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Battery power has been a popular choice of delivering DC power to many portable RF systems including wireless sensor nodes/internet of Everything (IoE). An autonomously powered RF system of the orders of 100’s of µwatts is becoming increasingly popular in recent times because of integrated solutions in energy harvesting. The minimum sensitivity of an RF energy harvester and the targeted deliverable output voltage for a specific load resistance are dictated by threshold voltage of transistors used and reverse leakage of such transistors. A detailed look into the architectures of rectifiers can help tackle design problems in the RF-DC conversion process. So in this work we focused on the design of integrated RF energy harvesting by studying four important architectures of RF-DC converters. To achieve a fair comparison between these architectures, a matching network operating at 0.8 MHz is designed and applied for simulation. All of these converters are tested in LT-SPICE XVII using the same designed matching network and the rectifiers are implemented in 180 nm CMOS technology.

Vehicular delay tolerant network (VDTN) technology uses vehicles on the road as moving nodes to deliver data from source to destination using several intermediate nodes. Efficient data dissemination in VDTN is a difficult problem due to existing trade-offs between several metric variables such as delivery ratio, delay, and overhead ratio. Inclusion of important social network parameters (like community, social strength, trust, friendship, and selfishness) in computation of forwarding probability may help to improve the performance of a routing algorithm. However, different tuning of these parameters results in different outcomes for the metric variables. A proper balancing of these parameters may result in an optimized outcome solving the trade-off between the metric variables. Nonetheless, dependency of the variables on a number of social network parameters and their mutual trade-offs makes this a non-trivial optimization problem. In this paper, we propose a novel approach to optimize these trade-offs using multi-objective particle swarm optimization (MOPSO). The proposed approach provides a set of pareto-optimal solutions also known as non-dominating solutions. Further, based on the requirements of a target application scenario, a specific optimal solution out of the pareto-optimal solution set is delivered using artificial intelligence (AI) technique. The proposed methodology is simulated in a VDTN scenario using the opportunistic network environment (ONE) simulator and Matlab. Furthermore, based on the experimental results, an exhaustive analysis is provided about how the metric variables are affected by the involvement of the social-based parameters. The capabilities of the proposed approach are validated using a statistical comparative analysis of the results. In future, the outcome of the study may play a helpful role to decide the priorities of the network parameters while designing new data dissemination algorithms for VDTN.

Agriculture occupation has been the prime occupation in India since the primeval era. Nowadays, the country is ranked second in the prime occupations threatening global warming. Apart from this, diseases in plants are challenging to this prime source of livelihood. The present research can help in recognition of different diseases among plants and help to find out the solution or remedy that can be a defense mechanism in counter to the diseases. Finding diseases among plant DL is considered to the most perfect and exact paradigms. Four labels are classified as “bacterial spot,” “yellow leaf curl virus,” “late blight,” and “healthy leaf.” An exemplar model of the drone is also designed for the purpose. The said model will be utilized for a live report for extended large crop fields. In this exemplar drone model, a high-resolution camera is attached. The captured images of plants will act as software input. On this basis, the software will immediately tell which plants are healthy and which are diseased.

Internet of things is the present technology that permits us to control hardware via the internet. Using IOT system, we can link the physical devices and possess the ability to send or transfer data over a network with either human-to-human or human-to-system interaction. This paper explains how to control home appliances using Internet of Things. The microcontroller we are using for this project is Raspberry Pi. The Raspberry Pi is interfaced with Wi-Fi modem for transmitting and receiving commands over the internet. To switch loads, we are using relays. Once the user passes instructions over the internet to switch on the appliance, the raspberry pi processes these commands and operates accordingly. Liquid Crystal Display is used to display the status of the system. Hence, IOT based home automation setup allows us to control home appliances over the internet.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

