Advances in Communication Systems and Networks

Classification of brain tumors using machine learning technology in this era is very relevant for the radiologist to confirm the analysis more accurately and quickly. The challenge lies in identifying the best suitable segmentation and classification algorithm. Active contouring segmentation without edge algorithm can be preferred due to its ability to detect shapeless tumor growth. But the perfectness of segmentation is influenced by the image enhancement techniques that we apply on raw MRI image data. In this work, we analyze different pre-processing algorithms that can be applied for image enhancement before performing the active contour without edge-based segmentation. The accuracy is compared for both linear kernel SVM and KNN classifiers. High accuracy is achieved when image sharpening or contrast stretching algorithm is used for image enhancement. We also analyzed that KNN is more suitable for brain tumor classification than linear SVM when active contouring without edge method of segmentation technique is used. MATLAB R2017b is used as the simulation tool for our analysis.

This paper describes the conductor backed coplanar waveguide (CB-CPW) fed with split ring resonator (SRR) biological planar U-slot antenna for ECG monitoring applications. The antenna is designed through bio-tissue layers like skin, fat, and muscle layers. The designed antenna covers the frequency bands of the industrial scientific medical band (ISM band) 2.36–2.48 GHz and fifth-generation medical service band (5G MSB) 3.4–3.6 GHz. The lowest value return loss is 18 dB at 2.41 GHz and 41 dB at 3.6 GHz for U-slot SRR loaded antenna. The reflection coefficient, voltage standing wave ratio; far-field plots are presented.

The increasing demand for varied wireless applications urges the development of an efficient resource allocation mechanism that provides required rate satisfaction and interference mitigation. Femtocell deployment provides capacity and coverage improvement and service quality to indoor users. However, overlay deployment of femtocells makes resource and interference management a bit challenging. Thus, a review on the resource allocation strategies for heterogeneous macro–femto networks is conducted. A self-organized resource allocation (SO-RA) scheme was implemented to mitigate co-layer interference in the downlink transmission for a macro–femto networks. An analysis on the SO-RA scheme was conducted, considering the cross-layer interference between macrocell and femtocell. A modification to the system was proposed to mitigate the cross-layer interference by adjusting the transmit power of femto base station. The resulting modified scheme and the SO-RA scheme were compared with existing schemes like reuse-1 and simple macro network for various system parameters. The modified SO-RA scheme achieves the best performance among all the four schemes. It results in improved throughput, fairness performance, and interference mitigation compared with the SO-RA scheme.

Music is an integral part of everyone’s life. The task of recognizing a composer by listening to a musical piece is difficult for people with no knowledge of music. Even such a task is difficult for people in musical theory because every day a new music composer is born. To address this problem of classification of a musical tune for a large group of music composers, we construct an automatic system that can distinguish music composers based on their tunes composed. Classification of music based on its composer is very essential for faster retrieval of music files. In this paper, we make a large database that consists of several musical tunes belonging to several composers. We extract MFCC features and train the system using the clustering technique by developing a classification model that can accurately classify a musical tune that belongs to a music composer whose tunes are trained. The overall accuracy of the proposed system is 72.5% without discrete wavelet transform (DWT) and 77.5% with DWT.

For 5G and beyond 5G systems, many technologies such as Carrier Aggregation (CA) have been suggested to improve the Spectral Efficiency (SE). Along with SE, Energy Efficiency (EE) is another key parameter to be considered while designing such systems. Judiciously using available power will not only help in reducing operational costs but also make the system more environment friendly by reducing greenhouse gas emissions. From an operator’s point of view, providing better data rates to users as well as reducing operational costs is important. This paper focuses on the tradeoff between SE and EE for a system operating in the FR1 frequency band (5G) and also using CA. Detailed analysis on the effect of various parameters such as 5G NR numerology, user velocity (Doppler shift), static and signal processing power consumption of base station and power amplifier efficiency at the base station in the tradeoff between SE and EE is performed. From the simulation results, it can be observed that while varying Doppler shift has very little impact, other parameters such as numerology, static and signal processing power consumption and power amplifier efficiency greatly affect the tradeoff between SE and EE. Tradeoff improves for lower subcarrier spacing, lower static and signals processing power consumption and higher power amplifier efficiency. For simulations, a TDL-A channel model is used. A base station power consumption model which takes into account both static and signal processing power consumption and power amplifier efficiency are considered in order to provide a more practical analysis of EE.

Dehazing is an important image restoration technique to remove the presence of Haze from a hazy image. Recent dehazing algorithm is not sufficient to remove Haze from the given outdoor or underwater hazy images. Therefore, an efficient dehazing algorithm is needed for the removal of Haze. Initially, multiple image dehazing methods are used to remove Haze and these dehazing methods have many drawbacks such as, multiple image methods cannot be applied to dynamic scenes and cannot provide results instantly. In order to overcome drawbacks of multiple image dehazing methods, Single image dehazing methods are introduced which are based on some important observations or priors. One such single image dehazing technique is dark channel prior. The thickness of Haze and airlight is estimated using dark channel prior. Guided Filter technique is used to refine the transmission map. But the estimated Haze thickness is inaccurate because of the usage of minimum operator in dark channel prior method. To improve the estimation of Haze thickness, the edge collapse based repair is used after dark channel prior and guided filter technique. This paper presents the time-efficient dehazing of outdoor images with patch size of 25 × 25 and airlight of 3% and this principle is applied to remove Haze in underwater images. The experimental result shows a better result for both outdoor and underwater images.

Apart from being simple and cost-effective compared to other imaging techniques, ultrasound (US) imaging provides a non-invasive, ionization-free approach for diagnosis of tumours. However, US images are inherent with blurring and granular speckle noise which poses as a problem for effective diagnosis. The images also contain intensity inhomogeneities which make detection of tumours (similar to surrounding tissues) difficult. The commonly used segmentation approach is thresholding with Otsu thresholding being mostly used. However, due to intensity of inhomogeneities, normal non-tumour regions are also categorized as tumour regions in standard Otsu method. In this paper, we adapt a new method for thresholding of ultrasound images using phase gradients. Phase gradients have been used in microscopy techniques to enhance the visibility of low contrast structures within the cell body. As a pre-processing step, the image is enhanced using neutrosophic image enhancement and despeckled using shearlets. Neutrosophic approach is used considering the fuzzy nature of ultrasound images. The enhanced despeckled image is thresholded using an adaptive Otsu thresholding where the constraints for thresholding is derived also from texture, gradient, phase and phase gradient apart from mean and between class variance. The performance was evaluated using metrics like SSIM, MSE, SNR and accuracy revealing that the proposed method shows better results than the conventional Otsu thresholding. The proposed method was tested on US images with different echogenecities (hypoechoic, hyperechoic, anechoic, isoechoic) and showed promising results in identifying the tumour regions.

