CRYSTALS-Kyber, officially standardized by the U.S. National Institute of Standards and Technology (NIST) in August 2024 as ML-KEM FIPS, is the only post quantum secure key encapsulation mechanism (KEM) standard. Due to its inherent computationally intensive nature, mapping it on lightweight IoT devices is often a struggle. This study examines the replacement of the Keccak hashing function in Kyber-KEM with the NIST LWC competition winner called Ascon hash function. We compare speed/ memory improvement by executing Kyber-KEM-Ascon on an ARM Cortex M4 device and present novel benchmarking results; Kyber-KEM-Ascon shows about 25% reduction in clock cycles, along with lower memory usage requirements (about 8%). These results suggest that Kyber-KEM-Ascon is more suited for lightweight platforms, offering benefits for IoT security applications.

This letter presents underwater image enhancement using Laplace decomposition. Underwater image undergoes Laplace decomposition resulting in low- and high-frequency bands. Haze is removed from the low-frequency band, and then it is normalized for white balancing. The high-frequency band is amplified for edge preservation. Adding the two frequency images results in an enhanced image. It has improved, in contrast, color, object prominence, edge preservation, reduced artifacts, and naturalness as compared to other methods related to underwater image enhancement.

This letter proposes a content-based image retrieval technique using novel dense angle descriptor and dictionary learning (DL). The histogram of oriented gradients (HOG) descriptor fails to obtain rotation invariance and well-defined rotation behavior, and therefore, a dense angle-based HOG descriptor has been presented to address the image rotation invariance. The technique computes angles across multiple scales and uses bag-of-visual features at different scales for DL. Experiments conducted on building and remote sensing datasets show that the proposed technique achieves high retrieval performance.

Abstract The paper presents an iterative matting technique for extraction of underwater objects from images. The technique adopts histogram division and stretching to obtain multiple images of different contrast levels that exhibit all image details. For each contrast image, alpha matte is produced and is further refined with every iteration. In the end, fuzzy weights are assigned to the alpha mattes obtained at different contrast levels that are combined using weighted average. The resultant alpha matte thus includes more accurate pixels from multiple alpha mattes and generates much refined matte image. The proposed technique is tested, visually and quantitatively, on a manual dataset containing 50 images. The less MSE shows that the proposed technique achieves noticeably higher accuracy as compared with contemporary image matting techniques.

An optimal utilization of cellular resources is ambitious target of 5thgeneration (5G) heterogeneous network (HetNet). In 4thgeneration (4G) HetNet, downlink (DL) uplink (UL) coupled access (DUCa) strategy where a mobile station (MS) is associated in DL & UL with a one base station (BS) to ensure energy efficiency (EE) and throughput maximization is a suboptimal solution. An optimal solution in term of EE and throughput is offered by novel DL and UL decoupled access (DUDa) strategy where a MS is associated in DL & UL with one or two BSs in 5G HetNet. This research work conducts performance analysis of DUCa strategy vs DUDa strategy for effective resource allocation to ensure EE in HetNet. First, EE maximization problem in term of resource allocation using DUCa and DUDa strategy in HetNet is formulated. The formulated problem belongs to a class of non-linear fractional programming problem. The formulated fractional programming problem is transformed to a concave optimization problem employing Charnes-Cooper transformation. Then outer approximation algorithm (OAA) is employed to solve the formulated concave optimization problem. Extensive simulation work is done to show the effectiveness of proposed algorithm. OAA approach gives ε-optimal solutions for multiple parameters such as MSs associated, minimum required data rate, EE and throughput in DUCa and DUDa HetNet.

This study presents a technique to automatically extract the aircraft from video sequences. The technique incorporates object tracking algorithm to separate the region of interest (ROI), i.e. movement of aircraft, from video sequence, to reduce the computational complexity. Trimaps are generated automatically for given ROI using non‐rigid image registration to obtain the prior information of the object and alpha mattes are estimated for each frame using KNN matting algorithm. However, the alpha matte of static region is estimated only once, that is combined with the alpha matte of moving region to generate final alpha matte. Simulations performed on different videos show that the proposed technique not only reduces the computational complexity, but also gives competitive results by generating high quality alpha maps.

An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre‐processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification.

The study presents a content‐based retrieval technique for retinal images. Given a query image, the aim is to automatically retrieve the relevant disease images (i.e. diabetic retinopathy, coats and choroidal neovascularisation) from the database. The proposed technique applies different pre‐processing steps to enhance the region of the exudate present in the retina. Segmentation step separates the exudates and vessels region to extract shape and texture features which are optimally combined with an automatic weight assignment approach. The simulations are performed on STARE dataset and compared with other existing techniques to ensure the significance of the proposed technique.

