Search

Searchable symmetric encryption enables a cloud server to answer queries directly over encrypted data. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading false positives and requiring the local search at the client side. In this work, we propose a novel approach to meet these requirements while minimizing the client's work and communication cost. First, a relaxed notion of ciphertext indistinguishability on partitioned data is formalized, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the server to perform search of relevant data and filter false positives. We present a construction for achieving these goals through a two-phase search algorithm. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. The experiment results on large real-world data-sets show that our approach outperforms the state-of-the-art. This work focuses on the class of equality conjunction search, but it applies to the general class of Boolean queries of equalities because the latter can be reduced to several equality conjunction queries.

Somatotopy is an important guiding principle for sensory fiber organization in the primary somatosensory cortex (S1), which reflects tactile information processing and is associated with disease-related reorganization. However, it is difficult to measure the neuronal encoding scheme in S1 in vivo in normal participants. Here, we investigated the somatotopic map of the undominant hand using a Bayesian population receptive field (pRF) model. The model was established in hand space with between- and within-digit dimensions. In the between-digit dimension, orderly representation was found, which had low variability across participants. The pRF shape tended to be elliptical for digits with high spatial acuity, for which the long axis was along the within-digit dimension. In addition, the pRF width showed different change trends in the 2 dimensions across digits. These results provide new insights into the neural mechanisms in S1, allowing for in-depth investigation of somatosensory information processing and disease-related reorganization.

Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, making it challenging to be compatible with continuous optimization pipelines in machine learning. In this paper, we design \underline{D}eep \underline{N}eural \underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions. We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design. We further integrate the game theoretical conditions within the model design, to guarantee the stability of the auctions. DNAs have been successfully deployed in the e-commerce advertising system at Taobao. Experimental evaluation results on both large-scale data set as well as online A/B test demonstrated that DNAs significantly outperformed other mechanisms widely adopted in industry., Comment: To appear in the Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms only focus on optimizing a single performance metric, e.g., either social welfare or revenue, and are not suitable for e-commerce advertising with various, dynamic, difficult to estimate, and even conflicting performance metrics. In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework. These new rank score functions are implemented via deep neural network models under the constraints of monotone allocation and smooth transition. The requirement of monotone allocation ensures Deep GSP auction nice game theoretical properties, while the requirement of smooth transition guarantees the advertiser utilities would not fluctuate too much when the auction mechanism switches among candidate mechanisms to achieve different optimization objectives. We deployed the proposed mechanisms in a leading e-commerce ad platform and conducted comprehensive experimental evaluations with both offline simulations and online A/B tests. The results demonstrated the effectiveness of the Deep GSP auction compared to the state-of-the-art auction mechanisms., To appear in the Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM), 2021

Humans exhibit a remarkable ability to discriminate variations in object volume based on natural haptic perception. The discrimination thresholds for the haptic volume perception of the whole hand are well known, but the discrimination thresholds for haptic volume perception of fingers and phalanges are still unknown. In the present study, two psychophysical experiments were performed to investigate haptic volume perception in various fingers and phalanges. The configurations of both experiments were completely dependent on haptic volume perception from the fingers and phalanges. The participants were asked to blindly discriminate the volume variation of regular solid objects in a random order by using the distal phalanx, medial phalanx, and proximal phalanx of their index finger, middle finger, ring finger, and little finger. The discrimination threshold of haptic volume perception gradually decreases from the little finger to the index finger as well as from the proximal phalanx to the distal phalanx. Overall, both the shape of the target and the part of the finger in contact with the target significantly influence the precision of haptic perception of volume. This substantial data set provides detailed and compelling perspectives on the haptic system, including for discrimination of the spatial size of objects and for performing more general perceptual processes.