Low power and high speed digital systems are essential for enhancing battery life of portable devices such as smartphones and digital computers. The integral part of any arithmetic and logic unit is adder. When compared to addition, subtraction and multiplication require more hardware resources and processing time. Low power consumption, delay and process variation parameters need to be taken care while designing the integrated circuit. In our proposed work, improved version of Vedic multiplier is designed and implemented by using CSA based on NEDFF. The proposed design offers low power dissipation and high speed. The power and delay results of existing and proposed multipliers are taken by using micro wind tool with technology of 90 nm. The experimental results signify that proposed Vedic multiplier using a CSA based on NEDFF provides 50% improvement in performance.

The 5G networks are in their final stage and are expected to be available soon. Fifth generation has increased the energy consumption in mobile networks in order to meet the demands of users with an increase in carbon footprints. Therefore, energy efficiency in cellular networks is a big concern in order to reduce the overall environmental effects. This paper proposes a green algorithm for energy-efficient networks which encompass D2D transmission initiated between a single transmitter (PT) and many receivers (PR). Three applications are considered: conversational video, conversational voice and text. Optimal power allocation is done in order to meet the target rates of all the demanded applications. The algorithm introduces a parameter known as zone factor for determining the transmission time at each zone for the demanded applications, and this time determines the energy consumed at the user terminal and thus it affects the battery life directly. The efficiency of the green algorithm proposed is validated using MATLAB simulation.

The real-time face detection (FD) algorithm is proposed to find faces in the images as well as videos. Besides face regions, this algorithm also finds the exact localities of the face parts like lips and eyes. Initially, skin pixels are extracted centered on the rules of simple quadratic polynomial model. By introducing small modifications, this polynomial model (PM) could be applied for extracting the lips. The merits of adopting these two identical PMs are two-fold. Firstly, computation time is saved. Secondly, these extraction processes could be executed at the same time on one scan of the video or image frame. Subsequent to skin and lips, the eyes are extorted. Later, the algorithm eliminates the falsely extorted parts by validating with rules taken as of the spatial and geometrical relationships (SGR) of face parts. At last, the exact face regions are ascertained accordingly. As per the experiential outcomes, the proposed algorithm evinces preeminent task in respect of accuracy and speed for FD with huge differences in color, size, shape, expressions, and angles.

The role of segmentation is vital in image processing for the extraction of region of interest. In the perspective of medical images, the region of interest corresponds to anatomical organs or anomalies such as tumor, cyst. This work analyses various algorithms for the analysis of MR brain images. The clustering-based segmentation technique was found to be efficient and for the validation of results, performance metrics like Jaccard Index, rand index, false positive, and false negative are used. The segmentation algorithms are tested on real-time MR brain and brain web database images. The algorithms are developed in Matlab 2010a and the goal of this research work is to guide the researchers for choosing appropriate algorithm for the analysis of MR brain images.

Effective Human Epithelial-2 (HEp-2) cell image classification can encourage the determination of numerous autoimmune system diseases Different computerized image processing methods can be successfully connected to perform HEp-2 segmentation and classification process. This work, a programmed framework for HEp-2 segmentation and classification utilizing image processing ideas is utilized. HEP-2 cell image classification (CIC) utilizing convolutional neural network (CNN) with Gray-Level Co-Occurrence Matrix (GLCM) and Discrete Cosine Transform (DCT) feature extraction (HEP-CIC-CNN-DCT-GLCM) is proposed in this work to improve the presentation of classification precision. Adaptive Gamma Correction (ADC) technique is used as preprocessing technique to improve the differentiation of the cell image for the segmentation process. Median Filtering technique method is utilized to expel noise from the image if any kind of distortion or cracks occurred while acquisition or transmission. Data augmentation is utilized in the training stage to improve the viability of training process. Different arrangement of data is created by pivoting pictures in various points to get more sample images to perform great training. Proposed technique can classify six classes such as Homogeneous, Speckled, Nucleolar, Centromere, Nuclear Membrane, and Golgi. Mean Class Accuracy (MCA) of about 96.56%, which is each a lot higher contrasted with past work related to accessible ICPR 2014 dataset.

The main objective of real-time traffic signal management system (RTSMS) is to save the life of people using cost-effective system. The proposed system is a straightforward and effective operable framework for the real-time embedded emergency vehicle (EV) management applications. In everyday life, there is consistent issue to a crisis vehicle, for example, rescue vehicle, ambulance and fireman to go through traffic light since it was red and it’s exasperating the drive. This circumstance is frequently happening since resistance from a regular citizen, some of the time drivers don’t have an encounter with this circumstance so they will hang tight for the traffic light to turn green. The RTSMS coordinates with the driver and assistants him in a cleared a path by keeping up a vital separation from a deferment in the surge hour gridlock. In this work, a new architecture for the emergency vehicle management system using an embedded system is proposed. This system is used to communicate between EV and traffic signal control unit. The processing time between the signal turn and decision making can be minimized by using the proposed hardware. The proposed work is simulated and tested with real-time hardware with different operating conditions.

Visible light communication provides bandwidth efficiency, secured communication and brightness along with data transmission simultaneously. Optical orthogonal frequency-division multiplexing (optical OFDM) in VLC systems can accomplish high information rate while transmission guarantees high dependability over the multipath fading condition; consequently, it has been embraced as a standard strategy in different communication systems that function as wireless. For decreasing peak-to-average power ratio of optical orthogonal frequency-division multiplexing signals, two partial transmit sequence (PTS) schemes without side information (SI) are proposed. Since recognizable phase alteration is connected to the components of every rotating vector, without transmitting SI, distinguish a rotating vector. The maximum likelihood (ML) detector is utilized to partition SI from the signal that is being obtained at the receiver and recuperate the information data stream. The Euclidean separation between the input information signal constellation and the signal constellation that is being rotated using the phase offsets is being abused by this ML detector. It is researched how to pick good phase offsets for implanting SI, by doing pairwise error probability (PEP) examination.

Methods employed for the automatic detection of valve ailments from the heart records is a skilled task in cardiology. However, automated approaches, proposed for envisaging the cardiovascular diseases greatly depend on the features mined from the heart sound. The analysis of Phonocardiogram (PCG) signals, offers adequate information about the functioning of the heart. Feature extraction techniques in the time domain have analytical simplicity and less computational complexity. In this paper, the effectiveness of the feature namely Wilson amplitude for the detection of murmur from heart signal is investigated and is tested with different threshold (5–50 mV) levels. It is found that the Wilson amplitude at 20 mV threshold is capable to detect the murmur from PCG signal with 88.33% accuracy, 76.67% sensitivity and 100% specificity than other thresholds.

This paper describes the electromagnetic field variance estimation in a random printed circuit board (PCB) model due to the unreliability in permittivity and conductivity of the substrate material using a stochastic finite difference time domain (S-FDTD) method. Traditional FDTD method is updated for mean and variance estimation of the electromagnetic field through the random medium by Taylor series approximation and delta method. The correlation coefficient between the constitutive properties is assumed as to bound the variance field. The 2D S-FDTD method is used for simulating variance of fields in PCB model, and the results are validated using Monte Carlo results.