In this paper, a sunlight-based hybrid electric vehicle is planned on MATLAB Simulink. In this a primary fuel source which sun based and a helper source which is battery is utilized to fabricate a bidirectional DC-DC converter. In General thought, an electric vehicle uses a battery that is charged from an external force gracefully, anyway sunlight-based PV modules are used to charge a battery by strategies for fascinating radiation from the sun and changing over it into electrical force (Photovoltaic Effect) by proposed procedure. The electrical capacity to batteries got from sun powered PV modules, which might be connected either in course of action or equivalent and charge regulators. To cross a conclusive force point in the sunlight-based board, additionally Maximum Power Point Tracker (MPPT) regulator is used [3]. The sun powered PV part is executed utilizing a fuzzy based MPPT strategy. In the control framework for bidirectional DC-DC converter, an ANFIS framework is utilized to diminish the blunders and give a DC yield to DC machine which speaks to the Electric Vehicle. The utilization of Solar gives limitless energy and diminishes the expense of different other options. The advantage of ANFIS and Fuzzy joined framework gives the best accuracies and proficiency in the framework as contrasted and conventional PI based which is a more calculative control framework. The yield voltage of bidirectional DC-DC converter is about 430v to drive the electric vehicle. The advantage of this plan is low count intricacy by utilizing ANFIS and Fuzzy based MPPT calculation for Photovoltaic module[3].

The demand of the construction industry has made the world to use the best materials which can reduce the time and cost of the project. The materials which are used nowadays should also be sustainable, can be customized as per the requirement of the client, should be good aesthetic wise and ultimately it should be long lasting. If it possesses all the above qualities, then it will be preferred and, it’s demand will be more. The construction fields nowadays use varieties of formwork materials and methods to speed up their construction. The formwork materials which are used will depend upon the type of buildings, whether the building is commercial, residential, factory or industrial or other type of building. The selection of kind of formwork is in it itself is big task and it effects the overall cost of the project. It is one of the major inputs which also affects the design of the building as well. This paper will discuss about what kind of formwork materials should be selected for different building typology.

The exponential growth seen in the past few years of internet users and its easy access through handy available devices, As a result of which the size of digital content has been growing very rapidly in recent years through messaging applications, posts on social networking sites, vlogs, blogs, online forums and other internet platforms. With the advancement of social media sites over the internet, one's post can be reached all over the world which means it can be used to influence people in both good and bad ways. Although social media has many advantages, people misuse social media to exploit other people. The anonymity offered by internet and flexibility afforded has made it convenient for users to socialize in an aggressive and disrespectful manner over the internet. Text contents are mostly used to perform suspicious activities. Hate speech is currently an interest in the domain of social media. So, one of the most difficult issues for NLP researchers and programmers is to develop an algorithm that can detect text with hate speech automatically and efficiently from its specific contents. So here, a classification based Machine Learning model is proposed to efficiently and automatically classify text into non-suspicious and suspicious categories based on its contents.

The routing in vehicular delay tolerant networks (VDTNs) does not presume an end-to-end connectivity from source to destination. One substitute is to use the real-time geographical information of vehicles to relay messages. However, if mobile destination is considered, then real-time information might not be generally available due to the sparse network density in VDTN. In this paper, a geographical routing approach based on the probability delegation replication (GR-PDR) is proposed. The functionality of GR-PDR is categorized into forwarder node selection phase and buffer management phase. The forwarder node selection utilizes the advantages of delegation replication approach to conquer the constraints of routing decisions and local maximum problem. Further, the buffer management is employed to prioritize packets to ensure delivery before the expiration of message lifetime. The proposed GR-PDR ensures that the message will be forwarded to higher quality node, resulting in a high delivery ratio. The approach utilizes the advantages of single-copy and multiple-copy forwarding approaches by avoiding redundant replication and enhancing delivery probability, respectively. The performance results show that the proposed GR-PDR outperforms in terms of packet delivery ratio, average delay and network overhead, when compared with the protocols of the same segment. Statistical analysis validates the effectiveness of the proposed protocol.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. Here providing an appropriate service is considered a major challenge in the heterogeneous environment. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. In order to provide optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. In addition to this, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. In addition to this, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

