This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including $50\%$ , $70\%$ and $90\%$ of the data used for training, while the remaining $50\%,30\%$ , and $10\%$ constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of $2,4,8,12,16,20,24$ and 28, each with an angle of $0,45,90$ and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of $93.7\%$

Underwater Wireless Sensors Networks (UWSNs) use acoustic waves as a communication medium because of the high attenuation to radio and optical waves underwater. However, acoustic signals lack propagation speed as compared to radio or optical waves. In addition, the UWSNs also pose various intrinsic challenges, i.e., frequent node mobility with water currents, high error rate, low bandwidth, long delays, and energy scarcity. Various UWSN routing protocols have been proposed to overcome the above-mentioned challenges. Vector-based routing protocols confine the communication within a virtual pipeline for the sake of directionality and define a fixed pipeline radius between the source node and the centerline station. Energy-Scaled and Expanded Vector-Based Forwarding (ESEVBF) protocol limits the number of duplicate packets by expanding the holding time according to the propagation delay, and thus reduces the energy consumption via the remaining energy of Potential Forwarding Nodes (PFNs) at the first hop. The holding time mechanism of ESEVBF is restricted only to the first-hop PFNs of the source node. The protocol fails when there is a void or energy hole at the second hop, affecting the reliability of the system. Our proposed protocol, Extended Energy-Scaled and Expanded Vector-Based Forwarding Protocol (EESEVBF), exploits the holding time mechanism to suppress duplicate packets. Moreover, the proposed protocol tackles the hidden terminal problem due to which a reasonable reduction in duplicate packets initiated by the reproducing nodes occurs. The holding time is calculated based on the following four parameters: (i) the distance from the boundary of the transmission area relative to the PFNs’ inverse energy at the 1st and 2nd hop, (ii) distance from the virtual pipeline, (iii) distance from the source to the PFN at the second hop, and (iv) distance from the first-hop PFN to its destination. Therefore, the proposed protocol stretches the holding time difference based on two hops, resulting in lower energy consumption, decreased end-to-end delay, and increased packet delivery ratio. The simulation results demonstrate that compared to ESEVBF, our proposed protocol EESEVBF experiences $20.2\%$ lesser delay, approximately $6.66\%$ more energy efficiency, and a further $11.26\%$ reduction in generating redundant packets.

In the current age of advanced technologies, there is an escalating demand for reliable wireless systems, catering to the high data rates of mobile multimedia applications. This article presents a novel approach to the concept of Self‐Concatenated Convolutional Coding (SECCC) with Sphere Packing (SP) modulation via Differential Space‐Time Spreading‐ (DSTS‐) based smart antennas. The two transmitters provide transmit diversity which is capable of recuperating the signal from the effects of fading, even with a single receiving antenna. The proposed DSTS‐SP SECCC scheme is probed for the Rayleigh fading channel. The SECCC structure is developed using the Recursive Systematic Convolutional (RSC) code with the aid of an interleaver. Interleaving generates randomness in exchange for extrinsic information between the constituent decoders. Iterative decoding is invoked at the receiving side to enhance the output performance by attaining fruitful convergence. The convergence behaviour of the proposed system is investigated using EXtrinsic Information Transfer (EXIT) curves. The performance of the proposed system is ascertained with the H.264 standard video codec. The perceived video quality of DSTS‐SP SECCC is found to be significantly better than that of the DSTS‐SP RSC. To be more precise, the proposed DSTS‐SP SECCC system exhibits an E_b/N₀ gain of 8 dB at the PSNR degradation point of 1 dB, relative to the equivalent rate DSTS‐SP RSC. Similarly, an E_b/N₀ gain of 10 dB exists for the DSTS‐SP SECCC system at 1 dB degradation point when compared with the SECCC scheme dispensing with the DSTS‐SP approach.

The reliable transmission of multimedia information that is coded through highly compression efficient encoders is a challenging task. This article presents the iterative convergence performance of IrRegular Convolutional Codes (IRCCs) with the aid of the multidimensional Sphere Packing (SP) modulation assisted Differential Space Time Spreading Codes (IRCC-SP-DSTS) scheme for the transmission of H.264/Advanced Video Coding (AVC) compressed video coded stream. In this article, three different regular and irregular error protection schemes are presented. In the presented Regular Error Protection (REP) scheme, all of the partitions of the video sequence are regular error protected with a rate of 3/4 IRCC. In Irregular Error Protection scheme-1 (IREP-1) the H.264/AVC partitions are prioritized as A, B & C, respectively. Whereas, in Irregular Error Protection scheme-2 (IREP-2), the H.264/AVC partitions are prioritized as B, A, and C, respectively. The performance of the iterative paradigm of an inner IRCC and outer Rate-1 Precoder is analyzed by the EXtrinsic Information Transfer (EXIT) Chart and the Quality of Experience (QoE) performance of the proposed mechanism is evaluated using the Bit Error Rate (BER) metric and Peak Signal to Noise Ratio (PSNR)-based objective quality metric. More specifically, it is concluded that the proposed IREP-2 scheme exhibits a gain of 1 dB $E_b/N_0$ with reference to the IREP-1 and $E_b/N_0$ gain of 0.6 dB with reference to the REP scheme over the PSNR degradation of 1 dB.

Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.

Registration of multi-modal images is one of the challenging problems in image processing nowadays. In this paper, two novel non-rigid registration models are proposed for multi-modality images. In model 1, mutual information of the template and reference images is used as data fitting term with Gaussian curvature regularization. This approach may not give satisfactory results in noisy images or images having bias field. To overcome this drawback, model 2 is proposed which is based on normalized gradient of both template and reference images as a data fitting term instead of mutual information. To get best transformations, both the models are minimized by using Augmented Lagrangian Method. The proposed models can register multi-modality images without effecting edges and other important fine details and are also tested on various medical images like (T1-T2 MRI, PD weighted-T2 MRI) noisy and synthetic images. The proposed models are also tested on a well known free available Brainweb dataset, where they produced satisfactory results. From experimental results, it can be observed that normalized gradient field based model gives better results than mutual information based model. Comparison is done qualitatively and quantitatively through Jaccard Similarity Coefficient.