This paper develops a novel generalized model for the terminal response of a printed circuit board trace, illuminated by an electromagnetic field using a finite difference time domain (FDTD) method. One-dimensional transmission line model has been widely used in time domain simulation, but this model cannot simulate accurately the electromagnetic effect on such structures. Therefore, FDTD is used to study the accurate effect. This desertion develops the transient analysis of PCB by deriving the coupling of the incident field with the geometrical structure using total-field/scattered-field FDTD formulation and then obtained the recursive relation of the electric field and magnetic field for the visualization of the propagation over the problem space. The geometrical structure under study is considered within a perfectly matched layer absorbing boundary grid.

Pseudorandom number generators are used in cryptographic as well as VLSI testing applications. Linear Feedback Shift Registers (LFSR) are circuits that can be used as pseudorandom number generators. This paper proposes a modified reseeding method for LFSR and also presents a design flow for the implementation. The work is done by combining Mentor Graphics HDL Designer FPGA flow and Xilinx Vivado. Different bit lengths of LFSR are generated using Mentor Graphics HDL Designer and synthesized using both Mentor Graphics Precision RTL synthesizer and Xilinx Vivado. The implementation targets Virtex-7 FPGA.

Co-segmentation is a subclass of segmentation used to segment out the common objects by assigning multiple labels to segment the targeted common things. Methods existing so far do not exhibit competitive results for any specific task. The key issue of the proposal selection-based co-segmentation problems lies in mining consistent information shared by the common targets. Due to uncertainty of the shared features, it usually requires manually selecting features or feature learning performed beforehand. The main goal is the comparison of an existing and proposed co-segmentation method. Proposed method (second method) focuses on reduction of running time. For each image, a multi-search strategy extracts target individually. Experiments are orchestrated on public dataset MSRC and iCoseg. Performance evaluation of both the methods is compared.

In space missions, geographically separated TTC stations support data reception between launch vehicle and earth in real time. GSO-based INSAT segment can act as telemetry data relay platform between the host vehicle and the ground station. It has better coverage for supporting TTC requirement. Telemetry via INSAT (TIST) is used to overcome the visibility constraints of conventional TTC ground stations, ship-borne terminals and air-borne terminals, especially in the descent phase. TIST covers events in such scenarios without any hindrance by transmitting data to GSO satellite and receiving it back at ISRO earth stations. It employs digital modulation with channel coding for establishing the link from the launch vehicle to satellite. The satellite transponder upconverts the signal and transmits the information in real time to ISRO ground station. The system was tested in the RLV-TD mission of ISRO and transmitted the critical vehicle parameters in real time in order to evaluate the performance of the vehicle. This scheme is the first of its kind in ISRO launch vehicle, where communication is established between a moving launch vehicle and a single satellite in GSO orbit.

A novel algorithm which can repair video sequence deprived of any artefacts that are present in numerous such prevailing technique is proposed in this paper. Specifically, a video inpainting is proposed that detects moving object in a background where water is falling from a fountain. The input video is converted to frames. Using the Canny edge detection, the edges of the objects in the frames are found out. These edges are classified into different clusters using fuzzy hard C-means algorithm so that inpainting can be done effectively. The classified edges are patch-matched with the most similar pixels. The resultant inpainted video when viewed gives a very good result with minimum time frame.

This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By introducing motor actions in the protocol, the subjects are able to perform actual motor planning, rather than just visualizing the motor movement, thus greatly improving the ease with which the motor movements can be imagined. This study also probes the added advantage of administering somatosensory cues in the subject, as opposed to the conventional auditory/visual cues. These changes in the protocol show promise in terms of the aptness of the spatial filters obtained by the data, on the application of the well-known common spatial pattern (CSP) algorithms. The regions highlighted by the spatial filters are more localized and consistent across the subjects when the protocol is augmented with somatosensory stimuli. Hence, we suggest that this may prove to be a better EEG acquisition protocol for detecting brain activation in response to intended motor commands in (clinically) paralyzed/locked-in patients.

The process of feature selection has a high impact on data mining tasks such as classification and clustering. Removing irrelevant, noisy and redundant data not only increases the quality of the task but also reduces the computational complexity and execution time. Nature-inspired algorithms have tackled the problem of feature selection efficiently. But when applying on a high-dimensional dataset, the metaheuristic algorithms have difficulty to converge. In this paper, an existing artificial bee colony algorithm for feature selection is modified by incorporating a data preprocessing step to reduce the size of the input dataset. The preprocessing step computes the centre of gravity vectors corresponding to the original dataset to form a smaller dataset. The artificial bee colony algorithm works on this smaller dataset for feature selection. The proposed method generates better results with less time and complexity when compared to the existing algorithms.

The development in the presentation of novel microstrip slotted patch antenna geometry by the use of a circular SRR at the rear side of the ground is proposed. This improved performance has been achieved in the terms of significant addition of bands, better reflection coefficient and enhanced bandwidth and gain at the different resonant frequencies in the final stage design. The prototype has been built through the iterations of a novel geometry, introduction of slots in the second stage and finally the insertion of a split-ring resonator of circular shape at the rear side of the dielectric substrate in the third stage. Coaxial feeding technique has been employed to excite the structure at its fundamental frequency of operation. The slots embedded into the patch structure and the modified ground geometry not only scale the number of resonant frequencies, the corresponding gain and bandwidth but also provide for impedance matching at the different sideband frequencies of resonance. The anticipated prototype possibly will be used for radiolocation purpose at the design frequency of 2.73 GHz and also at the sideband frequencies of 9.11 and 9.32 GHz, fixed satellite at 4.47 and 8.81 GHz, mobile applications except aeronautical mobile at 5.67 and 7.81 GHz and for fixed mobile applications at 7.1 GHz. The final two design stages have been fabricated, and an untried verification has naked their performance to be in close approximation to the simulated ones.

Individuals who are totally visually impaired or have weakened vision have a troublesome time exploring outside the spaces that they are acquainted with. Truth be told, physical adaptation is one of the greatest difficulties for visually impaired individuals. Voyaging or simply moving down a busy road can be challenging. Due to this, numerous individuals with low vision will like to go with a companion or relative while exploring new places. Likewise, blind individuals must remember the objects in their home condition. Items like beds, tables, and seats must not be moved without notice to avoid mishaps. In the event that a visually impaired individual lives with others, every individual from the family unit must be steadily about keeping walkways clear and all things in their assigned locations. This project addresses an assistive technology, designed and developed to facilitate conversion of the real-world scenes to voice. The system contains cameras. The view at their front is captured by the camera. This image captured by the camera is given to You Only Look Once (YOLO) to identify objects and then the description is generated using long short-term memory (LSTM) and Inception V3 model. It is converted to audio. This audio is output through the speakers. The created framework is viewed as a stage forward toward the progressions in Electronic Travel Aids, and should add to the improvement of the life of people with vision misfortune.

