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Since the invention of ultrasound, it has become the most popular and widespread method in obstetric care to establish fetal gestational age (GA), to monitor the growth and to predict the adverse pregnancy outcome. Nowadays, the information of placental details (location, size, volume, morphology) has become the greatest interest to the obstetrician. Assessment of in utero placenta by ultrasound along with other fetal biometry can provide more details regarding current pregnancy. The importance of examining the placenta cannot be overlooked as it plays the central role in supporting fetal growth, also maternal wellbeing. Whether there are any differences among the different physiological positions of the placenta are still smoky. In this study, we compared anterior and posterior placental pregnancy of 13 -40 weeks intending to determine whether there are any variations in fetal biometry between these two and we also compared maternal age between two placental positions to find out if there is any relation of placental attachment variation with maternal age. A total of 427 pregnancies were in the range of 13-40 weeks. Among them, in 40 pregnancies, the placentas were formed in locations other than anterior or posterior (low lying position or completely fundal or lateral position). We excluded those pregnancies. Only anterior and posterior placental pregnancies were analyzed in this study. As a result of the above exclusion criteria, we could include a total of 387 pregnancies in our final analysis among them 188 pregnancies were anterior placental, and 199 pregnancies were posterior placental. As the anterior and posterior placental locations are more common in our study group, we compared fetal presentation, fetal HR, fetal biometry (BPD, FL, EFW), AFI, maternal age between the pregnancies of these two placental locations. In the case of anterior placental pregnancies, the fetal presentations were cephalic in 78.7% cases, breach in 11.2% cases, unstable in 9.04% cases and transverse in 1.06% cases. On the other hand, in pregnancies of posterior placenta fetal presentations were cephalic in 82.4% cases, breech in 14.6% cases, unstable in 2.5% cases and transverse in 0.5% cases. In this observational study, several parameters (BPD, FL, EFW, AFI, fetal presentation, HR, and maternal age) were compared between pregnancies of anterior placenta and pregnancies of the posterior placenta. In the case of all the parameters, no significant differences were found between the two groups except in some measurements of a few gestational age groups. But overall, when the measurements of 13-40 weeks of each parameter in two different groups were compared, there were no significant differences between them. In summary, this study results suggest that advanced maternal age is not one of the predisposing factors of having either anterior or posterior placenta and there is no significant difference in fetal presentation, fetal biometry, or fetal biophysical profile between anterior or posterior placental pregnancy.

Background: Hepatocellular Carcinoma (HCC) is the most common malignant neoplasm of the liver worldwide. Liver cancer ranks third among all cancer deaths in Bangladesh as the clinical presentation is non-specific, the modern imaging modalities can play an important role in the diagnosis of HCC and the radiologist is often the first to suggest the correct diagnosis. With the advent of multi-detector spiral CT, detection and characterization of HCC has markedly improved. Multi-detector spiral CT with its increased spatial and temporal resolution allows multiple perfusion phases of liver to be acquired.Multi-detector biphasic spiral CT techniques have been increasingly promising with respect to accurate preoperative diagnosis and assessment of the extent of HCC. This study was carried out to evaluate the role of biphasic contrast-enhanced spiral CT including Hepatic Arterial Phase (HAP) imaging with Portal Venous Phase (PVP) imaging, in the detection and characterization of HCC. Methods: The study included 35 patients (M=31, F=4) with histopathologically proven HCC. Age range was between 21-75 years (mean=51) by following consecutive patients in whom HCC was diagnosed or suspected either by raised serum a (alpha)-fetoprotein level or byultrasound imaging. Results: Biphasic contrast-enhanced examination in these 35 patients could reveal a total of 65 lesions, out of which 48% were unifocal and 46% were multifocal HCCs. On HAP imaging 92% lesions were detected. (hyper attenuating = 56, hypo attenuating = 4) while on PVP imaging delectability was only 51% (hyper attenuating=2, hypo attenuating=30). Hence delectability was significantly superior in HAP as compared to PVP imaging (p

Natural fiber composite is the novel materials in recent decades having a high strength to weight ratio and light in weight are widely used for structural and unstructured applications. Jute fiber is one of the most common biodegradable natural fibers which successfully replaced the synthetic fibers composite and also replaced glass fiber where the high strength is not obliged. Jute is grown in tropical countries and is one of the strongest bast fibers with low cost. Jute fiber composite has several attractive advantages over synthetic and glass fiber like as low processing cost, low density, stiffness and excellent mechanical properties. This advantage makes the jute a very attractive reinforced fiber for composites and increased attention in construction, automotive, aerospace and many others. This paper presented an overview on different jute fiber reinforced based polymer composites with mechanical characterization and their applications. The jute composites involving various thermoset, thermoplastics polymers, bio-based resins, jute hybrid composites and their mechanical properties are elucidated.

Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and detection and the Nuclei Segmentation Challenge 2018 datasets for segmentation tasks. The experimental results were evaluated with a five-fold cross-validation method, and the average testing results are compared against the existing approaches in terms of precision, recall, Dice Coefficient (DC), Mean Squared Error (MSE), F1-score, and overall testing accuracy by calculating pixels and cell-level analysis. Results The results demonstrate around 2.6% and 1.7% higher performance in terms of F1-score for nuclei classification and detection tasks when compared to the recently published DCNN based method. Also, for nuclei segmentation, the R2U-Net shows around 91.90% average testing accuracy in terms of DC, which is around 1.54% higher than the U-Net model. Conclusion The proposed methods demonstrate robustness with better quantitative and qualitative results in three different tasks for analyzing the WSI.

U-Net is a deep learning technique that has made significant contributions to medical image segmentation. Although the accomplishments of deep learning algorithms in terms of image processing are evident, many challenges still need to be overcome to achieve human-like performance. One of the main challenges in building deeper U-Nets is black-box problems, such as vanishing gradients. Overcoming this problem allows us to develop neural networks with advanced network designs.We propose three U-Net variants, namely efficient R2U-Net, efficient dense U-Net, and efficient fractal U-Net, that can create highly accurate segmentation maps. The first part of our contribution makes use of EfficientNet to distribute resources in the network efficiently. The second part of our work applies the following layer connections to design the U-Net decoders: residual connections, dense connections, and fractal expansion. We apply EfficientNet as the encoder to our three decoders to design three conceivable models.The aforementioned three proposed deep learning models were tested on four benchmark datasets, including the CHASE DB1 and digital retinal images for vessel extraction (DRIVE) retinal image databases and the ISIC 2018 and HAM10000 dermoscopy image databases. We obtained the highest Dice coefficient of 0.8013, 0.8808, 0.8019, and 0.9295 for CHASE DB1, ISIC 2018, DRIVE, and HAM10000, respectively, and a Jaccard (JAC) score of 0.6686, 0.7870, 0.6694, and 0.8683 for CHASE DB1, ISIC 2018, DRIVE, and HAM10000, respectively. Statistical analysis revealed that the proposed deep learning models achieved better segmentation results compared with the state-of-the-art models.U-Net is quite an adaptable deep learning framework and can be integrated with other deep learning techniques. The use of recurrent feedback connections, dense convolution, residual skip connections, and fractal convolutional expansions allow for the design of improved deeper U-Net models. With the addition of EfficientNet, we can now leverage the performance of an optimally scaled classifier for U-Net encoders.

Lead-free inorganic metal halide perovskites CsGeCl3 have recently gained prominent research interest in solar technology due to their outstanding optoelectronic properties and mechanical stability. Here, the density functional theory is considered to investigate the biaxial strain-driven (from −6% to +6%) structural configuration, mechanical stability, and optoelectronic properties of non-toxic CsGeCl3 metal halide. Optical properties such as absorption coefficient, dielectric functions, and electron loss function show that due to the biaxial strain (compressive and tensile), this material has a high absorption capacity of photons in the visible and ultraviolet regions, and that’s why it is very much suitable to apply in the solar cells and other optoelectronic energy devices. The electronic band structure shows that CsGeCl3 is a semiconductor material with a direct bandgap of 0.768 eV at the R-point. Moreover, we observed a semiconductor-to-metallic transition of the bandgap of CsGeCl3 in the presence of the compressive strain. The findings of the mechanical properties of the CsGeCl3 perovskites demonstrate that Ge could be a suitable replacement for Pb in the traditional Pb-based perovskite structures. Especially in the strain portion of −2% to +2%, the investigated metal halide perovskite structure, Pb being replaced by Ge, shows mechanical ductility, absorption of visible and UV radiation, prominent absorption peaks, tunable bandgap value of 0.123 to 0.896 eV and thus, is very much suitable to be considered for solar photovoltaic applications.

