Foundations For Microwave Engineering

In this paper, we propose two palmprint identification schemes using fusion strategy. In the first fusion scheme, firstly, the principal lines of test image is extracted, and matched with that of all training images. Secondly, those training images with large matching scores are selected to construct a small training sub-database. At last, the decision level fusion, combing matching scores of principal lines and Locality Preserving Projections features, has been made for final identification in small training sub-database. From another point of view, it can be seen that the fusion is restricted by the previous results of principal lines matching, so we call it as restricted fusion. The second fusion scheme is similar to the first One. Just the fusion order is changed. The results of experiments conducted on PolyU palmprint database show that the proposed schemes can achieve 100% accurate recognition rate.

Palmprint identification using restricted fusion

This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework's computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

/pdf/cross-view-gait-recognition-using-joint-direct-linear-cy3hddurap.pdf

Cross view gait recognition using joint-direct linear discriminant analysis

In day to day life, security of an object or property plays a major role. Nowadays, security is the major threat faced by most of the organizations; hence security is gaining more importance in these days. This paper gives a survey on various automatic identification and access control mechanisms that have been used over the years to prevent unauthorized access. In olden days, for high security zones like locker rooms for banks, military sites etc, traditional lock systems or passwords were employed. But this solution was not secure. Due to the advancements in technology RFID cards were used, but this was not useful for the user due to the chance of getting lost, forgotten or stolen. Later various door lock security systems based on biometrics, GSM, OTP, cryptography etc were developed. A lot of research is going on various automatic door lock systems and can expect more secure systems in the upcoming years.

Survey on various door lock access control mechanisms

In spite of the availability of digital password lockers and advanced door locks, hacking the lock code by an unauthorized person has become a plain-sailing task. Various electronic locks are currently available in the market based on Password, RFID, Biometrics, OTP, Cryptography, Wireless and IoT. Every system has its own advantages and disadvantages where one system overcomes the limitations of other. The existing systems provide only single factor authentication that leads to less secure system. Thus, a secure door locking system with two-factor authentication and multiple encryptions using RFID, which can activate, authenticate, and validate the user and unlock the door in real time for secure access has been proposed. The main objective is to design and implement a digital security system which can deploy in critical zone where only authorized person can be entered.

Super secure door lock system for critical zones

Today people are facing more problems about security in all over world, nowadays security is the most essential issue everywhere in the world; so security of everything gains higher and higher importance in recent years. Here in this paper, trying to reproduce the comprehensive literature study related to the various door locks and gate security systems that are necessary in the fields such as home, industries and vehicle security where possibilities of incursion are increasing day by day. In past days, the research is gone on various door lock security systems like traditional security systems which provide indications using alarm. Due to the advancement in recent techniques, some door lock security systems are based on microcontroller, GSM, GPS, many sensors, software like MATLAB, PROTEUS, biometrics like face recognition, Iris scanner, RFID, Smart Card and password etc. Each system has their own advantages and disadvantages. In most of systems, SMS technique is used for communication so the system will become cost effective, more reliable and it will take less time to deliver message. As security becomes major problem nowadays, the security monitoring systems today needs to

https://www.ijcaonline.org/archives/volume153/number2/nehete-2016-ijca-911971.pdf

Literature Survey on Door Lock Security Systems

The digital down converter (DDC) is a principal component in modern communication systems. The DDC process traditionally entails quadrature down conversion, bandwidth reducing filters and commensurate sample rate reduction. To avoid group delay, distortion linear phase FIR filters are used in the DDC. The filter performance specifications related to deep stopband attenuation, small in-band ripple and narrow transition bandwidth lead to filters with a large number of coefficients. To reduce the computational workload of the filtering process, filtering is often performed as a two-stage process, the first stage being a down sampling Hoegenauer (or cascade-integrated comb) filter and a reduced sample rate FIR filter. An alternative option is an M-Path polyphase partition of a band cantered FIR filter. Even though IIR filters offer reduced workload to implement a specific filtering task, the authors avoid using them because of their poor group delay characteristics. This paper aims to propose the design of M-path, approximately linear phase IIR filters as an alternative option to the M-path FIR filter.,Two filter designs are presented in the paper. The first approach uses linear phase IIR low pass structure to reduce the filter’s coefficient. Whereas the second approach uses multipath polyphase structure to design approximately linear phase IIR filter in DDC.,The authors have compared the performance and workload of the proposed polyphase structured IIR filters with state-of-the-art filter design used in DDC. The proposed design is seen to satisfy tight design specification with a significant reduction in arithmetic operations and required power consumption.,The proposed design is an alternate solution to the M-path polyphase FIR filter offering very less number of coefficients in the filter design. Proposed DDC using polyphase structured IIR filter satisfies the requirement of linear phase with the least number of computation cost in comparison with other DDC structure.

Low computation digital down converter using polyphase IIR filter

The automatic creation of a repository of the building’s floor plan helps a lot to the architects to reuse them. The basic approach is to extract and recognize texts, symbols or graphics to retrieve the information of the floor plan from the images. This paper proposes a floor plan information retrieval algorithm. The proposed algorithm is based on shape extraction and room identification.
 $$\alpha $$
 -shape is used for finding an accurate shape. From the detected shapes, actual areas of rooms are calculated. Later, a regression model-based binary room classification model is proposed to classify them into room-type, i.e., bedroom, drawing room, kitchen, and non-room-type, i.e., parking porch, bathroom, study room and prayer room. The proposed model is tested on the CVC-FP dataset with an average room detection accuracy of 85.71% and room recognition accuracy of 88%.

Automatic room information retrieval and classification from floor plan using linear regression model

The ear has emerged as a new biometric trait to recognize humans from their profile faces. Stability over the years, noninvasive capturing process, expressionless images, and significant variation in shape among individuals make the ear a suitable choice when compared with other biometrics. Convolutional neural network (CNN)’s capability to learn and discriminate specific features irrespective of image variation makes it the most suitable choice for ear recognition in comparison to local geometrical feature extraction methods. The recognition performance affecting factors such as occlusion, image resolution, and head rotation during ear acquisition in an uncontrolled environment makes it a challenging task, and the use of spatial features only in CNN limits the recognition rate. We explored the various CNN configurations and propose the spectral–spatial feature-based CNN. An embedding algorithm is proposed to fuse multilevel spectral information from the image domain with spatial features extracted at each deep layer of the CNN network. The poor convergence problem of CNN is tackled by introducing batch normalization after every convolution. The analysis of the result shows that the proposed framework is superior among all CNN achieving 98.15% and 78.88% accuracy on the Annotated Web Ears with 1000 images and earVN1.0 with 28,412 images, respectively. In addition to fine-tuning and configuring the layers of CNN, the fusion of missing spectral features and spatial features can strengthen the CNN.

Wavelet features embedded convolutional neural network for multiscale ear recognition

This paper presents the development and implementation of a low phase-noise stable signal generator for a D-band microwave interferometer application used in the fusion plasma diagnostics. ...

Jitendra Chaudhari

Papers

Literature Survey on Door Lock Security Systems

Low computation digital down converter using polyphase IIR filter

Automatic room information retrieval and classification from floor plan using linear regression model

Wavelet features embedded convolutional neural network for multiscale ear recognition

Phase Locked Loop System with SIW-based Bandpass Filter for D-band Microwave Interferometer