A $$1\times 3$$ linear Koch Snowflake antenna array is proposed for multi-band applications. The Koch array has a gain of 1.2 dB, 4.38 dB, and 5.22 dB at 5.4 GHz, 7.7 GHz, and 9.8 GHz, respectively. The number of resonant frequency of the Koch fractal increases as the number of iteration increases. The design and simulation of the Koch antenna were done in AnSoft HFSS and MATLAB. The antenna parameters, such as peak gain, bandwidth, and return loss, are analysed and presented for different iterations. The return loss obtained for all Koch antennas studied is well below −10 dB which shows good radiation characteristics. The third iteration Koch fractal antenna was fabricated using FR4 substrate and tested using Vector Network Analyser. The good agreement between the simulated and measured values validates the proposed design and satisfies the requirements for multi-band applications. Based on these results, the presented fractal-based patch antenna system can be considered as a best candidate for multi-band wireless systems.

Image analysis is one of the fastest-growing research areas, and within it, light field or plenoptic image processing has gained great popularity in the recent years. Plenoptic images have got the inbuilt capability of imaging an object at slightly variant viewing angles which opens up possibilities for a deeper image analysis. A light field image is basically constituted with an array of sub-aperture images that have been captured using an array of lenses. Segmenting an image into its constituent parts of interest is very crucial in an image analysis point of view. Its efficiency influences the subsequent image processing stages. Various segmentation algorithms for conventional images have been developed over the recent years. This paper analyses the field of light field imaging in terms of its acquisition and discusses the various application areas and challenges pertaining to it. It also throws light on some of the research possibilities in this field. Finally, few segmentation algorithms are analysed on how it works when applied to light field all-focus images. The segmentation parameters like jaccard, dice, sensitivity, accuracy and specificity are being analysed here. From among the available sub-aperture images, the central view all-focus image has been chosen to evaluate the segmentation results. The segmentation is being evaluated on light field saliency dataset (LFSD) which has the ground truth data of segmentation corresponding to all-focus light field images.

Intelligent surveillance is an emerging research area in the field of computer vision. Person re-identification is one among the tools involved in intelligent surveillance. Person re-identification is used to recognize and identify a person of interest captured by different surveillance cameras at different times and at different locations, when an input image is given. Automation of person re-identification is difficult in real time due to changes in pose, background, illumination and occlusion. Recent researchers have focused on developing discriminant, robust features, learning distance metric models or fusion of both for matching between the images of person. Our main objective is to provide the future researchers the importance of various state-of-the-art feature extraction techniques and deep learning approaches used in person re-identification, till date. Different algorithms with their strengths and accuracy percentage were summarized in a comparison table. Finally, unsolved problems in person re-id were listed that can be used as guidelines for future research.

Emotional computing has played a crucial role in acting as an interface between humans and machines. Speech based emotion recognition system is difficult to be implemented because of the dataset which is containing a limited number of speeches. In this work, multi speaker independent emotion recognition encompasses the use of perceptual features with filters spaced in BARK scale and Equivalent rectangular bandwidth (ERB) and vector quantization (VQ) for classifying groups and convolutional neural network with backpropagation algorithm for emotion classification in a group. The proposed system has provided consistently better accuracy for the perceptual feature with critical band analysis done in ERB scale with overall accuracy as 86% and decision level fusion classification yielded 100% accuracy for all emotions. Speaker dependent emotion recognition system has provided 100% as accuracy for all the emotions for ERB-PLPC features and perceptual linear predictive cepstrum has given 100% as accuracy for all emotions except sad emotion.

Analysis of building stability is an essential measurement process for all buildings in cities for achieving safety and serviceability. This is achieved by measuring the vibration of the building, and also, an alert is created to save the life of people who live in very tall building. It is proposed to design a stability analyzer combined with earthquake detection alarm. When earthquake is detected, the controller checks the vibration level with threshold level. If the vibration input exceeds threshold level, the controller triggers alarm and sends voice message through GSM. In the proposed paper, the MEMS pressure sensor (BMP180) is used to sense the vibration. In addition to sending data to monitoring section, it automatically opens all doors and sends data to nearby rescue station and emergency warning call to evacuate the people from high-rise building. The vibration of the building is recorded continuously for the proper maintenance and service.

Single carrier frequency division multiple access (SC-FDMA) is the standardized technology used in the Third Generation Partnership Project (3GPP) used for implementing LTE-based uplink connection. It is gaining increased popularity as the preferred technique because of its better spectral efficiency. It also has an outstanding feature of reduced PAPR because of its single carrier design. An extensive study of modern modulation techniques such as SC-FDMA has played a vital role in the design of future networks and systems. This paper analyzes the effects of selective mapping (SLM) technique on PAPR lowering of the localized frequency domain multiple access (LFDMA) network which is a kind of SC-FDMA system by means of subcarrier mapping techniques. A multistage selective mapping method is used here to bring down the PAPR of the LFDMA system. This method achieves a better reduction in PAPR than that of the traditional SLM system. The behavior of the proposed selective mapping-based LFDMA network can be obtained through finding complementary cumulative distribution function (CCDF) for PAPR characteristics. The BER feature of the presented LFDMA system is better compared to the ones reported so far.

In this paper, a coplanar waveguide (CPW)-fed antenna is presented for medical anatomy region grid. The proposed antenna is operating at 2.45 GHz ISM band frequency, and it is simulated using IE3D simulator version 15. When it works close to human tissue, large specific absorption rate will occur. Based on flexible substrate and miniaturization, SAR value is decreased up to 95%. The results of proposed antenna produce a band from 2.4 to 2.48 GHz. The simulations are fulfilled in a body for validation. The antenna proposes a fine accordance between the simulated and analysis results to examine the range of the data transmission.

A reduced planar omnidirectional MIMO antenna accompanied by pattern phase variety is to be discussed in this paper. Because of the restricted bandwidth of the perpendicular-polarized element, the shape of usual MIMO antennas with two polarizations has extremely occurred in two-dimensional integration. Hence, for planar integration as for omnidirectional MIMO antenna, a two-slot parallel-polarized antenna is introduced. This antenna is made up of folded dipole array with four elements, and to feed the two channels in the company of shared elements, a close packed feed network having a pair of 90° continuous phases is hired, so this integration of antenna can be done in a sheet of substance with 0.8 mm ultra-low profile. Even though the polarization and radiation diagram of both the channels located adjacent to each other are similar to one another, as a consequence of unrelated pattern phase, an advanced isolation is yet achieved. So, a possible solution for planar integration of the system is done by the proposal of dual-channel antenna.

In this paper, a novel slot antenna has simple structure and small size designed for medical wearable applications. Good insulation is used as the substrates for flexible antenna, and it can be proposing for the medical applications and military purposes. When relative permittivity values are going to be deceased to low range, this is to activate performance of the antenna. The flexibility nature of the structure is the interaction between the gaps which make the antenna performing the radiation effectively. This antenna is health monitoring devices for human and animals. The proposed antenna consists of slot radiation elements fed by a coplanar waveguide (CPW) structure and a floating ground. The impedance matching bandwidth of the antenna is from 3.2 to 3.6 GHz, which can cover the 3.4 GHz band and perform good radiation characteristics.