Deep Learning (DL) shows the state-of-art performance in detection, classification and segmentation tasks compared to existing methods. As computational pathology becomes a very promising area of research, it is important to determine which color space provides better results for pathological image segmentation tasks. In this paper, we have considered six different color spaces, namely RGB, LAB, CIE, YCrCb, HSV and HSL for nuclei segmentation tasks where the Recurrent Residual based U-Net (R2U-Net) model is applied. The ISMI-2017 publicly available dataset is used for evaluating the model in this implementation. The Lab color space shows an F1-score of 0.9365, which is the highest segmentation performance when compared to the other color spaces. The Lab color space model shows around 0.38% better performance compared to the RGB color space for nuclei segmentation tasks. This investigation will provide a clear guidance in advance of pathological image segmentation and analysis tasks.

Reducing Magnetic Resonance (MR) image acquisition time can potentially help to reduce cost and make the MRI based assessment more accessible for the patients care. The GRAPPA and Compressive Sensing (CS) have been applied for image reconstruction from the data where original sampled at a rate inferior to the Nyquist-Shannon sample theory. However, most of the existing techniques are iterative and time-consuming, as a result these methods are not suitable for the real-time applications for MRI reconstruction problem. Nowadays, the Deep Learning (DL) based methods have been showing state-of-the-art performance for different image acquisition and reconstruction tasks including MRI reconstruction. The DL based solution is more robust, generic, transferable, and faster. Hence, the deep learning-based solution can be used to deploy a real-time and faster MRI reconstruction system. In this work, we applied the NABLA-N deep learning model for the MRI reconstruction in both image-domain and frequency-domain (k-space). For evaluating the model, the experiments have conducted on publicly available MRI reconstruction challenge dataset. The results demonstrate that the NABLA-N Net (∇N-Net) model is capable to reconstruct high-quality MR images and show that kspace reconstruction model is better and more capable compared to image-space based reconstruction model.

In the field of computer vision and medical imaging, the classification is one of the common approaches apply to solve different problems. There are several Deep Learning (DL) models have been proposed in the last few years and applied for classification tasks and shown significantly better performance compared to existing methods. In particular, the DL methods have been applied and achieved state-of-the-art performance for classification, segmentation, and detection tasks in the field of medical imaging. In the field of digital pathology, very large dimensional images are captured with microscopes which are significantly higher in dimension and different (texture, color, etc.) compared to the images are used for analysis in computer vision tasks. As the computational pathology becomes very promising area of research, it is very important to study the impact of different color spaces for pathological image classification problems. In this paper, we have considered six different color spaces including RGB, CIE, HSV, YCbCr, Lab, and HSL for histopathology tissue classification tasks where the Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model is applied. The KIMIA Path960 database is used for evaluating the model in this implementation. The RGB color space shows the highest testing performance for tissue classification task which is around 0. 73% and 1.13% better compared to the performance of Lab and CIE color spaces respectively and this performance is significantly higher than the testing accuracy of the model in others color spaces. In addition, the highest performance for the proposed model is around 0.5 percent better compared to existing methods. This study will provide a clear guidance in advance for implementing classification tasks in digital pathology.I n the field of computer vision and medical imaging, the classification is one of the common approaches apply to solve different problems. There are several Deep Learning (DL) models have been proposed in the last few years and applied for classification tasks and shown significantly better performance compared to existing methods. In particular, the DL methods have been applied and achieved state-of-the-art performance for classification, segmentation, and detection tasks in the field of medical imaging. In the field of digital pathology, very large dimensional images are captured with microscopes which are significantly higher in dimension and different (texture, color, etc.) compared to the images are used for analysis in computer vision tasks. As the computational pathology becomes very promising area of research, it is very important to study the impact of different color spaces for pathological image classification problems. In this paper, we have considered six different color spaces including RGB, CIE, HSV, YCbCr, Lab, and HSL for histopathology tissue classification tasks where the Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model is applied. The KIMIA Path960 database is used for evaluating the model in this implementation. The RGB color space shows the highest testing performance for tissue classification task which is around 0. 73% and 1.13% better compared to the performance of Lab and CIE color spaces respectively and this performance is significantly higher than the testing accuracy of the model in others color spaces. In addition, the highest performance for the proposed model is around 0.5 percent better compared to existing methods. This study will provide a clear guidance in advance for implementing classification tasks in digital pathology.