A framework for image visibility restoration and haze removal is proposed. The proposed technique utilizes hybrid median filtering in conjunction with accelerated local Laplacian filtering for initial dehazing of images. For visual enhancement and correct restoration of colors, constrained l0-based gradient image decomposition is applied. The proposed technique not only effectively removes haze from the images but also addresses the issues of distorted colors, visual, and halo artifacts, and haze removal from sky region in images in a better way when compared to other techniques. Experiments were performed on outdoor RGB images as well as remotely sensed images. The effectiveness of our proposed technique is demonstrated by quantitative and visual analyzes.

In this letter, an improved single image dehazing technique based on quadtree decomposition and entropy-based weighted contextual regularization is proposed. The boundary constraints are computed adaptively using statistical properties of image. The proposed technique produces high-quality dehazed image with better colors and minimal blocking artifacts. Simulation results compared visually and quantitatively with state-of-the-art existing schemes show the significance of proposed technique.

A fuzzy logic and additive wavelet-based image fusion scheme is proposed. The scheme injects high-frequency information from a high-resolution panchromatic image into a low-resolution multispectral image taking both the intensity levels of each band and spatial information of panchromatic image into consideration. The proposed scheme preserves both spatial and spectral information. Quantitative analysis performed on the Ikonos and Quickbird data sets demonstrates that the proposed scheme outperforms state-of-the-art multiresolution image fusion schemes.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. A fuzzy logic based technique is proposed for indirect trust computation that measures the credibility of recommendation (rather than credibility of recommender)to determine the influence of each honest recommendation.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. A fuzzy logic based technique is proposed for indirect trust computation that measures the credibility of recommendation (rather than credibility of recommender)to determine the influence of each honest recommendation.

Energy Detection (ED) is a well known approach for spectrum sensing due to its simplicity, low computational cost and its validity for all signal formats. However, it is ineffective under low signal to noise ratio conditions. On the other hand, the recent convergence of almost all the wireless standards to the incorporation of spatial dimension in these wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. Though it works well under all channel conditions but the related cost is high computational complexity of the algorithm. With the motivation of improving the sensing ability of cognitive radio under all channel conditions combined with the existing hardware constraints of low computational complexity, we have proposed an amalgamation of the aforementioned schemes (ED and PAPR based spatial spectrum sensing) with an element of adaptation by switching between these two techniques depending upon channel states. Proposed adaptive scheme is compared with ED and PAPR based spatial spectrum sensing on the basis of probability of detection, probability of false alarm and complexity.

A two step hybrid image fusion scheme is proposed for panchromatic and multi-spectral satellite sensors. First, we estimate an intermediate high/low resolution multi-spectral image using component substitution, which is followed by additive wavelet based high frequency injection into low resolution multi-spectral bands. Spectral dissimilarities between panchromatic and multi-spectral bands are taken into account while devising partial replacement strategy for component substitution. Quantitative analysis performed on Ikonos data set demonstrates that the proposed scheme outperforms state of the art multi-resolution image fusion schemes.

A fuzzy logic based active contour model for medical image segmentation is proposed. Image local features are incorporated in active contour model. Fuzzy logic is used to assign weights to pixels. Higher weights are assigned to pixels having less entropy and local variance whereas Lower weights are assigned to pixels having high entropy and local variance. Simulation results show the improvement in segmentation results in terms of efficiency and accuracy.

Principal component analysis and minimum description length criterion based image enhancement is proposed for through wall imaging. Minimum description length criterion is used with principal component analysis to find target eigen values. Simulation results compared on the basis of mean square error, peak signal to noise ratio and visual inspection shows the significance of proposed scheme.

A fuzzy logic and additive wavelet based fusion scheme is proposed. The scheme injects high frequency information from high resolution panchromatic image into low resolution multi-spectral bands taking both intensity level of each band and spatial information of panchromatic image into consideration. Quantitative analysis performed on IKONOS data set demonstrates that proposed scheme outperforms state of the art multi-resolution image fusion schemes.

A robust image watermarking scheme based on human visual system is proposed. The perceptual quality of the watermarked image is controlled by determining adaptive scaling factor for each individual pixel value. To determine the adaptive scaling factor, human visual system (texture masking) and fuzzy inference systems were used. The quality of the proposed scheme is tested against various attacks (Histogram equalization, rotation, Gaussian noise, scaling, cropping, JPEG compression, Y-shearing, X-shearing, median filtering, affine transformation, translation, sharpening, blurring, average filtering). The perceptual quality of watermarked image is calculated using peak-signal-to-noise-ratio, whereas the extracted watermark's quality is measured using normalized correlation.