Sentiment analysis has become an important opinion mining technique; in recent years, it becomes one of the most interesting fields in artificial intelligence. Pre-processing is considered as a significant stage in sentiment analysis, but it is not given much attention in the literature or models. The data which are collected from different sources might contain redundant and duplicates; it needs to undergo some detection process for any occurrence of redundancy in the datasets. This paper reviews, analyzes, and compares different data cleaning algorithms such as DySNI, PSNM, and brushing for identifying redundancy in the datasets. Further, it analyzed the effects of general data cleaning methods to enhance accuracy when it is applied to different classifiers. The result reveals that the DySNI algorithm gives the highest accuracy and the brushing algorithm (BAA-DD) helps to reduce the dataset size to a greater extent. Further, applying negation replacement and acronym expansion techniques helps to enhance the accuracy level.

Networking technology in the present scenario has the latest invention called ad hoc networks. VANET is a type of ad hoc network where vehicles communicate with each other in a network. From the last few decades, VANET is becoming popular where relocation, mobility, and converge of location by the vehicles were impossible to achieve by using a wired network. VANET provides the growth of intelligent transport systems by providing safety and comfort consideration for both drivers and passengers. VANET is an open-access network that may prone to various types of attacks. This paper is an overview of the study of various attacks in VANET focusing on Sybil attack in VANET which operates under the urban environment. A comparison of different methods of detection technique for Sybil attack is also included.

A compact (25 × 30 × 1.6 mm3) coplanar antenna suitable to operate on both ultra-wideband (UWB) and global system for mobile communications (GSM) is presented. A coplanar waveguide (CPW)-fed antenna for 3.1–10.6 GHz (UWB) along with 1.7–1.8 GHz (GSM) will be useful for high-speed data transfer applications in embedded communication systems. Vital characteristics like radiation pattern, return loss, and bandwidth of the proposed antenna are investigated.

Two critical aspects of automation of test systems are the ability to operate different test instruments through a controller and their seamless integration. Instrument interchangeability is a key factor in test automation as it can make automation easier and overcome the obsolescence of instruments, thereby extending the overall lifetime of an automatic test equipment (ATE). This becomes more important in fields such as aerospace and nuclear systems where the test systems and test programs have operational lifetimes covering several generations of test instrumentation. Across the generations, a feature that changes in the instrumentation side is the instrument interface. Various instrument interfaces are developed such as RS232, GPIB and in recent times USB, Ethernet and so on. This many a time necessitates the need to ensure compatibility with the interface available at the controller side. A novel product called matrix adaptor is proposed here to overcome this limitation and guarantee an interface independent communication between the controller and test equipment. The proposed product can be used to adapt any interface on the test instrument side to any other on the controller side, and novelty of the idea is that the same product can be used for achieving more than one type of interface conversion at the same time. This work elaborates on the design considerations for such a product and gives the implementation details of a proto model with USB, RS232 and GPIB interfaces.

Computers have become more and more potent over the years, and consequently, it has become more comfortable and easier to break encryption and hashing algorithms. Hence, it is essential to study the performance of these algorithms and analyze them, and possibly come up with better algorithms based on the knowledge we have gained from the analysis. In this paper, a comprehensive literature study of the conventional encryption and hashing algorithms is done, followed by a practical comparison of the time-efficiency and CPU usage of these algorithms, two important performance evaluation parameters.

In this paper, the study on fruits and vegetables grading system by using image processing techniques with various machine learning algorithms is presented. In our study, we observed that the images of fruits are used for analyzing about fruits. The parameters like shape, size and color of the samples are extracted for grading the fruits’ quality. Some of the research has done on fruit disease classification. Different techniques like color-based segmentation, artificial neural networks and different classifiers can be used to classify the grade or diseases of the sample. This review work is to provide a study of some machine learning techniques and morphological features with color-based grading for fruits and vegetables. The authors have proposed a hybrid system of fruit grading and disease detection system.

Facial expression detection is a well-studied domain in which facial features are extracted and then classified into six common expressions. One of the most common techniques used for extracting features is the Gabor filter. In literature, for extracting the features, the combined magnitude and phase values of the Gabor filter are used. This paper is exploring the performance of methods using the combined filtering method, using magnitude alone and using phase alone in the domain of facial expression detection. It is observed that considering phase values with the support vector machine classifier yielded an additional 8% accuracy when compared to combined methods.

Datasets may contain small sets of data objects whose characteristics are not in accordance with the mainstream characteristics of the data objects in a dataset. These data objects, which are not noise, may contain valuable information and are called outliers. Outlier detection is a topic of research in many fields like detecting malwares in cyber security, finding fake financial transactions, identifying defects in industrial products, detecting abnormality in health data, etc. Researchers have developed several application methods for detecting outliers and a few generic methods. These methods can be grouped into unsupervised methods, supervised methods and semi-supervised methods based on the readiness of class labels. We, in this paper, present the performance of three outlier detection algorithms using the realworld datasets. The algorithms used are one-class SVM, elliptic envelope and local outlier factor. In order to improve the performance, all these algorithms were selected and ensemble based on voting mechanism. The influence of dimensionality reduction on the proposed ensemble method has also been studied. Experiments using publicly available datasets show that the proposed technique outperforms individual outlier detectors.

A compact 25 × 30 × 1.6 mm3 coplanar antenna suitable to operate on both ultra-wideband (UWB) and industrial, scientific and medical bands (ISM) is presented. A coplanar waveguide (CPW)-fed antenna for 3.1–10.6 GHz (UWB) along with 2.4–2.48 GHz (ISM) will be useful for high-speed data transfer applications in embedded communication systems. The proposed antenna is fabricated on FR-4 epoxy substrate, and the experimental results are reported. Vital characteristics like radiation pattern, return loss and bandwidth have been investigated.

Mobile cloud-deployed mobile sensing networks are a growing area of technology where attaining energy utilization is a challenging task during data transmission from mobile sensor devices to the cellular base station. Data offload can address drawbacks like network delay, poor performance, and high-energy consumption. Such a setup requires an efficient scheme that focuses on energy efficiency in a better way reducing the faster death of nodes. In this paper, an energy-aware approach named modified partitioning around medoids with cluster head selection (MPAM-CHS) is proposed, that aims for better clustering of mobile devices and the fairer selection of group head to minimize the energy utilization of the nodes. The proposed scheme consists of four phases like initialization, clustering, cluster head formation, and transmission phase. Initially, the nodes are randomly deployed in the network field and then clustering is performed on them using a modified PAM algorithm to determine the actual cluster points for partitioning the nodes into small groups. Next, the cluster head (CH) or the group head is determined based on the criteria such as residual energy, signal-to-noise ratio (SNR), path loss, and average path loss between the sensor and the sink. Finally, the sensed information collected from the nodes is offloaded to the group head, aggregated, and then sent to the sink. The experimental analysis shows that the proposed algorithm has a significant gain in energy consumption in terms of network utilization and lifetime metrics.

