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Cloud storage has been in widespread use nowadays, which alleviates users’ burden of local data storage. Meanwhile, how to ensure the security and integrity of the outsourced data stored in a cloud storage server has also attracted enormous attention from researchers. Proofs of storage (POS) is the main technique introduced to address this problem. Publicly verifiable POS allowing a third party to verify the data integrity on behalf of the data owner significantly improves the scalability of cloud service. However, most of existing publicly verifiable POS schemes are extremely slow to compute authentication tags for all data blocks due to many expensive group exponentiation operations, even much slower than typical network uploading speed, and thus it becomes the bottleneck of the setup phase of the POS scheme. In this article, we propose a new variant formulation called “Delegatable Proofs of Storage (DPOS)”. Then, we construct a lightweight privacy-preserving DPOS scheme, which on one side is as efficient as private POS schemes, and on the other side can support third party auditor and can switch auditors at any time, close to the functionalities of publicly verifiable POS schemes. Compared to traditional publicly verifiable POS schemes, we speed up the tag generation process by at least several hundred times, without sacrificing efficiency in any other aspect. In addition, we extend our scheme to support fully dynamic operations with high efficiency, reducing the computation of any data update to O(log n) and simultaneously only requiring constant communication costs. We prove that our scheme is sound and privacy preserving against auditor in the standard model. Experimental results verify the efficient performance of our scheme.

In the past few years, cloud computing develops very quickly. A large amount of data are uploaded and stored in remote public cloud servers which cannot fully be trusted by users. Especially, more and more enterprises would like to manage their data by the aid of the cloud servers. However, when the data outsourced in the cloud are sensitive, the challenges of security and privacy becomes urgent for wide deployment of the cloud systems. This paper proposes a secure data sharing scheme to ensure the privacy of data owner and the security of the outsourced cloud data. The proposed scheme provides flexible utility of data while solving the privacy and security challenges for data sharing. The security and efficiency analysis demonstrate that the designed scheme is feasible and efficient. At last, we discuss its application in E-health (electronic health) record.

In this paper we propose an algorithm for automatic detection of an infant cry. A particular application of this algorithm is the identification of a physical danger to babies, such as situations in which parents leave their children in vehicles. The proposed algorithm is based on two main stages. The first stage involves feature extraction, in which pitch related parameters, MFC (mel-frequency cepstrum) coefficients and short-time energy parameters are extracted from the signal. In the second stage, the signal is classified using the k-NN algorithm and is later verified as a cry signal, based on the pitch and harmonics information. In order to evaluate the performance of the algorithm in real world scenarios, we checked the robustness of the algorithm in the presence of several types of noise, and especially noises such as car horns and car engines that are likely to be present in vehicles. In addition, we addressed real time and low complexity demands during the development of the algorithm. In particular, we used a voice activity detector, which disabled the operation of the algorithm when voice activity was not present. A database of baby cry signals was used for performance evaluation. The results showed good performance of the proposed algorithm, even at low SNR.

In this paper, we propose a novel bilateral 2-D neighborhood preserving discriminant embedding for supervised linear dimensionality reduction for face recognition. It directly extracts discriminative face features from images based on graph embedding and Fisher's criterion. The proposed method is a manifold learning algorithm based on graph embedding criterion, which can effectively discover the underlying nonlinear face data structure. Both within-neighboring and between-neighboring information are taken into account to seek an optimal projection matrix by minimizing the intra-class scatter and maximizing the inter-class scatter based on Fisher's criterion. The performance of the proposed method is evaluated and compared with other face recognition schemes on the Yale, PICS, AR, and LFW databases. The experiment results demonstrate the effectiveness and superiority of the proposed method as compared with the state-of- the-art dimensionality reduction algorithms.

Red Palm Weevil (Rhynchophorus ferrugineus) is one of the most serious pests of coconut (Cocos nucifera L.) palms. It is known to attack 20 palm species worldwide. Due to the concealed nature of feeding RPW, infestation is detected during the last stages and farmers become aware of the problem only when the tree is about to die. The acoustic activity of RPW larvae (inside an offshoot and base of leaves) consists of chewing, crawling, emission and quick oscillating sounds. In this paper, acoustic techniques are used to detect hidden larvae infestations of coconut palm trees in the early stages that are recorded using wireless sensor network establishing ad-hoc network. The fundamental frequency of the acoustic activity generated by the RPW larvae also contains environmental noise which is captured by the wireless sensors (nodes) fixed to the palms and transmitted to the server through access points covering number of palms arranged in the form of hexagon for processing using MatLab tools. This method is inexpensive when compared to the existing methods for the detection of RPW larvae. The simulation results are encouraging in establishing the detection of larvae (grubs) inside the palm, thus enabling the farmer to takeup the control measures before the damage reaches the economic threshold.

In this paper we explore the performance of multilingual speaker recognition systems developed on the IITKGP-MLILSC speech corpus. Closed-set speaker identification and speaker verification experiments are individually conducted on 13 widely spoken Indian languages. In particular, we focus on the effect of language mismatch in the speaker recognition performance of individual languages and all languages together. The standard GMM-based speaker recognition framework is used. While the average language-independent speaker identification rate is as high as 95.21%, an average equal error rate of 11.71% shows scope for further improvement in speaker verification performance.