Lecture material of a sophomore large-enrollment (N=271) system programming 15-week class was delivered solely online using a new video-based web platform. The platform provided accurate accessible transcriptions and captioning plus a custom text-searchable interface to rapidly find relevant video moments from the entire course. The system logged student searching and viewing behaviors as fine-grained web browser interaction events including full-screen-switching, loss-of-focus, incremental searching events, and continued-video-watching events with the latter at 15-second granularity. Student learning behaviors and findings from three research questions are presented using individual-level performance and interaction data. Firstly, we report on learning outcomes from alternative learning paths that arise from the course's application of Universal Design for Learning principles. Secondly, final exam performance was equal or better to prior semesters that utilized traditional in-person live lectures. Thirdly, learning outcomes of low and high performing students were analyzed independently by grouping students into four quartiles based on their non-final-exam course performance of programming assignments and quizzes. We introduce and justify an empirically-defined qualification threshold for sufficient video minutes viewed for each group. In all quartiles, students who watched an above-threshold of video minutes improved their in-group final exam performance (ranging from +6 % to +14%) with the largest gain for the lowest-performing quartile. The improvement was similar in magnitude for all groups when expressed as a fraction of unrewarded final exam points. Overall, the study presents and evaluates how learner use of online video using ClassTranscribe predicts course performance and positive learning outcomes.

As data outsourcing becomes popular, oblivious algorithms have raised extensive attentions. Their control flow and data access pattern appear to be independent of the input data they compute on. Oblivious algorithms, therefore, are especially suitable for secure processing in outsourced environments. In this work, we focus on oblivious shuffling algorithms that aim to shuffle encrypted data blocks outsourced to a cloud server without disclosing the actual permutation of blocks to the server. Existing oblivious shuffling algorithms suffer from issues of heavy communication cost and client computation cost for shuffling large-sized blocks because all outsourced blocks must be downloaded to the client for shuffling or peeling off extra encryption layers. To help eliminate this void, we introduce the "repeatable oblivious shuffling" notation that avoids moving blocks to the client and thus restricts the communication and client computation costs to be independent of the block size. For the first time, we present a concrete construction of repeatable oblivious shuffling using additively homomorphic encryption. The comprehensive evaluation of our construction shows its effective usability in practice for shuffling large-sized blocks.

We consider the following secure data retrieval problem: a client outsources encrypted data blocks to a semi-trusted cloud server and later retrieves blocks without disclosing access patterns. Existing PIR and ORAM solutions suffer from serious performance bottlenecks in terms of communication or computation costs. To help eliminate this void, we introduce "access pattern unlinkability'' that separates access pattern privacy into short-term privacy at individual query level and long-term privacy at query distribution level. This new security definition provides tunable trade-offs between privacy and query performance. We present an efficient construction, called SBR protocol, using PIR and Oblivious Shuffling to enable secure data retrieval while satisfying access pattern unlinkability. Both analytical and empirical analysis show that SBR exhibits flexibility and usability in practice.

A model reference sliding mode control(MRSMC) is proposed for the three-phase grid-connected inverter. Firstly, the phase voltage and current of the grid are set as the objective function. The corresponding state equations can be derived for the grid voltage and current. Then, a feedforward control scheme is designed to achieve current decouple. Moreover, the Lyapunov stability theorem is used to verify the stability of the system with the decoupled control law. The effectiveness of the proposed method is verified by the simulated results.

Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

Heart rate (HR) estimation using photoplethysmography (PPG) has drawn increasing attention in the field of wearable technology due to its advantages with higher degree of usability and lower cost than Electrocardiograph. It has been widely used in wearable devices, such as smart-watches for fitness tracking and vital sign monitoring. However, motion artifact is a strong interference, preventing accurate estimation of HR. Signal decomposition and adaptive filtering are two popular approaches for motion artifact removal, but each of them has inherent drawbacks. In this paper, a hybrid motion artifact removal method is proposed, which combines nonlinear adaptive filtering and signal decomposition, getting the best of both approaches. The method was evaluated on the PPG database used in the 2015 IEEE Signal Processing Cup. The experimental results showed that the method achieved the average absolute error of 1.16 beat per minutes (BPM) on the 12 training data sets, and 2.98 BPM on the ten testing data sets.