The multi-input multi-output (MIMO) terminal characteristics are used for the high examined element of dual-band MIMO antenna with the pattern diversity that has been proposed. The MIMO terminal antenna is having two similar monopole components. By calculating the envelope related to each other coefficient (ECC) and radiation patterns, the MIMO terminal pattern is checked. This antenna exhibits good efficiency and gain. The outcomes obtained for the designed antenna are very perfect.

Wireless sensor networks are important developments in remote sensing applications. Supplying power to these systems is invincible one, and replacing batteries all the time is inefficient and not an appropriate solution. Piezoelectric materials convert mechanical energy into electrical energy from the external vibration of the environment through its direct piezoelectric effect. In this paper, rectangular with and without hole based unimorph piezoelectric cantilever geometries are proposed using finite element method simulation and analysis developed. The outcomes display that the rectangle with hole piezoelectric structure is having a lower resonant frequency and harvests more energy compared to other structures.

Libraries are constantly looking for a solution to deliver fast and efficient information services and workflows, which brings a high level of satisfaction for both patrons and library staff. The library needs to constantly evolve with rapid changes in the field of information technology. As a result, digital library services are taking over the traditional library services. In this context, the radio frequency identification technology (RFID) is used for smooth circulation management in the library besides ensuring the security of the books. Also, the application of RFID in the library field will save the time of patrons and library staff. RFID technology helps libraries to improve user satisfaction with a self-service experience and increases staff efficiencies by augmenting several value-added services. This paper highlights the comprehensive application of RFID technology in the library domain with a case study of migrating RFID infrastructure implemented at the Scientific Information Resource Division (SIRD), Indira Gandhi Centre for Atomic Research (IGCAR) at Kalpakkam. The various challenges faced while implementing the technology and the impact of RFID technology in the library are highlighted.

Personal health record system allows patient to share their health information in a remote location. Security of the data is an important criterion to be considered while outing the facts in the remote servers. Encryption is the common technique to provide the security for the data before outsourcing. This paper focus on multiauthority attribute-based encryption (MAABE) approaches, by compressing the least value attributes. In all the attribute-based encryption, the attributes are used to encode and decode the data. As the quantity of attributes used increases, the calculation time will be increased for the performance. So, in order to increase the system efficiency, least value attribute details should be compressed before encoding and decoding the information. Also, a binary tree structure is used to administer the attributes in various authority levels of the algorithm. This binary tree structure helps to decrease the encoding and decoding timing of the algorithm.

The increased use of technology had an impact to the overall wellbeing. Health experts have increasingly taken advantage of the benefits of these technologies thus generating a scalable improvement in the area of health care. Because of this, there is paradigm shift from manual monitoring toward more accurate virtual monitoring with minimum percentage of error in the area of health care. Advances in artificial intelligence (AI) led to exciting solutions with good accuracy for medical imaging and is a key method for future applications in health care. Brain tumor detection is an important task in medical image processing. Early detection of brain tumors plays an important role in improving treatment possibilities and thus increasing the survival rate of the patients. Manual detection of the brain tumors for cancer diagnosis from a large amount of MRI images generated in clinical routines is a difficult and time consuming task due to the complexity and variance of tumors and medical data. So, there is a need for automatic brain tumor detection from brain MRI images. With the help of deep learning networks, we can automate the detection process. For that, we have proposed a new network known as BrainNET which reads the MRI images coming from the MRI machine, and then, it detects as well as classifies the brain tumor if present.

Inversion is a significant operation in ECC processors and is the most complex and time-consuming operation among operations like addition, subtraction and multiplication. Thus, proposing an algorithm and designing its architecture to compute inverse with minimum number of clock cycles are mandatory. In this brief, high-speed inversion of NIST recommended pentanomial $$\text {GF}(2^{163})$$, based on traditional Itoh–Tsujii inversion algorithm (ITIA) is proposed. This proposed inversion algorithm is then implemented on FPGA Virtex-5 platforms to analyze its performance. This design minimizes the latency and, thereby, improves speed. The developed high-speed Itoh–Tsujii inversion algorithm HS-ITIA computes inversions in 18 clock cycles with maximum clock frequency 64.5 MHz, which thereby yields a rise in performance by 56%.

In this paper, CPW-fed slotted flexible antenna has been presented for 5G mobile applications. The element of the bent antenna topology is analyzed to reduce the physical footprint, and a simple inspection with the terminals of a mobile is operated in the frequency band of 4.1 GHz. Ratio in the middle of the front and back of the bent antenna shows a high specific mapping up to the rate of a ratio of 1 dB. A compact wideband reflector is reported with a direct aspect ±90° and transmission <−25 dB. TThe broadband reflector is combined with a region twister over an antenna with 0.135λ long distance from the emitting design. The region twisted receiver is utilized in 4.1 GHz band combined with reflector, with a ratio in the middle of the front and back greater than 14 dB over the frequency and an onward gain of 5.5 dBi.

Due to development in wireless communication, advanced technology is necessary to meet the growing demands in this field. Since the number of users increases, spectrum resources have to be utilized in a planned and effective manner. Cognitive Radio paves the way for proper spectrum utilization. Balancing energy consumption in Cognitive Radio can be obtained by clustering methods. In this paper, cluster-based cooperative sensing is analysed. Energy-conserving cluster method with distance criteria is proposed, where cluster heads are selected based on their distance from primary user and fusion center, and cluster members are grouped to the nearest cluster head, thereby reducing reporting energy brings conservation in overall energy. Also a relay assistance approach is proposed where relay users placed between cluster head and fusion center assist cluster head in times of low energy. An energy consumption analysis is made to compare the proposed method with conventional methods. Simulations are performed using MATLAB R2016a software, and it is observed that the proposed method conserves energy and improves detection.

Toll quality speech codec design with a low bit rate is really a challenging task in modern communication because of the drastic increase in end-users in social networks. Most of the low bit rate speech codecs are based on linear prediction. The code-excited linear prediction codec (CELP) gives good quality decoded speech at a lower bit rate of 4.8 Kbps. But, it neglects the natural nonlinear effects present in speech production process. So, some adaptive techniques are to be used to make the system nonlinear to perform better than linear prediction speech codecs. An adaptive technique with nonlinear prediction of speech, based on truncated Volterra series, is used to generate the nonlinear prediction coefficients. The generated nonlinear prediction coefficients are implemented in G723.1 CELP codec to introduce code-excited nonlinear prediction (CENLP) codec. Advancements in the performance are evaluated using subjective and objective quality measures and compared with the normal G723.1 CELP codec.

Multimedia data processing has been gaining popularity due to its increasing demand in today’s scenario. In this paper, we discuss the various scheduling strategies that have been employed for multimedia processes. We study the performance of traditional scheduling algorithms such as First Come First Servce (FCFS) and Shortest Job First (SJF) for multimedia tasks and analyse the variation in their performance under different scenarios such as different sets of data and by varying the number of processes. Experimental results prove that the scheduling schemes vary in their performances under different situations.

