Search

Over the years world is revolutionized with the advent of technology, the field of transmission too is made easier with the use of different technologies but security during transmission is still a threat. Visual cryptography (VC) is a cryptographic technique that encrypts visual information (pictures, text, etc.) where the decrypted content appears as a visual image. Visual cryptography enables to transmit images securely as well as maintains data confidentiality. The security of multimodal biometric data is a challenging task in the current world scenario as various domains are prone to more threats. In order to secure and authenticate multimodal biometric data a new framework is proposed. The proposed framework provides an optimal solution for securing and authenticating images during transmission. In this paper, three types of biometric inputs were resolute and taken as input: Fingerprint, Face and Iris images. A new shuffling approach which is based on a pixel element is used for creating shares for every plane. These shares are used in the decoding process for reconstruction. The three reconstructed images are passed through the bilateral filter in order to eliminate noise and preserve edges. Each biometric data (BD) utilizes different image segmentation technique: (1) Binary segmentation for finger-print image, (2) face segmentation for face image, and (3) Daugman's integrodifferential operational for iris image. Similarly, feature extraction modules are executed on three segmented images. These features extracted from the reconstructed images are used to train the Visual Sharing Neural Network (VSNN) along with Recurrent Neural Network- Bidirectional Long Short-Term Memory (RNN-BiLSTM) model. In the testing process, the input image is classified using VSNN and it checks for matching BD in the database before providing the authentication to individual. The simulation result, of the proposed module, gives the PSNR as 38.58, MSE as 9, and NCC as 1, 95% of accuracy, 100% of sensitivity, and 5% of error rate, 98.8% of identification rate, 0.45% of false acceptance rate, and 0.97% of false rejection rate.

In the existing multimodal biometric authentication technique, there is no integrity in the biometric information, and authentication of end users is also not very secured. In this paper, we propose Fisher-Yates Chaotic Shuffling based Visual Cryptography Scheme (FYCS-VCS) for Multimodal Biometric Authentication. The proposed approach enhances the security of multimodal biometric images by protecting the individual shares which supports in maintaining the integrity and confidentiality. Also by the combination of FYCS –VCS Scheme the intruders cannot view and modify shares without knowing the sequential order of the shares. In this technique, initially two or three (finger print, Iris, Face) biometric modalities are considered and fused into a binary image using Histogram of Oriented Gradients (HOG) and Low-Rank Representation (LRR) scheme.Then using (k, n) VSS scheme, n shares of the binary image is created which are encrypted by applying Fisher-Yates Chaotic Shuffling algorithm.. The shares are reconstructed back by applying reverse of Yates-Shuffling algorithm, to generate the fused image. By extracting the HOG features from the fused image, the respective biometric images are obtained. The reconstructed biometric images are authenticated against the original images of the person by an authentication server. The proposed FYCS-VCS multimodal biometric authentication scheme is implemented in MATLAB and is compared against the VSS scheme. Experimental results have shown that FYCS-VCS attain lesser MSE and higher PSNR values when compared to VSS. Proposed approach reduces storage cost and time complexity.

Mobile ad-hoc network (MANET) is a theoretical and experimental approach for achieving the applications to the best using VANETs. Given the mobility of nodes in the mobile ad-hoc networks, it is hard to depict the nature of the network or the structure of the network. With static nodes, it is easy to monitor a network. In a mobile environment, any node can come and join the network based on the distance covered by the entire network. A node that enters the region joins the network, while one that moves away leaves it and ceases participating in network communication. The routing table is updated, based on the movement of the nodes. Owing to the factors above, security fails to live up to expectations. Identifying a vulnerable node is a difficult proposition. This paper offers a prediction model based on the delay factor, which impacts the performance of the node and its network. The experimental results determine the malicious node. A malicious node is disconnected from the network.

Nowadays, a demand is increased all over the world in the field of information security and security regulations. Intrusion detection (ID) plays a significant role in providing security to the information, and it is an important technology to identify various threats in network during transmission of information. The proposed system is to develop a two-layer security model: (1) Intrusion Detection, (2) Biometric Multimodal Authentication. In this research, an Improved Recurrent Neural Network with Bi directional Long Short-Term Memory (I-RNN-BiLSTM) is proposed, where the performance of the network is improved by introducing hybrid sigmoid-tanh activation function. The intrusion detection is performed using I-RNN-BiLSTM to classify the NSL-KDD dataset. To develop the biometric multimodal authentication system, three biometric images of face, iris, and fingerprint are considered and combined using Shuffling algorithm. The features are extracted by Gabor, Canny Edge, and Minutiae for face, iris, and fingerprint, respectively. The biometric multimodal authentication is performed by the proposed I-RNN-BiLSTM. The performance of the proposed I-RNN-BiLSTM has been analysed through different metrics like accuracy, f-score, and confusion matrix. The simulation results showed that the proposed system gives better results for intrusion detection. Proposed model attains an accuracy of 98% for the authentication process and accuracy of 98.94% for the intrusion detection process.