It has been established that, ratings are missing not at random in recommender systems. However, little research has been done to reveal how the ratings are missing. In this paper we present one possible explanation of the missing not at random phenomenon. We verify that, using a variety of different real-life datasets, there is a spiral process for a silent minority in recommender systems where (1) people whose opinions fall into the minority are less likely to give ratings than majority opinion holders; (2) as the majority opinion becomes more dominant, the rating possibility of a majority opinion holder is intensifying but the rating possibility of a minority opinion holder is shrinking; (3) only hardcore users remain to rate for minority opinions when the spiral achieves its steady state. Our empirical findings are beneficial for future recommendation models. To demonstrate the impact of our empirical findings, we present a probabilistic model that mimics the generation process of spiral of silence. We experimentally show that, the presented model offers more accurate recommendations, compared with state-of-the-art recommendation models.

Searchable symmetric encryption (SSE) enables a remote cloud server to answer queries directly over encrypted data on a client’s behalf, therefore, relieves the resource limited client from complicated data management tasks. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading many false positives or requiring the client to search relevant bucket ids locally, which limits the applicability of the method. In this paper, we propose a novel approach CLASS to meet these requirements for equality conjunction search while minimizing the client work and communication cost. First, we generalize the standard ciphertext indistinguishability to partitioned data, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the cloud server to perform search of relevant data and filtering of false positives. We present a construction achieving these goals through a two-phase search algorithm for a query. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. Both phases are performed by the server and are implemented by plugging in existing search methods. The experiment results on large real-world data sets show that our approach outperforms the state-of-the-art.

Secure top-k inner product retrieval allows the users to outsource encrypted data vectors to a cloud server and at some later time find the k vectors producing largest inner products giving an encrypted query vector. Existing solutions suffer poor performance raised by the client's filtering out top-k results. To enable the server-side filtering, we introduce an asymmetric inner product encryption AIPE that allows the server to compute inner products from encrypted data and query vectors. To solve AIPE's vulnerability under known plaintext attack, we present a packing approach IP Packing that allows the server to obtain the entire set of inner products between the query and all data vectors but prevents the server from associating any data vector with its inner product. Based on IP Packing, we present our solution SKIP to secure top-k inner product retrieval that further speeds up retrieval process using sequential scan. Experiments on real recommendation datasets demonstrate that our protocols outperform alternatives by several orders of magnitude.

Motion artifacts (MAs) are strong interference sources in wearable photoplethysmography (PPG) signals, significantly affecting estimation of heart rate (HR) and other physiological parameters. In this paper, a novel method called SPECTRAP is proposed for accurate motion-tolerant estimation of HR using a PPG signal and a simultaneous acceleration signal. The method first calculates the spectra of the PPG signal and the acceleration signal, and then removes the MA spectral components from the PPG spectrum using a new spectrum subtraction algorithm. The new spectrum subtraction algorithm is based on asymmetric least square and overcomes drawbacks of the conventional spectrum subtraction algorithms. To find the spectral peak corresponding to HR on the resulting spectrum, SPECTRAP formulates the problem into a pattern classification problem and uses the Bayesian decision theory to solve it. Experimental results on the PPG database used in 2015 IEEE Signal Processing Cup showed that the proposed algorithm has excellent performance. The average absolute error on the twelve training sets was 1.50 beat per minute (BPM) (standard deviation: 1.95 BPM). The average absolute error on the ten testing sets was 2.13 BPM (standard deviation: 2.77 BPM).

With the development of social network, users tend to get latest news from online social media, which demands an effective and efficient representation for social-media texts to filter from growing online short texts timely. However, VSM(Vector Space Model)-based representation for short texts is considerably sparse, which significantly decreases the effectiveness and efficiency to discover new social news. What’s more, the traditional dimension reduction methods target to choose key words according to their appearing frequency, which make it unsuitable for short social-media texts because of its sparsity of key words. In consideration of the word semantic relations, this paper introduces a non-backtracking word clustering and counting mechanism to utilize key word clusters instead of key words to represent short social-media texts and thus reduce the dimensions of the representation model. In addition, we implement this representation mechanism on a real-time computation platform, Storm, which enhances the representation efficiency of social-media texts. Experiments based on real-world social texts demonstrated that our mechanism can effectively reduce the dimension of the representation model and improve the efficiency of the latest social news discovery.

