Discrete Haar Wavelet Transform Research Articles

Smart Recognition and Authentication based on Haar Cascade Classifier and Visual Cryptography

The algorithm with an E-verification method is proposed that combines automated facial recognition technology with digital media and later it combines aspects of secret data exchange and visual cryptography. A face detection technique based on Internet of Things (IoT) selects features using the Principal Component Analysis (PCA) algorithm and the Haar cascade classifier. The selected face serving as a cover follows two level Discrete Haar Wavelet Transform. The shares generated from digital signature and fingerprint are diffused into the converted coefficients. Furthermore, the imperceptibility of the additional noise is increased by a bit-level noise reduction technique. Authenticity is confirmed by regenerating a message digest at the receiving end, and the extraction process operates in complete blindness. The approach is suitable for smart card design and may be used as an automatic recognition system in a real-world setting. Performance comparisons show a notable improvement over other approaches that are comparable. Additionally, the technique is effective against some related attacks.

An efficient VoLTE covert timing channel for 5 G networks: RDCTC

Covert Channels are necessary to protect the privacy of individuals as well as to obtain confidentiality to governmental and military agencies. A novel covert timing channel is proposed with three main improvements over the existing ones: Reorder Density (RD) metric-based packet reordering that reduces exposure, introducing Modified Turbo Code (MTC) for efficient encoding, and Discrete Haar Wavelet Transform (DHWT) that increases channel reliability. A threat model is proposed and shown to be inefficient to effectively meet covert channel properties against network jitter, noise, and curious attackers that try to identify a channel exists. Run and test is applied to investigate randomness of the proposed covert channel. The proposed covert channel is adoptable to 5 G VoLTE networks. These proposed strategies significantly improve the robustness while also minimizing the detectability of covert channels in VoLTE.

Image and video dehazing based on transmission estimation and refinement using Jaya algorithm

Due to its in-built ill-posed nature, image and video dehazing is an immensely thought-provoking problem, and it is an essential pre-processing step for various applications such as ADAS (automatic driver-assistance systems), security and surveillance, remote sensing, and long-range imaging. We proposed a novel single image and video dehazing method, called JA-dehaze, by using Jaya Algorithm (JA) to address this problem. The technique consists of three factors: Discrete Haar Wavelet Transform (DHWT) for sub-bands, local atmospheric light estimation and transmission map estimation and refinement using Jaya Algorithm. We formulated haze removal as an optimization problem, and the fitness function for the single image is taken from the information loss term, distance and the haze density term. The JA-dehaze method improves the gradient and preserves the information optimally by minimizing the fitness function. Furthermore, we extended the single image dehazing process to real-time video dehazing. Experimental results demonstrate that the proposed JA-dehaze method can successfully improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.

No-Reference Quality Assessment for 360-Degree Images by Analysis of Multifrequency Information and Local-Global Naturalness

360-degree/omnidirectional images (OIs) have received remarkable attention due to the increasing applications of virtual reality (VR). Compared to conventional 2D images, OIs can provide more immersive experiences to consumers, benefiting from the higher resolution and plentiful field of views (FoVs). Moreover, observing OIs is usually in a head-mounted display (HMD) without references. Therefore, an efficient blind quality assessment method, which is specifically designed for 360-degree images, is urgently desired. In this paper, motivated by the characteristics of the human visual system (HVS) and the viewing process of VR visual content, we propose a novel and effective no-reference omnidirectional image quality assessment (NR OIQA) algorithm by MultiFrequency Information and Local-Global Naturalness (MFILGN). Specifically, inspired by the frequency-dependent property of the visual cortex, we first decompose the projected equirectangular projection (ERP) maps into wavelet subbands by using discrete Haar wavelet transform (DHWT). Then, the entropy intensities of low-frequency and high-frequency subbands are exploited to measure the multifrequency information of OIs. In addition to considering the global naturalness of ERP maps, owing to the browsed FoVs, we extract the natural scene statistics (NSS) features from each viewport image as the measure of local naturalness. With the proposed multifrequency information measurement and local-global naturalness measurement, we utilize support vector regression (SVR) as the final image quality regressor to train the quality evaluation model from visual quality-related features to human ratings. To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degree images that combines multifrequency information and image naturalness. Experimental results on two publicly available OIQA databases demonstrate that our proposed MFILGN outperforms state-of-the-art full-reference (FR) and NR approaches.