Singular value decomposition and Fuzzy C-Means based clutter reduction is proposed for ground penetrating radar imaging. The scheme is capable of discriminating target, clutter and noise subspaces. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy c-means and target image is obtained by weighted sum of different spectral components. Proposed scheme works also for extracting multiple targets in heavy cluttered images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection.

Singular value decomposition and Akaike information criterion based image enhancement is proposed for through wall imaging. Akaike information criterion is used with singular value decomposition to find spectral images belonging to targets. Simulation results compared on the basis of mean square error, peak signal to noise ratio, miss detection, false detection and visual inspection show the significance of the proposed scheme.

Non-contiguous orthogonal frequency division multiplexing is an adaptable multicarrier modulation technique that achieves high data rates using non-contiguous subcarriers and can be the very suitable modulation technique to be used at the physical layer of cognitive radio. However its main drawback is high peak to average power ratio (PAPR). Different techniques have been used to combat this problem but all have some tradeoffs. An adaptive mode PAPR reduction algorithm has been proposed using tone reservation technique where some subcarriers called peak reducing carriers (PRCs) are reserved to reduce the signal's high peaks instead of data transmission. Different methods (including second order cone programming (SOCP), gradient and geometric) are used to generate these PRCs. The proposed algorithm uses adaptivity within and between these methods. This adaptive selection is based on the PAPR value of the signal and gives better PAPR reduction. The novelty in this approach is that the transmission of PRCs and secondary users will be through the use of underlay and overlay transmission respectively. PRCs shall share the bands occupied by the primary users at power levels so low that primary users in the same bands are not effected (underlay transmission) while the secondary users access the spectrum unoccupied by the primary users through dynamic spectrum sensing (overlay transmission).

This research work deals with the segmentation of grey scale, colour and texture images using graph‐based method. A graph is constructed using intensity, colour and texture profiles of image simultaneously. Based on nature of the image, a fuzzy rule‐based system is used to find the weight that should be given to a specific image feature during the graph development. The fuzzy rule‐based system provides a valuable approximation to cater the fact of imprecise knowledge (in our case knowledge about the involvement of a particular image feature in image). The graph is further used in multilevel graph‐partitioning algorithm based on normalised graph cuts framework where it is iteratively bi‐partitioned through normalised cuts to obtain optimum partitions. Multilevel algorithm makes the process fast enough to accommodate large databases as segmentation is often used in high‐level image processing‐techniques (i.e. object classification and recognition). Partitioned graph then results in segmented image. Berkeley segmentation database is used to experiment on the authors algorithm. The segmentation results are evaluated through probabilistic rand index and global consistency error methods. It is shown that the presented segmentation method provides effective results for most type of images.

Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing high-frequency contents (which results in blurring). The discrete-wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property). A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high-frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.

An adaptive trust based security architecture (based on human notion of trust) as a mechanism to secure computing in pervasive environment is proposed. The model encompasses the ability to reason human cognitive behavior and has the capability to adjust in accordance with behavioral pattern changes. This capability is currently lacking in the existing models as they rely only on mathematical calculation. A strategic attack-based analysis is used for evaluation of the proposed model. Finally we have compared our model with existing evolutionary models in this field.

A non-blind color image watermarking scheme using principle component analysis, discrete wavelet transform and singular value decomposition is proposed. The color components are uncorrelated using principle component analysis. The watermark is embedded into the singular values of discrete wavelet transformed sub-band associated with principle component containing most of the color information. The scheme was tested against various attacks (including histogram equalization, rotation, Gaussian noise, scaling, cropping, Y-shearing, X-shearing, median filtering, affine transformation, translation, salt & pepper, sharpening), to check the robustness. The results of proposed scheme are compared with state-of-the-art existing color watermarking schemes using normalized correlation coefficient and peak signal to noise ratio. The simulation results show that proposed scheme is robust and imperceptible.

This work deals with segmentation of the gray scale, color and texture images using graph cuts. From the input image, a graph is constructed using intensity, color and texture profiles of the image simultaneously. Based on nature of the image, a fuzzy rule based system is designed to find the weight that should be given to a specific image feature during graph development. The graph obtained from fuzzy rule based weighted average of different image features is further used in normalized graph cuts framework. The graph is iteratively bi-partitioned through the normalized graph cuts to get optimum partitions resulting in segmented image. The Berkeley segmentation database is used to test our algorithm and segmentation results are evaluated through probabilistic rand index and global consistency error methods along with binary classifiers based approaches like sensitivity, positive predictive value and Dice similarity coefficient. It is shown that the presented segmentation method provides effective results for most type of the images.