Cloud computing has emerged as a compelling vision for managing data and delivering query answering capability over the internet. This new way of computing also poses a real risk of disclosing confidential information to the cloud. Searchable encryption addresses this issue by allowing the cloud to compute the answer to a query based on the cipher texts of data and queries. Thanks to its inner product preservation property, the asymmetric scalar-product-preserving encryption (ASPE) has been adopted and enhanced in a growing number of works toperform a variety of queries and tasks in the cloud computingsetting. However, the security property of ASPE and its enhancedschemes has not been studied carefully. In this paper, we show acomplete disclosure of ASPE and several previously unknownsecurity risks of its enhanced schemes. Meanwhile, efficientalgorithms are proposed to learn the plaintext of data and queriesencrypted by these schemes with little or no knowledge beyondthe ciphertexts. We demonstrate these risks on real data sets.

Deep learning is increasingly popular, partly due to its widespread application potential, such as in civilian, government and military domains. Given the exacting computational requirements, cloud computing has been utilized to host user data and model. However, such an approach has potential privacy implications. Therefore, in this paper, we propose a method to protect user's privacyin the inference phase of deep learning workflow. Specifically, we use an intermediate layer to separate the entire neural network into two parts, which are respectively deployed on the user device and the cloud server. The encoder, deployed on the user device, is usedfor raw data transformation, which removes the need for users to upload raw data to the cloud directly. However, we also demonstrate there exists potential for privacy leakage in the intermediate features of the neural network through two concrete experiments. In other words, the encoder on its own does not adequately achieve privacy protection. By imposing privacy constraints during the feature extraction, we can reduce thecontribution of the extracted features to the privacy leakage. In this way, privacy protection capability of the encoder can be further strengthened. We then demonstrate the effectiveness of our method using a large number of experiments.

We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizing signals' block structure. We propose two families of algorithms based on the framework of block sparse Bayesian learning (BSBL). One family, directly derived from the BSBL framework, requires knowledge of the block structure. Another family, derived from an expanded BSBL framework, is based on a weaker assumption on the block structure, and can be used when the block structure is completely unknown. Using these algorithms we show that exploiting intra-block correlation is very helpful in improving recovery performance. These algorithms also shed light on how to modify existing algorithms or design new ones to exploit such correlation and improve performance., Matlab codes can be downloaded at: https://sites.google.com/site/researchbyzhang/bsbl, or http://dsp.ucsd.edu/~zhilin/BSBL.html

Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function, and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model based algorithms. Experimental results on the Extended Yale B, the AR and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases., Accepted by Mathematical Problems in Engineering in 2013

Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is non-sparse in the time domain and also non-sparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, Block Sparse Bayesian Learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the telemonitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for telemonitoring of EEG and other non-sparse physiological signals., Comment: Matlab codes can be downloaded at: http://dsp.ucsd.edu/~zhilin/BSBL.html, or http://sites.google.com/site/researchbyzhang/bsbl

Compressed sensing (CS) has been used as an efficient, low-energy data compressor for the telemonitoring of physiological signals such as ECG or EEG. However, the performance of these applications is often assessed by the quality of signal recovery such as Percentage Root-mean square Difference (PRD). In this paper, (1) we show that the metric of signal recovery quality such as PRD is not adequate for CS based compressor, especially when failing to regard the sampling rate of raw signals. (2) we analyze the distortion of CS on EEG telemonitoring for the epileptic, where the performance is evaluated using different sampling rates and the quality is assessed by epileptic seizure classification accuracy. We aim to establish a task-oriented protocol for correctly applying CS as a data compressor on EEG and other physiological signals.

Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human-machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.

Group $k$ k -nearest neighbor ( $k$ k GNN) search allows a group of $n$ n mobile users to jointly retrieve $k$ k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving $k$ k GNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving $k$ k GNN search in the full user collusion environment, where any $n-1$ n - 1 users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider $k$ k GNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., $k$ k GNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.

Fetal electrocardiogram (FECG) extraction is of vital importance in biomedical signal processing. A promising approach is blind source extraction (BSE) emerging from the neural network fields, which is generally implemented in a semi-blind way. In this paper, we propose a robust extraction algorithm that can extract the clear FECG as the first extracted signal. The algorithm exploits the fact that the FECG signal's kurtosis value lies in a specific range, while the kurtosis values of other unwanted signals do not belong to this range. Moreover, the algorithm is very robust to outliers and its robustness is theoretically analyzed and is confirmed by simulation. In addition, the algorithm can work well in some adverse situations when the kurtosis values of some source signals are very close to each other. The above reasons mean that the algorithm is an appealing method which obtains an accurate and reliable FECG.

Recently the constrained ICA (cICA) algorithm has been widely applied to many applications. But a crucial problem to the algorithm is how to design a reference signal in advance, which should be closely related to the desired source signal. If the desired source signal is very weak in mixed signals and there is no enough a priori information about it, the reference signal is difficult to design. With some detailed discussions on the cICA algorithm, the paper proposes a second-order statistics based approach to reliably find suitable reference signals for weak temporally correlated source signals. Simulations on synthetic data and real-world data have shown its validity and usefulness.

In many applications extraction of source signals of interest from observed signals is a more feasible approach than simultaneous separation of all the source signals, since the latter often costs lots of computing time and often is not necessary. If the desired source signals have some specific properties, then we can exploit these properties to design effective source extraction algorithms. This letter proposes an algorithm, which extracts the desired signal with a priori knowledge about its statistics. That is to say, if we know the range in which the kurtosis value of the desired signal lies, we can use this algorithm to extract it. The validity and performance of the proposed approach are confirmed through computer simulations and experiments on real-world ECG data.

This letter addresses the problem of fetal electrocardiogram (FECG) extraction. A class of source extraction algorithms is proposed and it uses a combination of two different approaches: Barros's source extraction approach and Hyvarinen's fixed-point approach. The proposed algorithms can rapidly extract the desired FECG with little noise, and can work well in some poor situations. The validity and performance of the algorithms are confirmed by extensive computer simulations and experiments on real-world data.

There is a trend to develop blind or semi-blind source extraction algorithms based on second-order statistics, due to its low computation load and fast processing speed. An important and primary work is done by Barros and Cichocki, who propose an extraction algorithm based on a time delay. The algorithm is simple and fast, but its performance is not satisfying. The paper extends their work and proposes a robust algorithm based on eigenvalue decomposition of several delayed covariance matrices. It is faster and has better performance, which is confirmed by theoretical analysis and computer simulations.

Goal: A new method for heart rate monitoring using photoplethysmography (PPG) during physical activities is proposed. Methods: It jointly estimates spectra of PPG signals and simultaneous acceleration signals, utilizing the multiple measurement vector model in sparse signal recovery. Due to a common sparsity constraint on spectral coefficients, the method can easily identify and remove spectral peaks of motion artifact (MA) in PPG spectra. Thus, it does not need any extra signal processing modular to remove MA as in some other algorithms. Furthermore, seeking spectral peaks associated with heart rate is simplified. Results: Experimental results on 12 PPG datasets sampled at 25 Hz and recorded during subjects' fast running showed that it had high performance. The average absolute estimation error was 1.28 beat per minute and the standard deviation was 2.61 beat per minute. Conclusion and Significance: These results show that the method has great potential to be used for PPG-based heart rate monitoring in wearable devices for fitness tracking and health monitoring., Comment: Published in IEEE Transactions on Biomedical Engineering, Vol. 62, No. 8, PP. 1902-1910, August 2015

Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease (AD). Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.