DWT Based QR Steganography

Data security pays more attention in most of the communication fields. Image steganography is one of the leading data security techniques that provides confidentiality with high embedding capacity and high steganography image quality. This work proposes Discrete Haar Wavelet Transform (DWT-H) and bitwise operations-based embedding technique to embed secret QR-code message in the cover image and extraction of QR-code secret message is also carried out by the same methodology. The efficiency of the proposed method is estimated by Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). This technique provides less MSE and PSNR in the range of 31dB to 44dB and extracts QR code image up to version 40. The results of the proposed method provide the practical feasibility for QR-coded-based security applications with less time and high accuracy.

A 100Gbps all‐optical orthogonal frequency division multiplexing based on discrete Haar wavelet transform for radio over fiber system

AbstractAll‐optical orthogonal frequency division multiplexing (OOFDM) for a radio over fiber system (RoF) using discrete Haar wavelet transform (DHWT) is proposed and investigated for 100Gbps system. The system makes use of passive optical couplers to generate orthogonal symbols based on DHWT. Phase preemphasis technique is incorporated to minimize high peak average power ratio, which in turn minimizes the nonlinear effects of the system. Simulation set up of 25 × 4Gbps all‐optical OFDM is compared with that of the existing systems. Results of the proposed system are on a par with that of the conventional OFDM‐RoF systems employing discrete fourier transform.

Colour-image encryption based on 2D discrete wavelet transform and 3D logistic chaotic map

This paper proposes a new lossless encryption and decryption method for digital colour-image using 2D discrete Haar wavelet transform (DHWT) and 3D logistic chaotic map. The proposed cryptosystem provides the security of the digital images using the cryptographic algorithm in the frequency domain of the image. The proposed method uses two levels of 2D DHWT for the decomposition of the image. For the encryption, 2D DHWT is applied on the different colour components of the colour-image and then the encryption method is applied on the different sub-bands of each level of decomposition and for the decryption the reverse process of the encryption is used with the inverse of functions used in encryption. The experimental results and the security analysis like brute force analysis, statistical analysis, occlusion and differential attack of the proposed method are given to validate the feasibility and robustness of the method.

Digital Watermarking for Vector Drawings based on Discrete Haar Wavelet Transform

Digital Watermarking for Vector Drawings based on Discrete Haar Wavelet Transform

Hardware based Brain MR Image De-Noising using Weiner filter with Discrete Haar Wavelet Transform

Hardware based Brain MR Image De-Noising using Weiner filter with Discrete Haar Wavelet Transform

Imperceptible Image Authentication Using Wavelets

This paper introduces an enhanced image authentication technique providing greater security with uncompromising visual quality. To augment data security, the authenticating information is diffused into the transformed coefficients of both the levels after a two level Discrete Haar Wavelet Transform. In addition a bit level noise reduction algorithm increases the imperceptibility of the added noise. The extraction algorithm is completely blind and authenticity is verified by regenerating a message digest at the receiving end. The algorithm has been tested against some related attacks and is appropriate in smart card design. Performance comparisons exhibit significant growth over other similar techniques.

Audio Steganography using Echo Hiding in Wavelet Domain with Pseudorandom Sequence

paper deals with the encoding of message bits in a cover audio carrier signal. It applies the basic concepts of audio steganography in transform domain to achieve higher efficiency in data transmission, while preserving the secrecy of the information being transmitted. The proposed model in this paper deals with the application of echo hiding of binary message bits in the carrier signal, in the transform coefficients obtained by applying 2D- discrete Haar Wavelet Transform on the cover signal. Moreover, this algorithm applies pseudorandom sequence to encode the data, which gives the method more efficiency and prevents unauthorized decoding at any moment. The application of echo hiding technique makes the stego signal more immune to noise and disturbances, during transmission through the channel network. The performance of this method is analyzed on the basis of the output SNR and PSNR values calculated for several test cases, that has been discussed later in the paper.