In the last few years, deep learning approaches have been applied successfully in different modalities of medical imaging problems and achieved state-of-the-art accuracy. Due to the huge volume and variety of imaging modalities, it remains a large open research area. However, in this paper, we have applied Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model for histopathological image classification where a new publicly available dataset named KIMIA Path960 is used. This database contains 960 histopathological images with 20 different classes (different types of tissue collected from 400 Whole Slide Images). In this implementation, we have evaluated the model with non-overlapping patches size of 64×64 pixels and the variant of samples are generated from each patch with different data augmentation techniques including rotation, shear, zooming, and horizontal and vertical flipping. The experimental results are compared against Linear Binary Pattern (LBP), bag-of-visual words (BoVW), and deep learning method with AlexNet and VGG16 networks. The IRRCNN model shows around 98.79% testing accuracy for augmented patch-level evaluation which is around 2.29% and 4% superior performance compared to Support Vector Machine with histogram intersection kernel (IKSVM) with BoVW and VGG16 methods respectively. Additionally, this evaluation also demonstrates that the deep feature representation-based method outperforms compared to a traditional feature-based method including LBP and BoVW for the histopathological image classification problem.

Asset allocation is a compute intensive combinatorial optimization problem commonly tasked to autonomous decision making systems. However, cognitive agents interact in real time with their environment and are generally heavily power constrained. Thus, there is strong need for a real time asset allocation agent running on a low power computing platform to ensure efficiency and portability. As an alternative to traditional techniques, work presented in this paper describes how spiking neuron algorithms can be used to carry out asset allocation. We show that a significant reduction in computation time can be gained if the user is willing to accept a near optimal solution using our spiking neuron approach. As of late, specialized neuromorphic spiking processors have demonstrated a dramatic reduction in power consumption relative to traditional processing techniques for certain applications. Improved efficiencies are primarily due to unique algorithmic processing that produces a reduction in data movement and an increase in parallel computation. In this work, we use the TrueNorth spiking neural network processor to implement our asset allocation algorithm. With an operating power of approximately 50 mW, we show the feasibility of performing portable low-power task allocation on a spiking neuromorphic processor.

Extreme Learning Machines (ELM) has been introduced as a new algorithm for training single hidden layer feedforward neural networks instead of the classical gradient-based approaches. Based on the consistency property of data, which enforces similar samples to share similar properties, ELM is a biologically inspired learning algorithm that learns much faster with good generalization and performs well in classification tasks. However, the stochastic characteristics of hidden layer outputs from the random generation of the weight matrix in current ELMs leads to the possibility of unstable outputs in the learning and testing phases. This is detrimental to the overall performance when many repeated trials are conducted. To cope with this issue, we present a new ELM approach, named State Preserving Extreme Leaning Machine (SPELM). SPELM ensures the overall training and testing performance of the classical ELM while monotonically increases its accuracy by preserving state variables. For evaluation, experiments are performed on different benchmark datasets including applications in face recognition, pedestrian detection, and network intrusion detection for cyber security. Several popular feature extraction techniques, namely Gabor, pyramid histogram of oriented gradients, and local binary pattern are also incorporated with SPELM. Experimental results show that our SPELM algorithm yields the best performance on tested data over ELM and RELM.

Scene classification remains a challenging task in computer vision applications due to a wide range of intraclass and interclass variations. A robust feature extraction technique and an effective classifier are required to achieve satisfactory recognition performance. Herein, we propose a nonregularized state preserving extreme learning machine (NSPELM) to perform scene classification tasks. We employ a Bag-of-Words (BoW) model for feature extraction prior to performing the classification task. The BoW feature is obtained based on a regular grid method for point selection and Speeded Up Robust Features (SURF) technique for feature extraction on the selected points. The performance of NSPELM is tested and evaluated on three standard scene category classification datasets. The recognition accuracy is compared with the standard extreme learning machine classifier and it shows that the proposed NSPELM algorithm yields better accuracy.