Objectives: To design and evaluate neural frequency compression method to improve speech intelligibility for Marathi language hearing aid users. Methods/Statistical Analysis: In Recurrent Neural Network - Frequency Compression algorithm (RNN-FC), classification and processing are two important stages. After segmentationof input speech into discrete frames. Features are extracted in terms of signal to noise ratio, Pitch, formant frequency and gain frequency spectral coefficient. Extracted features will classify into two segments for processing and improvement in SNR level. Based on classification sample data is divided in two categories; wanted and unwanted samples for processing. Findings: Extracted feature vectors, Training date rate are key performance parameter of RNN-FC method. Testing of RNN-FC was performed on Marathi spoken HA user. In regional Marathi language 14-15 consonants are located over frequency band 7-13.5 kHz. Proposed algorithm shows improvement of classifier with Min 94- Max 96% sensitivity, Specificity and Accuracy. Results reports improved recognition rate of Marathi vowel, Consonants and short words. Unwanted vowel, consonants process reduced from 5.67% to 3.56%. The inability to access the high-frequency speech contents in terms of speech and consonant recognition ability enhanced for Marathi HA users. Application/Improvements: Frequency Compression method is extensively adopted by researchers in which high frequency speech is compressed by certain compression factor which causes distortion at lower speech frequencies. Distortion occurred during processing will results in loss of information in Lower band of speech. This challenge is overcomes by using frequency compression approach with neural network classifier (RNN-FC). Keywords: Frequency Compression, Neural Network, Marathi, Speech

This paper presents the hybrid method of medical image fusion, and a hybrid method is a combination of PCNN and particle swarm optimization. The pulse coupled neural network is a dynamic neural network model, and the rate of recognition is very high. The particle swarm optimization is a swarm-based algorithm. The nature of the algorithm is iterative and reduces the unwanted noise factor during the process of fusion. For the extract of feature, coefficient applied contourlet transform, contourlet transform is rich feature extraction process in image processing. The method of contourlet transform is subband decomposition and directional feature coefficient with Laplacian pyramidal transform. The proposed algorithm increases the value of mutual information of the fused image. The experimental analysis process uses two types of images: computed tomography (CT) and magnetic resonance imaging (MRI). The proposed algorithm simulated in MATLAB software and tested with reputed medical image dataset. The process of evaluation measure standard parameters and compare with CTGA, and PCNN algorithm. Our experimental results show better performance instead of CTGA and PCNN.

copy>978-1-7281-7741-0/20/$31.00 ©2021 IEEEDuring this Pandemic time a day without Internet is unimaginable. At present the most widely used non-cellular wireless technology is Wi-Fi (Wireless Fidelity). It is based on radio wireless local area networking of devices based on IEEE 802.11 standards operating at the frequency range of 2.4GHz (12cm) UHF (Ultra High Frequency) and 5.8GHz (5cm) SHF (Super High Frequency) is probably the most popular used non-cellular wireless technology. Now imagine a world where each LED Light in and around Hospitals can connect us to high speed wireless internet with a simple flickering of light which is Li-Fi or Light Fidelity which is a new way of sending data wireless. As Doctors are the front line soldiers against Covid-19, This paper which explains about the prototype for transmitting the data by using different body sensors such as Heart Beat Sensors, Temperature Sensors or Pressure sensors from Li-Fi Transmitter to Li-Fi Receiver by using simple LED light as a platform to provide higher speed, secure, safer and greener light based radiation-free communication network compared to the existing modes of Wi-Fi Communication or Telecom tower- based Communication or LAN network for Covid-19 patients and the Medical Professionals.