A method for compression of intra-cortically-recorded neural signals dedicated to implantable brain-machine interfaces.

This paper proposes an efficient data compression technique dedicated to implantable intra-cortical neural recording devices. The proposed technique benefits from processing neural signals in the Discrete Haar Wavelet Transform space, a new spike extraction approach, and a novel data framing scheme to telemeter the recorded neural information to the outside world. Based on the proposed technique, a 64-channel neural signal processor was designed and prototyped as a part of a wireless implantable extra-cellular neural recording microsystem. Designed in a 0.13- μ m standard CMOS process, the 64-channel neural signal processor reported in this paper occupies ∼ 0.206 mm(2) of silicon area, and consumes 94.18 μW when operating under a 1.2-V supply voltage at a master clock frequency of 1.28 MHz.

A new similarity measure based on shape information for invariant with multiple distortions

Due to the characteristics of noise and volatility, two similar time series always appear in diverse kinds of distortions, which usually are considered as the combinations of the following basic transformations: noise, amplitude shift, amplitude scaling, temporal scaling, and linear drift. In this paper, a novel similarity measure (SIMshape) invariant to these basic distortions and any combinations of them is proposed. It is parameter-free and easy to implement. Specifically, a multi-scale shape approximation for time series based on Discrete Haar Wavelet Transform, key point extraction and symbolization is presented first; then, based on this proposed representation and a scale-weight factor, a robust similarity measure is proposed. The novelty of SIMshape lies in two aspects as follows: (a) symbolizing key points sequence extracted from approximate wavelet coefficients; (b) adding the scale-weight factor and shape similarity in the similarity criterion. To show the effectiveness and efficiency, SIMshape is compared with other popular methods Euclidean Distance (ED), LB_keogh, Complexity Invariant Distance (CID), and ASEAL (Approximate Shape Exchange ALgorithm) using two indices: the number of kinds of distortions and the degree of distortion. Obtained results show that compared with ED, CID, LB_keogh, and ASEAL, SIMshape has better robustness in synthetic data, and shows better performance in real time series classification.

해양 환경에서 웨이블렛 변환을 이용한 크기 변화에 무관한 물표 탐지 알고리즘

In this paper, we propose an algorithm to detect scale invariant object from IR image obtained in the sea environment. We create horizontal edge (HL), vertical edge (LH), diagonal edge (HH) of images through 2-D discrete Haar wavelet transform (DHWT) technique after noise reduction using morphology operations. Considering the sea environment, Gaussian blurring to the horizontal and vertical edge images at each level of wavelet is performed and then saliency map is generated by multiplying the blurred horizontal and vertical edges and combining into one image. Then we extract object candidate region by performing a binarization to saliency map. A small area in the object candidate region are removed to produce final result. Experiment results show the feasibility of the proposed algorithm.

Spoofing detection in IEEE 802.15.4 networks based on received signal strength

The shared medium used in wireless networks makes them vulnerable to spoofing attacks, in which an adversary masquerades as one or more legitimate nodes to disturb normal operation of the network. In this paper we present a novel spoofing detection method for static IEEE 802.15.4 networks based on spatial correlation property of received signal strength (RSS). While most existing RSS based techniques directly process RSS values of the received frames and rely on multiple traffic air monitors (AMs) to provide an acceptable detection performance, we extract features of RSS streams to reduce data redundancy and provide a more distinguishable representation of the data. Our algorithm employs two features of RSS streams, summation of detailed coefficients (SDCs) in discrete Haar wavelet transform (DHWT) of the RSS streams and the ratio of out-of-bound frames. We show that in a typical scenario, a single AM with SDC as detection parameter, can theoretically outperform a system with 12 AMs which directly applies RSS values as detection parameter. Using ratio of out-of-bound frames facilitates detection of high rate attacks. In addition, we suggest adaptive learning of legitimate RSS values which enhances the robustness of the attack detector against environmental changes. Using both magnitude and frequency related features, we achieved high detection performance with a single AM; this enables development of preventive measures for spoofing attacks. The performance of our approach was evaluated through an IEEE 802.15.4 testbed in an office environment. Experimental results along with theoretical analysis show that the proposed method outperforms the existing RSS-based spoofing detection solutions. Using a single AM, we were able to attain 94.75% detection rate (DR) with 0.56% false positive rate (FPR). For 4 AMs, the results improved to 99% DR and 0% FPR.

