A bayesian scene-prior-based deep network model for face verification

Face recognition/verification has received great attention in both theory and application for the past two decades. Deep learning has been considered as a very powerful tool for improving the performance of face recognition/verification recently. With large labeled training datasets, the features obtained from deep learning networks can achieve higher accuracy in comparison with shallow networks. However, many reported face recognition/verification approaches rely heavily on the large size and complete representative of the training set, and most of them tend to suffer serious performance drop or even fail to work if fewer training samples per person are available. Hence, the small number of training samples may cause the deep features to vary greatly. We aim to solve this critical problem in this paper. Inspired by recent research in scene domain transfer, for a given face image, a new series of possible scenarios about this face can be deduced from the scene semantics extracted from other face individuals in a face dataset. We believe that the “scene” or background in an image, that is, samples with more different scenes for a given person, may determine the intrinsic features among the faces of the same individual. In order to validate this belief, we propose a Bayesian scene-prior-based deep learning model in this paper with the aim to extract important features from background scenes. By learning a scene model on the basis of a labeled face dataset via the Bayesian idea, the proposed method transforms a face image into new face images by referring to the given face with the learnt scene dictionary. Because the new derived faces may have similar scenes to the input face, the face-verification performance can be improved without having background variance, while the number of training samples is significantly reduced. Experiments conducted on the Labeled Faces in the Wild (LFW) dataset view #2 subset illustrated that this model can increase the verification accuracy to 99.2% by means of scenes’ transfer learning (99.12% in literature with an unsupervised protocol). Meanwhile, our model can achieve 94.3% accuracy for the YouTube Faces database (DB) (93.2% in literature with an unsupervised protocol)

A new proxy signature scheme as secure as EIGamal signature

Proxy signature helps the proxy signer to sign messages on behalf of the original signer. It is very useful when the original signer (e.g. the president of a company) is not available to sign a specific document. If the original signer can not forge valid proxy signatures through impersonating the proxy signer, it will be robust in a virtual environment; thus the original signer can not shift any illegal action initiated by herself to the proxy signer. In this paper, we propose a new proxy signature scheme. The new scheme can prevent the original signer from impersonating the proxy signer to sign messages. The proposed scheme is based on the regular ElGamal signature. In addition, the fair privacy of the proxy signer is maintained. That means, the privacy of the proxy signer is preserved; and the privacy can be revealed when it is necessary

A New Proxy Signature Scheme As Secure As ElGamal Signature

Proxy signature helps the proxy signer to sign messages on behalf of the original signer. It is very useful when the original signer (e.g. the president of a company) is not available to sign a specific document. If the original signer can not forge valid proxy signatures through impersonating the proxy signer, it will be robust in a virtual environment; thus the original signer can not shift any illegal action initiated by herself to the proxy signer. In this paper, we propose a new proxy signature scheme. The new scheme can prevent the original signer from impersonating the proxy signer to sign messages. The proposed scheme is based on the regular ElGamal signature. In addition, the fair privacy of the proxy signer is maintained. That means, the privacy of the proxy signer is preserved; and the privacy can be revealed when it is necessary

Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks

Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms

A new encryption algorithm over elliptic curve

Various public key encryption systems have been proposed in modern information techology. Some of them have also been used in various applications, such as E-commerce and mobile database. This paper proposes two secure receipt oriented encryption systems. The decryptioner's private keys could be changed with the different time periods. This case would be very useful in some practical scenarios, for instance, in a mobile database environment. Besides the semantic security, the proposed schemes have the backward-and-future security, a new security requirement for semantically secure encryption schemes. In terms of construction, the two schemes are based on the pairings over elliptic curves. Also, this paper provides a heuristic security analysis for the underlying system

A Magneto-Mechanical Piezoelectric Energy Harvester Designed to Scavenge AC Magnetic Field from Thermal Power Plant with Power-Line Cables

Piezoelectric energy harvesters have attracted much attention because they are crucial in portable industrial applications. Here, we report on a high-power device based on a magneto-mechanical piezoelectric energy harvester to scavenge the AC magnetic field from a power-line cable for industrial applications. The electrical output performance of the harvester (×4 layers) reached an output voltage of 60.8 Vmax, an output power of 215 mWmax (98 mWrms), and a power density of 94.5 mWmax/cm3 (43.5 mWrms/cm3) at an impedance matching of 5 kΩ under a magnetic field of 80 μT. The multilayer energy harvester enables high-output performance, presenting an obvious advantage given this improved level of output power. Finite element simulations were also performed to support the experimental observations. The generator was successfully used to power a wireless sensor network (WSN) for use on an IoT device composed of a temperature sensor in a thermal power station. The result shows that the magneto-mechanical piezoelectric energy harvester (MPEH) demonstrated is capable of meeting the requirements of self-powered monitoring systems under a small magnetic field, and is quite promising for use in actual industrial applications

A Flexible Piezoelectric Energy Harvester-Based Single-Layer WS2 Nanometer 2D Material for Self-Powered Sensors

A piezoelectric sensor is a typical self-powered sensor. With the advantages of a high sensitivity, high frequency band, high signal-to-noise ratio, simple structure, light weight, and reliable operation, it has gradually been applied to the field of smart wearable devices. Here, we first report a flexible piezoelectric sensor (FPS) based on tungsten disulfide (WS2) monolayers that generate electricity when subjected to human movement. The generator maximum voltage was 2.26 V, and the produced energy was 55.45 μJ of the electrical charge on the capacitor (capacity: 220 μF) when applying periodic pressing by 13 kg. The generator demonstrated here can meet the requirements of human motion energy because it generates an average voltage of 7.74 V (a knee), 8.7 V (a sole), and 4.58 V (an elbow) when used on a running human (weight: 75 kg). Output voltages embody distinct patterns for different human parts, the movement-recognition capability of the cellphone application. This generator is quite promising for smart sensors in human–machine interaction detecting personal movement