In this paper the real time virtual dress-up system has been proposed. The proposed system consists of multiple tasks including extraction of different body parts, torso detection, resizing input dress images and dress up using blending and re-blending techniques over the subject. The coexistence of different clothing and cluttering backgrounds is the main difficulty for accurate body extraction in image. Haar classifier is applied for detecting face from input frames and geometrical information is used to extract different parts (like a face, a torso, and hands) of a body according to the face position in a frame. Due to the variability of human body, it is complicated to extract accurately. A novel dominant colors based segmentation method is proposed to tackle this problem. First, an image is segmented into uniform areas based on HSV color components. Then, dominant colors of the torso are adaptively selected using color probability model. The torso has been extracted based on the dominant colors to resize the input dress image to fit over the subject body as well as dress size prediction. Automatic dress blending points are calculated on human body using torso starting position and geometrical relationship with face region. A selected dress is scaled and rendered to fit with the subject’s body even they move around. Some preprocessing and post processing techniques are used to make outputs more accurate and realistic.

The models for investigating the phase diagram of InGaN thin films have been anticipated by considering the effects of strain energy, the self-energy of misfit dislocations and surface energy to Gibbs free energy. Total Gibb's free energy varies with Indium composition and thickness of the epitaxial thin film. The calculated results indicate that over critical thickness, energy of the films increases with increasing thickness. The phase diagrams of InGaN films grown on GaN substrate have been calculated. There is a small effect of surface energies of solid phases on the phase diagrams of epitaxial film. With increasing the thickness of InGaN films the wurtzite phase is found to be decreased. This is due to the increase of thickness, misfit dislocations and the commencing of phase separation. Accurate information of InGaN nano film is applicable for the innovation of III-nitride based nano-electronics system.

With the advent of digital technology, security threats for computer networks have increased dramatically over the last decade being much bolder and brazen. There is a great need for an effective Intrusion Detection System (IDS) which are intelligent specialized system designed to interpret the intrusion attempts in incoming network traffic. Deep belief neural (DBN) networks proved to be the most influential deep neural nets and generative neural networks that stack Restricted Boltzmann Machines. In this paper, we explore the capabilities of DBN's performing intrusion detection through series of experiments after training it with NSL-KDD dataset. The trained DBN network now identifies any kind of unknown attack in dataset supplied to it and to the best of our knowledge this is first comprehensive paper performing intrusion detection using deep belief nets. The proposed system not only detect attacks but also classify them in five groups with the accuracy of identifying and classifying network activity based on limited, incomplete, and nonlinear data sources. The proposed system achieved detection accuracy about 97.5% for only fifty iterations that is state of art performance compare to the existing system till today for intrusion detection.

The face recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses etc.), etc. Principal components from the face space are used for face recognition to reduce dimensionality of database images. However, this paper discusses on adaptively weighted sub-pattern PCA (Aw-SpPCA) based face recognition system for dark images that have captured at night. It is really difficult to capture good quality picture at night for lacking of light source with traditional acquisition devices like camera or mobile phone. The computational photographic concepts have been applied to enhance the quality of the capture images at night automatically. Multi-scale retinex color restorations (MSRCR) technique has been applied for overcome this problem. Moreover, for recognition phase of this propose method, unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and then endows them to a classification task in order to enhance the robustness to both expression and illumination variations. Experimental results show that the proposed method is competitive.

A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.

Orthogonal space-time codes (OSTCs) have recently captivated considerable attention due to their properties of full diversity and low implementation complexity. In this paper, we derive effective use OSTCs for MIMO systems successfully. It has been calculated coding gain of the orthogonal ST code and fast ML decoding at the receiver can be decoded separately. This goal achieved by properly choosing the signal constellations and compare bit-error-rate (BER) to signal-to-noise ratio (SNR) of different QPSK and QAM techniques. Simulation results show that the obtained constellations outperform not only the conventional QAM constellation, but also other well-known constellations used for efficient data communications.

A real-time background segmentation system has already proposed based on two methods Principle Components Analysis (PCA) and Single Gaussian Model (SGM). But PCA based method is required to calculate of decomposition and for this reasons time consumption is vast. On the other hand SGM based foreground segmentation model which is reliably deals with lighting changes, detects repetitive motion from clutter and long-term scene changes. This paper presents a method consists with two methods extended PCA (EPCA) and SGM. An adaptive strategy is used to integrate two methods. We adopt the adaptively incremental eigenspace model to build the intensity information for each pixel. Weng designed Candidate Covariance-Free Incremental PCA (CCIPCA) method, which does not need decomposition. Moreover, it is also simpler and faster than other proposed incremental PCA (IPCA) methods. Shadow removal using adaptive filter and post-processing techniques are used in a proposed method. Therefore, as the proposed algorithm does not require decomposition so this method is faster with PCA and SGM based foreground extraction method. Experimental results represent that this proposed model is robust to noise and illumination change due to inheriting eigenbackground and Gaussian's model advantages to improve the segmentation results.