Wavelet transform and principal component analysis based clutter reduction scheme is proposed for through wall imaging which is capable of discriminating between target, noise and clutter signals. Principal component analysis is used to reduce clutter and wavelet transform is used to further suppress noise signals (which results in enhancement of target signals). Proposed scheme significantly work well especially for extracting multiple targets in heavy clutters. Selection of different parameters (like mother wavelet, threshold type, threshold parameter, decomposition level and filter order etc) are explored for TWI. Existing and proposed schemes are compared on the basis of mean square error, peak signal to noise ratio and visual inspection.

A novel wavelet domain approach based on nonlocal means filtering is presented to eliminate artifacts generated in discrete wavelet domain based super resolution techniques. These artifacts are generated due to shift variance of discrete wavelet transform. Objective analysis reveal superiority of the proposed technique over the conventional and state of art super resolution techniques.

An optimal adaptive multiband-sensing framework is used to jointly detect a wideband BPSK primary signal over multiple non-overlapping narrowband Gaussian channels so as to maximize the throughput in cognitive radio networks using cyclostationary detection scheme. Under some specific primary user and channel conditions the framework which appeared to be non-convex is explored to determine convexity range.

A robust color image watermarking scheme is proposed, in which a color watermark is embedded into a color image. Principle component analysis is used to uncorrelate the R, G and B channels of both the images. Each channel of color watermark is embedded into singular values of corresponding channel of cover image after discrete wavelet decomposition. As original image is required at the time of extraction of watermark so given scheme is non-blind. Peak signal to noise ratio is used to measure the imperceptibility whereas similarity between original and extracted watermark is measured using normalized correlation coefficient. The scheme was tested against various attacks (including histogram equalization, Gaussian noise, cropping, Y-shearing, X-shearing, affine transformation, salt & pepper), to check the robustness. The results of proposed scheme are compared with existing color watermarking schemes. The results show that proposed scheme is robust and imperceptible.

Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy c-means clustering algorithm. Moreover, weights are assigned to different singular values based on their membership value. Proposed scheme significantly works well for extracting multiple in heavy cluttered through wall images. Simulation results are compared on the basis mean square error, peak signal to noise ratio and visual inspection.

An optimal multiband spectrum sensing framework is explored which jointly detects a wideband orthogonal frequency-division multiple access primary signal over multiple non-contiguous narrowband channels. The spectrum sensing problem optimizes the aggregate opportunistic throughput of a cognitive radio network restraining the interference to the primary user keeping the individual energy detector thresholds and sensing slot duration as optimization variables. The problem is solved as convex optimization under certain practical constraints for any input signal and channel noise conditions. Simulated results for BPSK modulated signal and real-valued Gaussian noise show the increase in secondary throughput when both subchannel thresholds and sensing time is adaptively chosen. The effect of sub-channel parameter variation on the overall problem is also shown.

An optimal joint multiband-sensing framework is studied to maximize the throughput in cognitive radio networks. Convexity range for this non-convex framework is calculated under certain channel conditions keeping either sensing time or threshold constant. Also joint convexity range for both sensing time and threshold is determined. Waveform based detection, which is accurate and better than other detection schemes in terms of reliability and convergence time, is applied.

Trust management for multimedia P2P applications in autonomic networking, which is a mathematical evolutionary model and uses historical behaviour for trust calculation is critically analyzed. It is shown that this model is not attack resistant. Several critical remarks on its behaviour are given. An improvement is presented to overcome these shortcomings.

To secure computing in pervasive environment, an adaptive algorithmic approach for access control (based on human notion of trust) is proposed. The proposed model provides support to calculate direct trust in situations when the requesting entity has a past experience with the service. It encompasses the ability to reason human cognitive behavior and has the capability to adjust in accordance with behavioral pattern changes. An adaptive policy based management is proposed to handle strategic malicious behavior. The defense mechanism incorporated by proposed model against strategic attack is also demonstrated. Finally we have compared our model with existing evolutionary models in this field.

An optimal multiband spectrum sensing algorithm is explored which jointly detects the wideband circularly symmetric complex Gaussian primary signal over multiple frequency bands. The spectrum sensing problem is formulated as a set of optimization problems, which maximizes the aggregate opportunistic throughput of a cognitive radio system under some constraints on the interference to the primary user. It is shown through mathematical transformations that these problems can be solved as convex optimization under certain practical constraints.

In this paper, a survey of frequency weighted balanced model reduction techniques is presented. Several comments regarding their properties are given. The techniques are illustrated and compared using a practical numerical example