India is rich with its culture and heritage. It is known for its traditional medicinal system and it is mentioned even in the ancient Vedas and other scriptures also. In India, Traditional Medical system includes Ayurveda, yoga, siddha, Unani, and homeopathy. Biomedical Text Mining (BioTM) is aiming at the extraction of novel, non-trivial information from the large amounts of biomedical related documents. This unstructured bio medical document holds greater knowledge about medical diagnostics, treatment, and prevention. Ontology plays a vital role in deep understanding of information. It is the building block of the Semantic Web and considers it’s important on the semantic clarity of concepts and entities. The main objective is to search the most relevant content from this huge set of text by understanding the meaning of conceptual terms. We proposed Ontology based Concept Extraction and Classification in which the domain ontology and semantic document description was used to improve classification accuracy. The results show that the classification accuracy of proposed algorithm outperforms than the other existing supervised machine learning algorithm. To further prove the efficiency of the model, experiments were conducted by giving different queries and the results are compared with other existing methods. The results show that the content retrieved by the proposed model was most relevant when compared with existing system.

As the technology grows rapidly the level of sophistication also increases day by day. One of the biggest challenges faced today is the energy conservation with the growth in technology. The conservation of energy starts with the control of the appliance, ranging from home to large industry. Appliances control at home is a way where the energy wasted can be reduced to a minimum. This can be done with the help of smartphones. As the world is shrinks into the smartphones, everything becomes handy. An idea of home appliance control with the smartphones makes it easy for the user to control the appliances inside the home. As every smartphone is provided with the Bluetooth technology, it's tranquil to control the devices inside the home premises. Also with the help of SEAP protocol and the sensors, a secure way of controlling the appliances is made. An Android application for the smartphone is been developed, which is used in the control of the home appliances. Thus the user can control the appliances from their place and conserve the energy without much work done.

In this work we use drip irrigation where the water was allowed to drip slowly to the roots of plant either from above the soil surface or buried into the surface so that the water can be placed directly into the root zone and minimize evaporation. It uses temperature sensor, soil humidity sensor to collect and monitor field information and also uses float switches to monitor ground water level through web page. When the field gets dry and ground water level falls down it will be notified through SMS. This provides a solution for the problems in developing a smart farming system. It uses node MCU, relay and water pump.

Security plays a vital role in communication networks. Since the nodes are mobile in Mobile Ad-hoc Networks (MANET), they are vulnerable to different types of attacks. Because of its mobility nature any node can enter the network at any time based on the coverage of the network. No centralized mechanism is found to verify or authenticate the nodes that are arriving/leaving the network. An algorithm is proposed for secure communication between source and destination based on the QoS parameters is called Modified Device Key Generation Algorithm (MDKGA). This algorithm elects an agent node based on the QoS parameters. Agent node is responsible for secure key generation and distribution of keys among the nodes. The neighboring node selection is based on trust value which acts as a heuristic function to select the node using A* algorithm.Various performance metrics are also analyzed. Comparison study has been carried out between the protocols of MANET.

Telecommunication services have been developing at a continuously accelerating pace, and it is forecasted that it will accelerate even more, that is particularly so for the wireless communication system with the introduction of 4G and 5G and the integration of the Internet of Things (IoT). Microstrip patch antennas present undeniable advantages when compared to other antennas such as low cost, compact, planar surface, flexibility in performance parameters and easy fabrication. Such advantages have made these antennas so popular. The main objective of this research is to design an antenna operating at multiple frequencies with high performance. This paper discusses design of compact multiband antenna with microstrip-line-feed designed for future wireless devices applications. The introduced antenna is simulated using Rogers RT-5880 substrate of having dimensions 21 × 16 × 0.507 mm3 with dielectric value 2.2 and 0.0009 tangent loss. Planned patch antenna operates initially at 10GHz with return loss -11.19dB and VSWR 1.76. I shaped slots on patch and defective ground structure technique is employed for multiband operation and improvement in performance parameters. Multiband antenna resonates frequencies 10, 21, 30and 34 GHz with return loss -19 dB, - 12dB, -12dB, -11 dB and VSWR 1.2, 1.5, 1.6, 1.82. International Telecommunication Union (ITU) specified these frequencies for radio astronomy, wireless LAN, satellite communications, DBS applications. Designed antenna provides gain 7.785dB, 3.508 dB, 7.510 dB,7.7746 dB at 10,21,30,34GHz respectively.