Geospatial clustering is an important topic in spatial analysis and knowledge discovery research. However, most existing clustering methods clusters geospatial data at data level without considering domain knowledge and users’ goals during the clustering process. In this paper, we propose an ontology-based geospatial cluster ensemble approach to produce good clustering results with the consideration of domain knowledge and users’ goals. The approach includes two components: an ontology-based expert system and a cluster ensemble method. The ontology-based expert system is to represent geospatial and clustering domain knowledge and to identify the appropriate clustering components (e.g., geospatial datasets, attributes of the datasets, and clustering methods) based on a specific application requirement. The cluster ensemble is to combine a diverse set of clustering results produced by recommended clustering components into an optimal clustering result. A real case study has been conducted to demonstrate the efficiency and practicality of the approach.

Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels. This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals., Comment: Codes are available at: https://sites.google.com/site/researchbyzhang/stsbl

As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study proposes and evaluates a spatio-temporal sparse Bayesian learning algorithm, which has the desired ability to recover such non-sparse biosignals. It exploits both temporal correlation in each individual biosignal and inter-channel correlation among biosignals from different channels. The proposed algorithm was used for compressed sensing of multichannel electroencephalographic (EEG) signals for estimating vehicle drivers' drowsiness. Results showed that the drowsiness estimation was almost unaffected even if raw EEG signals (containing various artifacts) were compressed by 90%., Invited paper for 2013 Asilomar Conference on Signals, Systems & Computers (Asilomar 2013)

Telehealth and wearable equipment can deliver personal healthcare and necessary treatment remotely. One major challenge is transmitting large amount of biosignals through wireless networks. The limited battery life calls for low-power data compressors. Compressive Sensing (CS) has proved to be a low-power compressor. In this study, we apply CS on the compression of multichannel biosignals. We firstly develop an efficient CS algorithm from the Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination of the block sparse model and multiple measurement vector model. Experiments on real-life Fetal ECGs showed that the proposed algorithm has high fidelity and efficiency. Implemented in hardware, the proposed algorithm was compared to a Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one has low power consumption and occupies less computational resources., 2013 International Workshop on Biomedical and Health Informatics

University physics is an important public basic course for science and engineering universities. It has the irreplaceable role compare with other courses in improving students’ scientific literacy and ability to analyze and solve problems, coordination theory knowledge and improving students' practical ability. Previous teaching had “attention to theory, despise application”, “attention to model, despise deduction” misunderstanding. At the same time college entrance examination curriculum makes diversified physical knowledge system for education groups. This paper explores a new effective teaching mode. First analysis of the University Physics Teaching, then discussed the diversified teaching mode based on the university physics teaching objectives.

Video course is an important part of resource sharing course construction for college. Firstly, this paper analyzed some shortage such as lack of professional filming environment, monotony of the video screen, lack of design of teaching, lack of post-production. Secondly, some improvements were proposed, such as equipment with professional recording and broadcasting classroom, welldesigned teaching process, demonstration experiment extensive video teaching, focus on post-production, integration courses feedback seriously. Thus improve the quality of video courses and promote the construction of resource sharing courses.

In this paper we study the recovery of block sparse signals and extend conventional approaches in two important directions; one is learning and exploiting intra-block correlation, and the other is generalizing signals' block structure such that the block partition is not needed to be known for recovery. We propose two algorithms based on the framework of block sparse Bayesian learning (bSBL). One algorithm, directly derived from the framework, requires a priori knowledge of the block partition. Another algorithm, derived from an expanded bSBL framework using the generalization method, can be used when the block partition is unknown. Experiments show that they have superior performance to state-of-the-art algorithms.