A sign language recognition system is an attempt to bring the speech and the hearing impaired community closer to more regular and convenient forms of communication. Thus, this system requires to recognize the gestures from a sign language and convert them to a form easily understood by the hearing. The model that has been proposed in this paper recognizes static images of the signed alphabets in the Indian Sign Language. Unlike the alphabets in other sign languages like the American Sign Language and the Chinese Sign language, the ISL alphabet are both single-handed and double-handed. Hence, to make recognition easier the model first categorizes them as single-handed or double-handed. For both categories two kinds of features, namely HOG and SIFT, are extracted for a set of training images and are combined in a single matrix. After which, HOG and SIFT features for the input test image are combined with the HOG and SIFT feature matrices of the training set. Correlation is computed for these matrices and is fed to a K-Nearest Neighbor Classifier to obtain the resultant classification of the test image.

In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a set of face images which were captured from different variations of pose, illumination, expression, resolution and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face datasets clearly show that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.

This paper proposes a general intelligent video surveillance monitoring system to explore and examine some problems in animal behavior analysis particularly in cow behaviors. In this concern, farmers, animal health professionals and researchers have well recognized that analysis of changes in the behavioral patterns of cattle is an important factor for an animal health and welfare management system. Also, in today dairy world, farm sizes are growing larger and larger, as a result the attention time limits for individual animals smaller and smaller. Thus, video based monitoring system will become an emerging technology approaching to an era of intelligent monitoring system. In this context, image processing is a promising technique for such challenging system because it is relatively low cost and simple enough to implement. One of important issues in the management of group-housed livestock is to make early detection of abnormal behaviors of a cow. Particularly failure in detecting estrus in timely and accurate manner can be a serious factor in achieving efficient reproductive performance. Another aspect is concerned with health management to identify unhealthy or poor health such as lameness through analysis of measured motion data. Lameness is a one of the biggest health and welfare issue in modern intensive dairy farming. Although there has been a tremendous amount of methods for detecting estrus, still it needs to improve for achieving a more accurate and practical. Thus in this paper, a general intelligent video surveillance system framework for animal behavior analysis is proposed to be by using (i) various types of Background Models for target or targets extraction, (ii) Markov and Hidden Markov models for detection of various types of behaviors among the targets, (iii) Dynamic Programming and Markov Decision Making Process for producing output results. As an illustration, a pilot experiment will be performed to confirm the feasibility and validity of the proposed framework.

Unmanned aerial vehicle (UAV) based computer vision system, as a more and more promising option for forest fires surveillance and detection, is now widely employed. In this paper, an image processing method for the application to UAV is presented for the automatic detection of forest fires in infrared (IR) images. The presented algorithm makes use of brightness and motion clues along with image processing techniques based on histogram-based segmentation and optical flow approach for fire pixels detection. First, the histogram-based segmentation is used to extract the hot objects as fire candidate regions. Then, the optical flow method is adopted to calculate motion vectors of the candidate regions. The motion vectors are also further analyzed to distinguish fires from other fire analogues. Through performing morphological operations and blob counter method, a fire can be finally tracked in each IR image. Experimental results verified that the designed method can effectively extract and track fire pixels in IR video sequences.

This paper presents research made for independent daily life assistance of elderly or persons with disabilities using IoT technologies. Our scope is to develop a system that allows living for as long as possible in familiar environment. This will be possible by wider spread of assistive technologies and the internet of things (IoT). We aim to bring together latest achievements in domain of Internet of things and assistive technologies in order to develop a complex assistive system with adaptive capability and learning behavior. We can use IoT technologies to monitor in real time the state of a patient or to get sensitive data in order to be subsequently analyzed for a medical diagnosis. We present the state of our work related to the development of an assistive assembly consisting of a smart and assistive environment, a human activity and health monitoring system, an assistive and telepresence robot, together with the related components and cloud services.

Cloud storage auditing schemes for shared data refer to checking the integrity of cloud data shared by a group of users. User revocation is commonly supported in such schemes, as users may be subject to group membership changes for various reasons. Previously, the computational overhead for user revocation in such schemes is linear with the total number of file blocks possessed by a revoked user. The overhead, however, may become a heavy burden because of the sheer amount of the shared cloud data. Thus, how to reduce the computational overhead caused by user revocations becomes a key research challenge for achieving practical cloud data auditing. In this paper, we propose a novel storage auditing scheme that achieves highly-efficient user revocation independent of the total number of file blocks possessed by the revoked user in the cloud. This is achieved by exploring a novel strategy for key generation and a new private key update technique. Using this strategy and the technique, we realize user revocation by just updating the nonrevoked group users’ private keys rather than authenticators of the revoked user. The integrity auditing of the revoked user’s data can still be correctly performed when the authenticators are not updated. Meanwhile, the proposed scheme is based on identity-base cryptography, which eliminates the complicated certificate management in traditional Public Key Infrastructure (PKI) systems. The security and efficiency of the proposed scheme are validated via both analysis and experimental results.