A discrete time continuous level VLSI architecture in current mode to implement Discrete Haar Wavelet Transform

A discrete time continuous level VLSI architecture in current mode is proposed to implement 1-D Discrete Haar Wavelet Transform (DHWT). This architecture is implemented in current mode on unquantized samples, to achieve high dynamic range that is suitable for high frequency applications. DHWT is performed on the incoming samples which are available continuously from a real-time system. In this implementation, data converters are not needed, that leads to a reduction in complexity and savings in area.

Multifractal analysis of 1-min summer rainfall time series from a monsoonal watershed in eastern China

Multifractal analysis can provide parameters associated with different scales of rainfall, which may be useful for setting up parsimonious downscaling models of rainfall, or for revealing climate-specific properties. Time series of rain rate with 1-min resolution collected from ten stations over a monsoon watershed in eastern China were used to study the multifractal properties. The power spectra estimated by fast Fourier transform (FFT) and discrete Haar wavelet transform (DWT) showed three scaling regimes: the sub-hourly scaling regime with β ≈ 1.2, the scaling regime from 1 h to 1 day with β close to 0.6, and the low-frequency spectra plateau with β ≈ 0.1. From the hyperbolic tails of exceeding probability distributions, the estimated values of parameter qc are in 2–2.5, which were consistent with the critical order of K(q) curves. The statistical moments display two main scaling regimes: the high-frequency regime from 3 min to 5 days and the scaling regime beyond 5 days. The scales of 5–10 days seem a transitional regime. The reason that the regimes, revealed by the power spectra, disagree with the statistical moments may be that both FFT and DWT power spectra have limited abilities of analyzing low-frequency scaling but are sensitive to the properties in high-frequency scales. The H values estimated for the regime of sub-hourly scales are larger than 0.4, and the values for the regime 1 h–1 day are close to 0.1. For the low-frequency scales beyond 1 day, negative H is obtained by DWT power spectra. The parameters of universal multifractal models were also estimated. The values of α for the scaling range of 1 min–5 days are 0.486 ± 0.047, and for the low-frequency scaling range, its values are 0.808 ± 0.323. For the high- and low-frequency scaling ranges, the values of C1 are 0.5 and 0.169, respectively, which is different from the values for daily rainfall series collected at the same rain gages.

Ocultamiento del Mensaje de Señales de Voz usando la Transformada de Ondita Haar Discreta

This work introduces a message concealment method for voice signals transmission using codification through multi-resolution filter banks implementation for analysis and synthesis for the Discrete Haar Wavelet Transform. Through these multi-resolution filter banks of Haar wavelet the voice signal can be split in its components, and later a reversible distortion of the synthesis of its multi-resolution component signals is made, obtaining an output signal that, although maintains the same information of the original input signal, hides the message of the carried voice signal in itself. This method can be applied in codified transmission of voice, analog or digital signals, and due to the simplicity of the method it’s suitable to be implemented on digital electronic systems like FPGA, or ASIC.

Wavelet based preconditioners for sparse linear systems

A class of efficient preconditioners based on Daubechies family of wavelets for sparse, unsymmetric linear systems that arise in numerical solution of Partial Differential Equations (PDEs) in a wide variety of scientific and engineering disciplines are introduced. Complete and Incomplete Discrete Wavelet Transforms in conjunction with row and column permutations are used in the construction of these preconditioners. With these Wavelet Transform, the transformed matrix is permuted to band forms. The efficiency of our preconditioners with several Krylov subspace methods is illustrated by solving matrices from Harwell Boeing collection and Tim Davis collection. Also matrices resulting in the solution of Regularized Burgers Equation, free convection in porous enclosure are tested. Our results indicate that the preconditioner based on Incomplete Discrete Haar Wavelet Transform is both cheaper to construct and gives good convergence.