This article presents performance analysis of multiple channel noise cancellation by beam forming algorithm depending on the variation of arrival of angles of the interference signal. In this article we examined that the effectiveness of the desired signal by the different arrival of angles of interference signals. The variation of the arrival angles of the interference signal considered to with intervals and the desired signal arrival angle is . The desired signal and the optimized multi-layer perception (MLP) were used to calculate the correlation values at different angle. We also used the Back propagation algorithm as learning rule for MLP and improving our signal quality. This involves a desired signal whilst removing any noise or interference signals which may come from different sources [1][2].

This paper presents performance analysis of beamforming algorithm for canceling multiple channel noise depending on variation of hidden layer of the multilayer feedforward network and the number of epoch (also known as number of iteration). We consider the learning rate variation of the multi layer perception (MLP) to determine the adaptive learning rate of the network. An MLP has been considered to perform beam-forming, which is an array of sensors connected to the MLP inputs. We use the backpropagation algorithm as the learning rule for MLP and improving the signal quality. This involves a desired signal whilst removing any noise or interference signals which may come from different sources.

The bit error rate (BER) performance for quadrature amplitude modulation (QAM) with different diversity combining scheme is derived for Rayleigh fading channel using Log-likelihood ratio (LLR). Three combining techniques, maximal ratio combiner (MMC), selection combining (SC), and Equal Gain Combiner (EGC) are considered for the BER analysis in this paper. The average BER performance of QAM symbol through a flat fading channel is derived from the individual bits forming the QAM symbol with MRC, SC, and EGC space diversity. The analytical and simulation of BER performance of the QAM symbol through the fading channel with MMC, SC, and EGC combiner is compared. The both analytical and simulation result shows that the probability of bit error decreases with the order of diversity for all the cases of MRC, SC, and EGC. It is also seen from the results that MRC provides better BER performance than the other two combiners (SC and EGC) for the same environment of the fading channel.

Single carrier communication (SCC) uses equalizer to minimize signal distortion. Design complexity increases with data rate in SCC. Multi-carrier based wireless communication reduces channel fading by sub-dividing the channel into a number of equal narrow bands, in which inter symbol interference (ISI) and inter carrier interference (ICI) effects cause bit error. Recently, a mechanism has been proposed to mitigate these interferences by cyclically shuffling the sub-carrier frequency and using CI (Carrier Interferometry) code to each sub-carrier. We attempt here to compute the cross-correlation between the sub-carriers to observe the proper orthogonality conditions. We also compute the cross-correlation between the present and previously transmitted symbols to find the orthogonality condition effect. Finally, the authors compute the peak-to-average power ratio (PAPR) of the frequency-shuffled CI/OFDM modulation system to observe its effectiveness for safety of the signal processing devices in the receiver.

In orthogonal frequency division multiplexing (OFDM) based wireless communication systems; extra guard period is inserted to eliminate ISI (inter symbol interference) and ICI (inter carrier interference) effect. The guard period decreases the symbol rate. The authors describe here a model that eliminates the effects of ICI and ISI without inserting extra guard period. The sub-carrier frequencies and carrier interferometry (CI) code distribute alternatively between consecutive OFDM symbols to eliminate the ISI effect. The CI code is used to each sub-carrier in such a way that the subcarrier frequency, orthogonal to each other, when Doppler frequency shift occurs due to relative movement of the transmitter and receiver. A pilot symbol is used in between the two symbols for linear operation of all the electronic devices. A properly designed matched filter at the transmitter and receiver can reduce the effect of ICI. Here, we consider the effect of ICI for carrier frequency synchronizing error, Doppler shift and phase error with a time invariant channel as well.

This paper presents performance analysis of beamforming algorithm for canceling multiple channel noise depending on variation of hidden layer of the multilayer feedforward network and the number of epoch (also known as number of iteration)[1]. A multi-layer perception (MLP) has been considered to perform beamforming. A beamforming is an array of sensors connected to the MLP inputs. We have also used the backpropagation algorithm as learning rule for MLP and improving our signal quality. This involves a desired signal whilst removing any noise or interference signals which may come from different sources.