A Visible Light Communication (VLC) is a fast growing technology became ubiquitous in the Optical wireless communication domain. It has the benefits of high security, high bandwidth, less power consumption, free from Electro Magnetic radiation hazards. VLC can help to address the looming spectrum crunch problem with secure communication in an unlimited spectrum. VLC provides extensive wireless connectivity with larger data densities than Wi-Fi along with added security features that annihilate unwanted external network invasion. The problem such as energy consumption and infrastructure complexity has been reduced by integrating the illumination and data services. The objective is to provide fast data communication with uninterrupted network connectivity and high accuracy to the user. In this paper, a proposed visible light communication system for transmitting text information using amplitude shift keying modulation (ASK) has been presented. Testing of transmitter and receiver block based on frequency, power and distance has been analyzed. The results show that the receiver is capable of receiving input data with minimum length under direct communication with the transmitter.

Biometrics is an ever-emerging field which is now being utilized in many applications and systems. Face biometrics are quite prevalent and have already been deployed in many devices and applications. Iris biometrics though already studied and deployed in certain areas are considered very effective due to their lesser chances of changing with time and uniqueness. Face detection and iris detection for verification have been employed in various biometric applications already. Unimodal systems of face and iris are secure, but for enhanced levels of security, multimodal biometric systems are being created. Multimodal biometric systems are those systems which use the various biometric traits simultaneously, in order to authenticate a person’s identity. There is a direct enhancement in the verification accuracy due to such systems. The fusion of these two biometrics in consideration to make the process of unauthenticated or unauthorized intervention complex because it will be difficult and inefficient to get multiple biometric traits simultaneously in real time. In order to draw a contrast between a unimodal approach and multimodal approach for the biometrics, this paper presents a survey on using individual face classifiers or individual iris classifiers and compares it with the combined classifier results. After analysing the various techniques, it is concluded with the directions towards the future scope.

The identification of the make and model of the primary knee implant is an essential step for planning a revision surgery. Currently, the surgeons email the radiographs of the implant to the medical representatives of the manufacturing companies to get this information. This manual process is prone to errors and involves a considerable amount of resources and surgeon time that could be otherwise spent on more useful tasks. This study proposes an artificial intelligence based solution for the automatic identification of 6 makes of orthopedic knee implants. Deep Convolutional Neural Networks were trained on a dataset comprising 878 images of radiographs of orthopedic knee implants including both anterior posterior and lateral views. The results of the experiments showed a validation accuracy of 96.66%. Furthermore, class activation maps generated on the images when passed through the deep learning algorithm provided visual conformance to the region of interests that a surgeon would consider for identification of the make of the implant. The outcomes of this study demonstrates the effectiveness of the usage of Deep Convolutional Neural Networks for assisting orthopedic surgeons in pre-operative planning of revision surgery by accurate automated identification of make and model of orthopedic knee implant.

Coronavirus disease has been rampaging the world since its onset in the Wuhan region of China with cases skyrocketing every day. A crucial step for mitigating the havoc in this situation is the early screening of the infected patients and isolating them. Given the overwhelming number of people falling prey to this pandemic, it becomes very difficult for hospitals to provide these services within the required time. The power of Artificial Intelligence can be leveraged to solve the issue of delay in the diagnosis of coronavirus by automating the process. In this study, we propose our method, a transfer learning approach on deep convolutional neural networks to classify chest radiographs of patients as healthy or infected with Coronavirus based on the fact that Coronavirus attacks the epithelial cells of lungs resulting in pneumonia. The data used for this study has been curated from various public resources online. Our proposed model was able to achieve a validation score of 100% on the curated dataset along with high specificity and sensitivity of 1.00. This work aims to serve as a first cut solution to the automated diagnosis and doesn’t claim to be the exact solution without further validation by medical professionals.