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as non-sparsity and strong noise contamination, current CS algorithms generally fail in this application. This work proposes to use the block sparse Bayesian learning (BSBL) framework to compress/reconstruct non-sparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage., Comment: The code and the data can be downloaded from the first author's homepage: http://sites.google.com/site/researchbyzhang/bsbl, or http://dsp.ucsd.edu/~zhilin/BSBL.html

This paper presents a general model for analyzing the characteristics of routing control traffic and the influences of changing network parameters on these characteristics in mobile ad-hoc networks that use on-demand routing protocols. It provides an analytical framework for studying the features of control traffic due to mobility, node density and data traffic etc. Using this model, we found that: 1) there is an "stabilization" process in the spread of control traffic in MANETs, 2) clusters exist in mobile ad-hoc network, 3) there are backbone nodes, serving as a gateway to relay RREQ packets between different clusters. The analysis presented in this paper can be used for designing novel and efficient heuristic routing protocols for mobile ad-hoc networks.

Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal correlation among source vectors and thus their performance degrades significantly in the presence of correlation. In this work we propose an iterative reweighted sparse Bayesian learning (SBL) algorithm exploiting the temporal correlation, and motivated by it, we propose a strategy to improve existing reweighted $\ell_2$ algorithms for the MMV problem, i.e. replacing their row norms with Mahalanobis distance measure. Simulations show that the proposed reweighted SBL algorithm has superior performance, and the proposed improvement strategy is effective for existing reweighted $\ell_2$ algorithms., Accepted by ICASSP 2011

We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. In this work, we propose a block sparse Bayesian learning framework which models the temporal correlations. In this framework we derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlations. Furthermore, our algorithms are better at handling highly underdetermined problems and require less row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model., The final version with some typos corrected. Codes can be downloaded at: http://dsp.ucsd.edu/~zhilin/TSBL_code.zip

Process cooperation is a key technique of enterprises process enactment. During enterprises process enactment, how to analyze and process various information effectively? It is very important to implement process-driven cooperative enactment with high efficiency. The paper introduced an approach to design enterprises process cooperative model which focused on the perspective of the monitoring model to support process cooperative. We extended the cooperative model in the Process-cored Integrated Enterprise Model by adding process monitoring model. Some concepts and the key technologies are seriously discussed such as the mapping from models to application systems, monitoring objects and monitoring events. And a prototype system has proposed to validate the idea we proposed in this paper.

Sparse signal recovery algorithms utilizing multiple measurement vectors (MMVs) are known to have better performance compared to the single measurement vector case. However, current work rarely consider the case when sources have temporal correlation, a likely situation in practice. In this work we examine methods to account for temporal correlation and its impact on performance. We model sources as AR processes, and then incorporate such information into the framework of sparse Bayesian learning for sparse signal recovery. Experiments demonstrate the superiority of the proposed algorithms. They also show that the performance of existing algorithms are limited by temporal correlation, and that if such correlation can be fully exploited, as in our proposed algorithms, the limitation can be overcome.

Blind source extraction (BSE) is of advantages over blind source separation (BSS) when obtaining some underlying source signals from high dimensional observed signals. Among a variety of BSE algorithms, a large number of algorithms are based on linear prediction (LP-BSE). In this paper we analyze them from practical point of view. We reveal that they are, in nature, minor component analysis (MCA) algorithms, and thus they have some problems that are inherent in MCA algorithms. We also find a switch phenomenon of online LP-BSE algorithms, showing that different parts of a single extracted signal are the counterparts of different source signals. The two issues should be noticed when one applies these algorithms to practical applications. Computer simulations are given to confirm these observations.

Independent component analysis (ICA), blind source separation (BSS) and related methods like blind source extraction (BSE) are all the promising unsupervised neural network technique for analysis of biomedical signals, especially for ECG, EEG and fMRI data. However, most of source extraction algorithms based on ICA neural network are not suitable to extract the desired signal since these algorithms are not to obtain the desired signal as the first output signal. In this paper, we propose an algorithm based on ICA neural network that can extract a desired source signal as the first output signal with a given kurtosis range. Because of adopting a robust objective function, the algorithm becomes very robust to outliers and spiky noise. Simulations on artificially generated data and real-world ECG data have shown that the algorithm can achieve satisfying results.