summary based on the relationship between people and characteristic words. Then, we design a local summary updating algorithm based on sampling technique. Finally, we propose a scoring method of data source base on the gain of cultural content of a single or synthetic character for integration purposes. We conduct a number of experiments based on field data collection and the results showed that compared with the existing update algorithms, our method has higher accuracy.

Improving Write Performance and Extending Endurance of Object-Based NAND Flash Devices

Write amplification is a major cause of performance and endurance degradations in NAND flash-based storage systems. In an object-based NAND flash device (ONFD), two causes of write amplification are onode partial update and cascading update. Here, onode is a type of small-sized object metadata, and multiple onodes are stored in one NAND flash page. Updating one onode invokes partial page update (i.e., onode partial update), incurring unnecessary migration of the un-updated data. Cascading update denotes updating object metadata in a cascading manner due to object data update or migration. Although there are only several bytes that need to be updated in the object metadata, one or more pages have to be re-written accordingly. In this work, we propose a system design to alleviate the write amplification issue in the object-based NAND flash device. The proposed design includes (1) a multi-level garbage collection technique to minimize unnecessary data migration incurred by onode partial update and (2) a B+ table tree, Semantics-Aware Flexible (SAF) data layout, and selective cache design to reduce the write operations associated with cascading update. To guarantee system consistency, we also propose a power failure handling technique. Experiment results show that our proposed design can achieve up to 20% write reduction compared to the best states of the art.

Improving adaptive frameless rendering

By eliminating the rigid sampling patterns of framed renderers and adapting sampling and reconstruction to spatio-temporal changes, combined with sample reprojection, adaptive frameless rendering offers significant potential to generate coherent imagery from sparse set of samples in real-time rendering systems. It capitalizes on the unique feature of ray-based rendering methods the ability to select where to render along with when to render, which is missing in traditional double buffered renderings. With its unique contribution of adaptive partial update of display it has applications in rendering, display, and imaging systems. In this work, we propose alternate data structures and modified algorithms to reduce memory consumption by more than 90%, reduce computation overheads by approximately 50%, and improve sampler response to regions of change by modifying sampling pattern and tracking temporal changes in 3D space, improving the quality of rendering.