Biometric technique uses biological or behavioural characteristics to recognize individuals. A number of biometric features have been developed and studied, including fingerprint, voice, face, palm-print, iris, gait, and many other features. Other than identity, biometric data has the potential to reveal various other information about a person. Among these, analysis of demographics (age, sex and ethnicity) has become a popular subject of interest because of its endless applications and enormous potential. One plausible application is to create a detailed customer profile that can be used by the business intelligence model to benefit on several grounds. The two attributes of the demographics namely, age and gender provide us with a great deal of understanding about the customer visiting the outlet. The advantages which can be tapped by having a database of the age and gender of the customer are enormous.

In this contemporary world, mobile ad-hoc networks emerges as an imperative field in computer science. An application of MANET is vehicular networks (VANET) which gives its maximum coverage towards real time actions like vehicular traffic control etc. A mobile ad-hoc network is a network where nodes are movable in state, infrastructure-less, self-configuring and self-repairing from the disconnected links between the nodes. The network topology changes according to the nodes’ factors. Due to lack of monitoring, any node can join the network topology based on RF level or distance coverage. Any unauthorised node enters the network and involves in communication without any legalized acceptance from the network. In case of any unusual entry of the node will results in degrade in the network efficiency factors. Anyhow it will ruin the entire network performance and makes to split the topology which causes overhead to start from the initial process. In this paper, we combined one or more attacks by deploying one or more suspicious nodes into the network. By analysing the results from simulation various performance factors are compared based on several test cases. An authorised agent node is placed in the network and issues secret key value to the mobile nodes. Analysis form the results are calculated using regression and deviation method. This paper also gives the analysis of various combinatorial ways of deployed attacks in the network.

Cognitive radio based dynamic spectrum access mechanism is a revolution in wireless communication which alleviates the spectrum utilization problem. Here cognitive users uses spectrum sensing techniques, to sense the bands before transmitting on them to avoid collision with the licensed users which leads to delay and conserves more energy. To reduce delay and energy consumption and to predict the future usage of channels spectrum prediction methodologies used. Spectrum prediction used to predict the future channel status based on collected historical data, here to solve the problem, the neural network based spectrum prediction using back propagation training model has been proposed. To optimize the structure of the neural network and to reduce the aggressive weight structural pattern genetic algorithm is used here to avoid trapping in local optimal solution. Selection, crossover and mutation functions are performed to increase the randomness, which extends the population converge to the set that contains the global optimal solution. Simulation results show the genetic algorithm which is used has increased the probability of obtaining the best weights by optimizing the network, also the proposed scheme results indicate high prediction accuracy.

India is known for its traditional medicines like Ayurveda, siddha, unnani, homeopathy and yoga. Publishing of literatures on traditional medicines are expanding rapidly. Due to the exponential growth of articles and literatures existence, it is difficult for the user or the practitioners to find the relevant and useful information from this large amount of data. Text mining is applied on this data to find the useful facts. In this study, we concentrate on ayurvedic medical system and we discussed on how to apply text mining techniques on ayurvedic literatures to find the most relevant ayurvedic facts. Moreover we constructed ontology for the medicinal plants for better understanding of relations among such herbal plants with its usage in curing the diseases.

Biometrics is the emerging technology in the era of internet and mobile communication. The IoT revolution has enabled the things around us to communicate as it emerges as a smart system, hence security should be considered as primary issue as everything around us is going to be connected. Biometric Technology is considered to be the future of all electronic security which provides authentication and security management. This paper provides a detailed survey on existing biometric technology, different types of biometric traits, techniques adopted for feature extraction of various biometric traits and application areas of different biometric traits. The importance of biometric technology related to the various fields of security is also discussed through this paper. The biometric technology connected with smart systems helps in monitoring the human activities all over the world, thus providing a good security level. Biometrics acts as a major support to various fields of automobile security, Internet of Things (IOT) Security, health care security, workforce management of organization, government security, banking, and retail industry.

India is rich with its culture and heritage and is well-known for its conventional or traditional medicinal systems like yoga, Ayurveda, homeopathy, Siddha, and Unani. These kinds of medicinal information are available on the web from heterogeneous sources. The information contained in the form of published literature is in free form and it is natural language. Finding vital information hidden in the literature can be challenging. There have been a massive number of publications and a large number of articles on Ayurvedic research. To understand the underlying concepts and its relation semantically, the ontological approach can be incorporated to structure the Ayurvedic information on the web and to access those thereby enabling the heterogeneous sources of information and systems interoperability. This paper proposed the construction of domain ontology for traditional Ayurvedic medicine.