Face recognition is one of the most sought-after biometric technologies in the field of machine learning and computer vision in recent years. Histogram of oriented gradients (HOG) descriptor was originally developed for human detection and recently, it is also being applied to face recognition. However, when compared with other successful feature descriptors such as SIFT, LBP, Gabor, and so on, there is still considerable research space on the application of HOG features for face recognition. Co-HOG, a variant of HOG, uses a pair of gradient orientations as its basic building block, unlike HOG which uses a single gradient. Using the pair of orientations, the co-occurrence matrix is computed and histograms are calculated. However, in Co-HOG, gradient direction alone is considered and magnitude is ignored. It is believed that gradient magnitude also carries significant information about features. In this paper, we develop a face recognition system that utilizes Co-HOG features with embedded gradient magnitude information. The experimentation is done on ORL face dataset and it is observed that the proposed model is better than other existing methods with a maximum accuracy value of 97%.

Non-destructive testing (NDT) is the process of analyzing the materials, components, structures, etc. without causing damage to it. In this paper, a NDT technique is proposed for detecting the cracks in the concrete surfaces. Initial results are obtained using a neural network for concrete crack detection. A convolution neural network model has been developed and trained using both positive (crack images) and negative (non-crack) images. In this work, a database consisting of 40,000 images is used. The model is trained with 36,000 images, 4000 for validation and 4000 for testing. To evaluate the effectiveness of model, accuracy, recall, precision and F1 score parameters are calculated.

Computer-aided systems are gaining interest in every field over the world. The medical field has also enhanced to a great extent with the use of computer-aided diagnosis. Among the different diseases in the era, breast cancer has shown a rapid hike in the number of deaths. Scoring of nuclear atypia is an efficient method for the prognosis of breast cancer. The biopsy samples taken from the suspicious tissues are analysed under microscope by the pathologists and are graded. But manual grading highly depends on the pathologists and can cause variation in the results. Hence, the requirement of computer-aided systems for grading has increased. Many studies related to nuclear atypia scoring have taken place in the literature based on different algorithms and classifiers. This paper gives an overview of the different studies in the literature, related to nuclear atypia scoring. Various techniques are used for nuclear atypia scoring. Multifarious image processing techniques are used for this. The aim of this study is to analyse these techniques and their results and know the most efficient one from them. Our analysis shows that promising results are achieved by machine learning techniques. Scores obtained using these techniques are comparable to manual grading.

Surveillance cameras have become commonplace in urban scenarios. These cameras are currently proving to be a valuable aid in apprehending criminals after the camera feed is analyzed. Their utility can be further enhanced if detection capabilities are provided, via additional hardware for realizing specific functionality. Currently, Deep Neural Networks are the most popular set of tools for image classification and have also been successfully adapted for other modalities such as video, speech and ECG signals. However, many of these Deep Nets are designed for very complex multi-class problems and hence do not allow for real-time functionality on an embedded platform, due to the large number of parameters involved during the classification task. In this work, we use a modified version of an existing dataset that can be easily trained with published networks to give reasonably good detection accuracy. It is shown via experiments that even smaller nets perform as well as state-of-the-art networks and provide the same level of detection accuracy as the more complex networks. Hence, the simpler networks can be used for a detection task by downloading it to an embedded system and allowing for classification to happen in real time.

Abnormality and presence of neurological brain disorder such as epileptic seizure is diagnosed by analyzing electroencephalography signals accurately. The acquired brain signals are analyzed in time–frequency domains by using wavelet for accurate diagnosis. The online standard EEG database signal is preprocessed to remove power noise and most important eye blink artifact using independent component analysis. Daubechies wavelet is implemented, and decomposition of frequency is carried out up to eight levels. The exact sub-band of frequencies are extracted from band of frequencies which are called as delta band, theta band, alpha band, beta band and gamma band from lower to higher. Suitable features such as Lacunarity, Fluctuation Index, Energy and Entropy, Kolmogorov Entropy, Kurtosis and Skewness are extracted and classified using K-Nearest Neighbor, Support Vector Machine and Probabilistic Neural Network. Performance analysis is carried out by measuring specificity, sensitivity, accuracy and true predictive value. Implementation of independent component analysis to the affected channels in preprocessing block to remove eye blink artifact, decomposition of brain signal using Daubechies wavelet up to eight levels provides accurate diagnosis of epileptic seizure. The proposed seizure detection system provides high metric of performance parameters.

Due to the world’s rapid economic growth, the motor vehicles’ number has significantly increased which lead to the necessity for the security of vehicles. It becomes difficult to manually track the vehicles which are over speeding and to monitor the traffic so as to avoid congestion problems, tracking stolen cars, detecting the unlawful activities that involve a vehicle. Automatic number plate recognition (ANPR) is an image processing technology which allows users to track, identify, and monitor moving vehicles by automatically detecting the license plate from the vehicle. A convolutional neural network (CNN) is used which slides over the entire image so as to detect the characters from an image. After detecting the characters, the non-plate region must be eliminated from plate region. For that a plate/non-plate CNN classifier is used. After detecting the license plate, LSTM is being used for obtaining the feature sequence. Then the characters are decoded, and the license plate is recognized. By combining both CNN and LSTM methods, it gave an accuracy of 85.0%.

The availability of the most advanced image correction tools along with the latest highly classy capturing devices has made the tampering of the images more easier. Copy move method of tampering happens when a particular section in the digital image is copy pasted on a new section on the same image for guarding or else hiding offensive areas. Digital forgery detection methods on images target the discovery of fake regions or the duplicated parts. Many pre- or post-operations are done on the images by persons who do the tampering. A novel method integrating the block-based and key-point-based methods is suggested in this paper. Initially, it adaptively segments the input host picture into non-overlapping and unequal sized slabs and features and thus mined from each block is harmonized with each further to discover labeled feature points that point out doubted fake regions approximately. In order to make it further precise, an extraction algorithm is proposed that substitutes the feature points by minor superpixels as slabs of feature which later fuses with the adjacent blocks which show like local color features to create that detected forgery sections. Lastly, morphological operations are performed on these fused areas to produce the spotted tampered areas. Experiments show that this method of detection achieves better outputs under several perplexing conditions compared with the latest CMFD algorithms.

The problem of video surveillance toward behavior tracking has been well studied. The object tracking in video surveillance and behavior analysis has been used for different problems. Numerous techniques have been presented earlier for the tracking of objects and classification of behaviors. The general approaches use a background model to identify the foreground objects. Based on the foreground objects identified, the object tracking has been performed. Similarly, various approaches consider different features to perform object detection and classification. Each method produces a different result with varying accuracy in behavior tracking. This paper analyses various methods of object tracking and behavior analysis in video surveillance. A detailed survey on the methods of object tracking is performed, and a comparative study is presented in this paper.