With the advancement in technology and digitalization of society, the number of smartphone users has increased drastically. According to [1], there will be 5.3 billion Internet users by 2023. Due to this, Wi-Fi penetration rate will also increase. For tracking smartphones present inside the vehicles, the uniqueness of Wi-Fi Medium Access Control (MAC) addresses helps a lot. Road traffic monitoring systems can make use of this information for estimating travel time, average speed, and traffic flow in the areas of interest. This article focuses on utilizing Wi-Fi based road-side sniffers to listen to Wi-Fi probe signals broadcasted by the smartphones present inside vehicles for estimating traffic stats of road segments. Hopping Interval (HI) is the rate at which Wi-Fi radio of the sniffer is changed to one of the available channels in ISM band. A channel hopping algorithm is proposed, which adjusts HI of the sniffer according to the current traffic conditions. The proposed algorithm reduces the estimation error and increases the robustness and efficiency of the traffic monitoring system. To validate the algorithm, simulation experiments have been performed in Indian traffic scenarios, and results show that the proposed algorithm is better than static channel assignment on the sniffer with an improvement of 4%. Also, estimated travel time and the ground truth values are found out to be closely related, which assures the suitability of the Wi-Fi based road-side sniffers for traffic monitoring.

Higher technical education is one of the indicators of national growth. Higher technical education of the country is responsible for its global progress in technology and standard of living as technical advancement helps to make the country economically progressive as the availability of quality technical staff attracts global entrepreneurs who, in turn, increase the revenue of the nation and therefore the higher technical education system is the backbone of the country's economic progress. In the present research paper, India's higher technical education system is being studied, which is currently undergoing uneven expansion due to the implementation of the various policies responsible for this plethora of expansion. The main objectives of this study are to improve the quality of higher technical education by developing a dynamic system model by incorporating some of the important parameters of higher technical education system stakeholders to test various long-term policies that can improve the quality of higher technical education.

This chapter presents a new single-phase multilevel inverter topology. This topology requires fewer components in comparison to conventional multilevel inverter topology such as diode clamped, flying capacitor, and cascade h-bridge inverter. In this topology, no extra clamping diode and no extra clamping capacitor are required for operation. This chapter also presents the control and modulation techniques. These techniques applied on this proposed MLI topology and simulation result are calculated with the help of MATLAB/SIMULINK software and are compared on the basis of THD with over and under modulation index.

Electrocardiogram(ECG) is a valuable clinical signal, which is widely used to identify the cardiovascular diseases. However, it remains a cumbersome process to manually evaluate the ECG signals because of smaller variations in its physiological features in normal and abnormal cases that too when there are a huge number of cardiac patients to examine. In such a scenario, automatic classification of ECG signals can provide an ease to the doctors to make a correct diagnosis of a particular disease. This work proposes a classification model to classify the ECG in five different classes based on their morphological features. Instead of using manually designed features as most of the existing ECG classification works do, we have extracted data-driven non-linear features using convolutional neural network. The 1D-CNN model architecture is based on three convolutional, max pooling and dense layers which automatically extracts distinguishable nonlinear features from the ECG signals and automatically classify them into five different classes: Non-ectopic beats (Normal Beat), Supraventricular ectopic beats, Ventricular ectopic beats, Fusion Beats and Unknown Beats. The proposed algorithm was assessed using open-source database of MIT-BIH, which is based on 47 subjects. After 5-fold cross-validation, the presented algorithm achieves an accuracy of 97.36% and f1 score of 99.83%. It is a simple yet fast performing model that is implementable on e-healthcare-based devices for remote heart diagnosis of patients.