Face recognition finds its major applications in biometrical security, health care and marketing. Among many biometrics like finger print, iris, voice, hand geometry, signature etc., face recognition has attained the most popular research orientation because of high recognition rate, uniqueness and large number of features. Some of the challenges in face recognition task include the differences in illumination, poses, expressions and background. Now-a-days deep learning methods are widely employed and they ensure promising results for image recognition and classification problems. Convolutional Neural Networks automatically learn features at multiple levels of abstraction through back propagation with convolution layers, pooling layers and fully connected layers. In this work, an automated face recognition method using Convolutional Neural Network (CNN) with transfer learning approach is proposed. The CNN with weights learned from pre-trained model VGG-16 on huge ImageNet database is used to train the images from the face database. The extracted features are fed as input to the Fully connected layer and softmax activation for classification. Two publicly available databases of face images – Yale and AT&T are used to test the performance of the proposed method. Experimental results verify that the method gives better recognition results compared to other methods.

Controlling a bot machine that uses non-conventional energy form, i.e. light is said to have an upper hand in pioneering transportation system. The expanding request of making the streets more secure has persuaded a ton of organizations to create finest autonomous vehicles. This paper will concentrate on the potential outcomes of utilizing just light-sensing gadgets alone for the light tracking bot using advanced color detection algorithm. The algorithm would help the bot in sensing the color of light and act accordingly, for instance green color to proceed, red color to stop. This particular requisition has high scope in real time application over the emergent underground transportation system; speculating on how the emerging innovative advances fit to the fiddle urban areas of the 21st century.

Smart mirror also called magic mirror is a wall-mounted mirror or a bathroom mirror which helps in displaying data along with the functionality of a normal mirror. It displays the plain texts, imag...

This paper is about study of comparative analysis of Spatial and Transform domain fusion techniques under Compressive Sensing or Compressive Sampling principle. The compressive measurements of two source images are obtained using star shaped sampling pattern and fuse the measurements. The output image is reconstructed from 25% of samples using Minimum Total Variation method with equality constraints and with reduced computational time. Finally, for different fusion techniques under Compressive Sensing are performed and compared. Multi focus and Multi modal images are used for simulation and no prior knowledge of source images is required for reconstruction. Based on fusion evaluation metric with reference and without reference image conclude that in spatial domain, simple average & principal component analysis and in transform domain, DCTav and Laplacian Pyramid are performed well.

In recent years, the concepts of Internet of things (IoT), wearable medical devices (WMDs), big data analysis, etc. has brought upon huge technological advancements in the fields of medicine and health care delivery. WMDs and sensors able to monitor physiological parameters can provide continuous physiological data, which in turns gives us a better idea about the condition of the patient. Such round-the-clock, vital-sign monitoring systems will drastically reduce the cost of health care by enabling early detection of diseases. With the integration of IoT, it is possible to relay such information in real time to the health care professionals and emergency responders. In this chapter, we will explore how WMDs along with IoT are being used in the detection and management of diseases such as epilepsy, hypertension, cancer, and migraines. It also explores some of the computerized tools available for diagnostics of these diseases.

Power system consist of large number of nonlinear load which causes power quality issues. Sensitive components like electronic devices, medical equipment working on power supply are vulnerable to power quality problems. Voltage sag, Swell, harmonics, noise are considered to be some important power quality issues. Voltage dips are the major disturbance affecting the power system. On load tap changers can be used to compensate voltage dip but it is not economical. Dynamic voltage restorer (DVR) is an alternate to conventional method of compensating the voltage. These are custom power devices. The main applications of DVR is to protect sensitive loads that are severely affected by fluctuations in system voltage.

The Phasor Measurement Unit (PMU) is a modern metering apparatus positioned on current power system to facilitate the accurate monitoring and control. PMUs are most precise and latest technique based on time synchronization which gives measurements of voltages and current angles at the buses and branches at which PMU is placed and branches which are connected to those buses, respectively. In addition, as a result of Ohm's Law, due to placement of PMU at a bus, adjacent buses also turn out to be observable. This entails that a system can be made observable with a smaller number of PMUs than the total number of system buses. Hence it is essential to find the minimal number of PMUs for entire system observability. In the present work, Power System Analysis Toolbox (PSAT) is employed for a comparative analysis of optimal placement of PMU using different methods such as depth first, graph theory, and simulated annealing. Different methods have been verified, compared and studied on southern region 21 Bus System Ethiopia.