Recently, wearable antennas are become popular due to the miniaturization of wireless devices. The main advantage of using wearable antennas is that they are part of clothing and easily transmit and receive signals through clothes. These antennas play a vital role in many applications like navigation, tracking, health monitoring, physical training, RFID, medicine, and military. This paper describes the design and development of microstrip patch antenna with different fabric materials as substrates for ISM band applications. The performance characteristics like gain, directivity, return loss, VSWR, and radiation characteristics are simulated and measured by using CST.

With the emergence of IoT, the number of devices that is getting connected is increasing exponentially, which poses a constraint on the lower latency requirements of processing these tasks. Evolving technologies such as fog computing and edge computing consist of computationally lesser exhaustive power servers, which bring this processing near the edge or onto the devices, thereby reducing the round-trip delay as well as the load on the entire network. In this work, we propose the hierarchical arrangement of servers in the fog computing layer for this processing of data and derive the analytical framework for the same. The proposed architecture works on top-down scheduling policy rule for the incoming packets, defined mathematically in terms of two-dimensional Markov chains. The performance of the proposed architecture is compared with an equivalent flat architecture analytically in terms of the mean sojourn time and the mean computational power and justified using simulation results.

The cognitive radio technology is considered as an effective method for alleviating the dilemma of spectrum scarcity by creating spectrum access opportunities for the secondary users. Future cognitive devices require advanced sensing techniques for rapid and active identification of spectrum holes over a wideband. Incorporation of prior data from the geolocation database enhances the sensing efficacy, reduces the computation complexity and maximizes the spectrum hole detection capability for cognitive devices. However, unlike the TV white space database, other wideband spectrum databases are not yet available. Therefore, for dynamic and fragile cognitive networks, the geolocation database cannot assist the spectrum sensing process, which necessitated database-independent real-time spectrum sensing. In this paper, the performance of modulated wideband converter-based wideband spectrum sensing is analysed using prior information from geolocation database.

The increasing global focus on health protection issues draws attention to the importance of point-of-care (POC) technologies. Its ability to provide cost-effective solutions for health maintenance points out their importance. However, against the obvious benefits, POC does not provide any primary diagnosis at the patient side. So, this work aims at a primary diagnosis of cardiac diseases at patient side itself by detecting the electrocardiogram (ECG) abnormality. For perfect diagnosis, confidential data of patients are also required. However, transmission of this confidential data through public network may cause many security concerns. Also, wide privacy problems may occur as private data may be revealed to illicit servers. This paper uses support vector machine (SVM)-based technique for detecting abnormality in ECG signals. And Fast Walsh–Hadamard transform-based steganographic method is used for providing privacy, security and confidentiality of the transferred data.

Design and analysis of multiband antenna for WiMAX and WLAN are presented. The designed antenna comprises of a triangular patch and six circular patches that are connected with the triangular patch using rectangular striplines which lies on the top plane of the loaded FR4 substrate. A defected ground structure is developed in this design to get isolation between WiMAX bands. A parametric study on the radius of circular patches and lengths of the rectangular microstriplines has been carried out on the designed antenna to afford all the crucial WiMAX operative bands (2.5/3.5/5.5 GHz). The designed antenna is compact (23 × 27 × 0.8 mm3) when compared with the previously proposed multiband antennas. The simulated and measured responses show that the antenna is proficient to operate among the 2.3–2.4, 2.5–2.69, 3.3–3.8, 4–4.7, and 5.425–5.850 GHz frequency bands. The design parameters have been analyzed and measured for validation.

Speech synthesis systems aim at generating high-quality, natural-sounding speech. Synthesizers become more static due to the inaccessibility of large databases for Indian languages. The prominence of the artificial speech made by these synthesizers is poor. Though statistical-based technique on hidden Markov models (HMMs) and Gaussian mixture model (GMM) is a powerful technique in Text to Speech (TTs) synthesis, recent work in TTS has concentrated on support vector machine (SVM). In this paper, a TTS system is developed for Tamil language using SVM. By using SVM, better sensitivity measures for Tamil Text to Speech are obtained. Output portrays that SVM can produce effectively natural speech compared to HMM and GMM.

One of the fundamental operations in computer algebra is the multiplication of multi-precision numbers. Some algorithms are available to implement these operations. Naive algorithms like high school mathematics (Neugebauer in the exact sciences in antiquity. Courier Corporation, Chelmsford, 1969) provide simple and good results for smaller numbers, while complex algorithms like FFT provide better results for larger numbers. Almost all algorithms are application-specific. We propose a novel and generic algorithm which is fast and suitable for cryptographic applications.

Tuberculosis (TB) is the deadliest, communicable disease and can nearly affect all the parts of our body. To diagnose this disease, there are various tests available such as chest X-rays and TB skin tests. The most commonly used tool for TB detection is the sputum smear microscopy (SSM) which is less costly. The specimens are stained using Ziehl–Neelsen and are then examined by the technicians for any microbes. Detection of bacilli from stained sputum images manually is a lengthy, far-reaching process which can lead to inaccuracy in the output. Therefore, automatic methods are provided which give an optimal solution in a short time, in the absence of skilled experts in disease diagnosis. This paper gives an overview of available preprocessing methods in various digital images and hence will benefit the researchers working in the smear microscopy field.

Our country is facing a lot of challenges in clearing the wastes and reducing the pollution. The wastes are created by so many sources and dumped as a landfill in almost all towns and cities and major source of pollution. Out of the total wastes, 20% is from the household kitchen wastes. This is due to the contradiction between high food consumption and low food recycling rate. The food waste or kitchen waste from the house is treated and decomposed properly, to reduce food waste capacity and converting as organic manure. But the customary composting of waste takes six months to a year to decompose. It also requires regular maintenance for the compost pile, and collecting scraps to take outside can be smelly and attack bugs. This project gives an innovative solution to recycle the food or kitchen waste from each house. The solution is to design an eco-friendly compact bin placed in every kitchen of the house that converts food waste to organic fertilizer. Unlike most composting device which dehydrates the scrap is actually a huge technical challenge to provide consistent quality of organic fertilizer every time by this novel method. The organic manure from this recycler bin improves the soil health, reduces the need for additional water and fertilizers. In order to fasten the composting process, the recycler bin is constructed with heating, mixing and grinding sections under controlled conditions.

Image denoising is a crucial pre-processing step on images to restore the original image by suppressing the associated noise. This paper extends the performance study of the denoising convolutional neural network (DnCNN) architecture on images having the Gaussian noise. The DnCNN is an efficient deep learning model to estimate a residual image from the input image with the Gaussian noise. The underlying noise-free image can be estimated as the difference between the noisy image and the residue image. In this paper, we analyse the performance of DnCNN with data augmentation, batch normalisation and dropout. The experiments are conducted on the Berkeley natural image dataset, and quantitative and qualitative study has been performed. The comparison of the experimental results demonstrates that the DnCNN model converges at a faster rate and works well with a smaller dataset.