This paper evaluates the performance of online scheduling for the objective of minimizing the flow time plus energy usage under dynamic speed scaling model and using arbitrary power function in data centers. An algorithm (Jyoti Algorithm) JAlg(sRPT, P∼∗((3Aj/ 2) + (1/ 8)2A)) is proposed which uses Shortest Remaining Processing Time (SRPT) for scheduling jobs and processes jobs at speed such that the consumed power depends on the queue length (i.e. number of active jobs), rather than the remaining work of active jobs. It is applicable in both the settings: clairvoyant and nonclairvoyant. The amortized potential function analysis is used to calculate the competitive ratio of JAlg. The competitive ratio of JAlg is (41/16) against an optimal offline adversary.

With increasing design complexity, as well as continued scaling of supplies, the design and analysis of power distribution networks poses a difficult problem in modern IC design. We propose several closed-form solutions for power distribution network optimization and analysis which explicitly take into consideration the mesh topology. An RLC model for on-chip power distribution networks is presented for array and wire bonded integrated circuits including inter block decoupling capacitors and impedances. One of the more common design techniques for power distribution networks PDN is the determination of the impedance & supply noise. Current-mode technique is used. By using this current mode technique noise and impedance, voltage is analyzed.

Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey can also help us to know about number of papers that are implemented to diagnose the breast cancer.

Now-a-days breast cancer has become one of the leading cause of cancer death among women. This cancer is caused mostly due to the lifestyle changes, avoiding breast feeding etc. Detecting breast cancer takes long time due to manual diagnosis. Even though there are many diagnostic systems are available still, it takes more time for proper classification. For detecting breast cancer, mostly machine learning techniques are used. This work deals with the comparative study of Relevance vector machine(RVM) which provides Low computational cost while comparing with other machine learning techniques which are used for breast cancer detection. The aim of this work is to compare and explain how RVM is better than other machine learning algorithms for diagnosing breast cancer even the variables are reduced.

Background: Now a day, data communication plays a vital role in day-to-day life. There is a strong desire to develop and implement more secure authentication protocol to protect secret information against security threats. Method: The paper proposes a Quick Response (QR) code Authentication Protocol (QRAP) using Visual Cryptography. Visual Cryptography (VC) is a model of cryptography split the secret into share images, which prevents the secret image from being altered by using the concepts of the cipher and can reconstruct the secret image by stacking the legitimate share images. Findings: The passwords for authentication are encoded as QR-codes and later encrypted into colour share images. Thus, the colour share images by itself convey no information, but when the shares are combined, the secret password can be revealed. The necessary is that the user needs to handle a device containing a QR-code reader, most probably a Smartphone. The experimental result shows that the proposed QRAP scheme provides secure data communication with less computational complexity. Applications/Improvement: This research work can give a way for providing authentication to all services which are related to electronic devices.

Indian Regional Navigation Satellite System (IRNSS) is a indigenous and autonomous satellite navigation system being developed by Indian Space Research Organization. IRNSS operates in two frequency bands namely L5 band (1176.45 MHz) and S1 band (2492.02 MHz). The Objective of IRNSS is to develop a regional navigation and positional accuracy system for Indian applications with 1500 kms. This paper describes a methodology for performance analysis of IRNSS receiver on a probe vehicle. A vehicle is equipped with IRNSS and GPS receiver and the data collected for every second throughout the test field. From the observed data an analysis has been done with vehicle speed, satellite visibility, precision and signal strength. The vehicle speed will not affect the signal strength and accuracy of IRNSS. The precision is consistent over the Indian geographical region when compared to GPS. Though the precision is better with GPS, it's not consistent with satellite visibility over the Indian geographical region.

The field of Real Time Authentication on videos follows rapid improvements in recent years because of the common counterfeits and illegal sharing of Digital media. Real Time Authentication for videos is becoming popular to secure the personal videos. People today have become well aware of the intrusions of their personal videos and are very reluctant to share on internet. This paper provides a wide survey of different Real Time Authentication algorithms and techniques and plug out their merits and demerits.

Peak-to-Average Power Ratio (PAPR) reduction is one of the main key factors in orthogonal frequency division multiplexing (OFDM) systems. From the various existing PAPR reduction techniques, Neural Network (NN) has been one of the efficient and powerful techniques in reducing the PAPR due to its good generalization properties with flexible modeling and learning capabilities. In this paper, we propose a new method that uses NNs trained on the active constellation extension (ACE) signals to reduce the PAPR of OFDM signals. It employs a receiver NN unit, at the OFDM receiver side for achieving significant bit error rate (BER) improvement with low computational complexity. To reduce the time complexity SFBC (Space frequency block coding) block is used in NN and multiple input and multiple output (MIMO) technique is introduced.

Diagnosing various diseases in medical field is very difficult even for medical expert. For solving this problem data mining was introduced. It discovers knowledge from the database. There are many subfields in data mining. One of the subfield is fuzzy logic. It is applied in many fields such as control theory, Artificial Intelligence (AI) and also in the field of medicine. This paper focuses on detecting the risk of breast cancer by using fuzzy logic. The dataset used in this work is retrieved from UCI machine learning repository. The aim of this proposed work is to detect the breast cancer by reducing the variables, so that it reduces the time taken for diagnosing the disease. The features were extracted by using one of the feature selection method called Linear Discriminant Analysis (LDA) and training is done by using one of the fuzzy inference method called Mamdani Fuzzy inference model. The results were evaluated by using the above model. It gave the result of 93%.

Life time and its quality can be enhanced with early diagnosis of osteoporosis. Osteoporosis is a state of having brittle and fragile bone which arises due to vitamin deficiency, tissue loss, hormonal changes, etc. Diagnosis of osteoporosis is very important and mandatory in current scenario. Now a days Computer Aided Diagnostic (CAD) is used to detect osteoporosis more efficiently. This osteoporosis can be efficiently detected by calculating various features like Bone mineral density (BMD), statistical features from various trabecular region such as hip, toe, elbow, etc. Analysis of trabecular bounding on digital calcaneus radiographic (DCR) images will help in efficient identification of low BMD. Bone Mineral density measurement can be achieved by various segmentation methods such as K-means, Fuzzy segmentation etc. In this paper we proposed a Regression Artificial Neural Network (ANN) classifier-based computer-aided diagnosis (CAD) system for accurate osteoporotic risk detection using digital calcaneus radiographic images. The evaluation of diagnostic capability of the proposed method is to spot areas of Low Bone Mineral Density at the calcaneus region. The proposed system has achieved the highest classification accuracy of 90%, sensitivity, specificity and positive predictive value.

Emerging applications in wireless communications and Software Defined Radio require robust and generalized decoders with a very good efficiency. This paper aims at introducing a novel and powerful method of implementing a decoder using Support Vector Machines (SVM) to exhibit good performance irrespective of the channel model. The method proposed also ensures a generalization in the design of decoder, which can be easily adaptable for any type of coding technique used. In addition, this method overcomes the demerits of the traditional decoders like Viterbi and other decoders using Neural Networks. The error correction codes like Hamming and Convolutional codes are considered for experimentation. Using SVM, which is a class of machine learning algorithm, this process is viewed as a multi-class classification problem and error correction is achieved in a simpler way. An extensive analysis with regard to the effect of channel and modulation techniques is also made and presented. The proposed SVM model is sufficiently cross validated and found to be an effective replacement for the existing counterparts.

Aligning Learning Outcomes (LO) to relevant portions of Learning Resources (LR) is necessary to help students quickly navigate within the recommended learning material. In general, the problem can be viewed as finding the relevant sections of a document (LR) that is pertinent to a broad question (LO). In this paper, we introduce the novel problem of aligning LOs (LO is usually a sentence long text) to relevant pages of LRs (LRs are in the form of slide decks). We observe that the set of relevant pages can be composed of multiple chunks (a chunk is a contiguous set of pages) and the same page of an LR might be relevant to multiple LOs. To this end, we develop a novel Lexico-Semantic Spatial approach that captures the lexical, semantic, and spatial aspects of the task, and also alleviates the limited availability of training data. Our approach first identifies the relevancy of a page to an LO by using lexical and semantic features from each page independently. The spatial model at a later stage exploits the dependencies between the sequence of pages in the LR to further improve the alignment task. We empirically establish the importance of the lexical, semantic, and spatial models within the proposed approach. We show that, on average, a student can navigate to a relevant page from the first predicted page by about four clicks within a 38 page slide deck, as compared to two clicks by human experts.