In the vehicular delay-tolerant networks, (VDTN) association between the sources to the destination is not always achievable at any required period. Consequently, the carrier node saves the message in its intrinsic buffer until an opportunity occurs for forwarding. Fix nodes facilitate in message storage and message relaying. It also helps in improving the performance of VDTN. Considering the mobility of nodes, the bit error rate is high whereas the bit error rate in fixed nodes is comparatively low. In VDTN, bit error rate is not considered in most of the routing schemes. In this article, a composite routing approach is introduced to conquer aforementioned issues. Some features of vehicular ad hoc networks (VANET) are associated to PRoPHET routing protocol for VDTN. The propagation models of VANET are executed for mobile node communication and without it for VDTN. The effect of environmental hindrance is also considered, and this can be either positive or negative. This makes the composite routing approach two-dimensional and much competent. The simulation and performance analysis of the composite approach is done via opportunistic network environment (ONE) simulator. Results show that the composite routing approach outperforms the PRoPHET wrt. delivery ratio and average delivery delay.

“Vehicular delay tolerant network (VDTN)” appears as a challenging network. VDTN is characterized by the unavailability of prior terminal path amidst the source and the destination nodes. Therefore, routing is one of the pivotal affair in such networks. To transmit a packet, VDTN uses a “store-carry-forward (SCF)” paradigm. The packets are stored in the buffer of a node and are scheduled from that buffer only. Hence, management of node’s outgoing buffer is important. This paper instigates a routing mechanism that limits the count of copies to be distributed in the network along with scheduling the packets based on their priority. The distribution of copies is limited by using remaining message lifetime and predicted time to delivery. Also, packet’s priority is based on its type, i.e., emergency packets, traffic related packets, and general purpose packets. The proposed routing algorithm is estimated on “opportunistic network environment (ONE)” simulator. Evaluation results demonstrate that the proposed routing algorithm outruns existing VDTN routing approaches.

This paper presents an ultra-low-power solar energy harvesting system with a power management module (SEHS-PMU) for the Internet-of-Things (IoT) applications. The voltages generated are suitable for the IoT sensors used for smart cities as well as Internet-of-Medical-Things (IoMT) based biomedical applications. The SEHS-PMU is harvesting energy from the solar cell, and it can provide regulated voltages of 3.3 V, 1.8 V, 1 V, and 0.5 V. The harvesting mechanism leads to battery-less IoT, and is safe for the humanity and environment. Charge pumps (CP) are used as a boost and buck converter, which is suitable for monolithic integration. Low drop-out regulators (LDOs) are used for regulating the loads. A digital controller controls the selection of loads as per requirement for saving the power. The SEHS-PMU is designed in CMOS 180 nm technology library.

These days the web applications are profoundly valuable and amazing for utilization in many fields, for example, fund, web-based business, human resource, etc so it must be all around verified. Applications may be susceptible, hence get abused by assailants to take the client's credentials. Cross scripting (XSS) is a significant risk to web applications as it is the fundamental and simple assault on web applications. Xss layout the platform for assaults such as Cross-Site Request Session Hijacking, Forgery ...etc. An XSS assault is an infusion assault in which vindictive content is infused into the site by the assaulter in the user-side in the client's program or on the server-side in the database. The malicious code is basically a JavaScript code and is performed on an input field in web applications. Kinds of XSS assaults are i.e. nonpersistent (or reflected) XSS, persistent (or stored) XSS, and DOM-Based vulnerabilities. The main cause of Cross-site scripting (XSS) weakness is failing to sanitize user inputs implanted in web pages. Despite adopting secure coding techniques and using vulnerability detection tools XSS remains in many web applications because the method is quite complex, improper implementation of methods, lack of knowledge of vulnerabilities. In this paper, we have surveyed on XSS attack, causes, and approaches for the prevention of stored based XSS and DOM based XSS.

This paper presents a multiband microstrip antenna that can be used in L, S, C and X band application of microwave frequencies. The multiband effect brought by the use of closed C shape conductive strip around the patch. Microwave substrate of size 30*30mm having dielectric constant 4.4 is used for an antenna design. At resonating frequency 5.3GHz the No. of bands obtain is 6 with a gain of 23.8 dB.

This is the survey paper which includes abstract of multiple references for better understanding of the topic. This paper includes brief overview about predictive maintenance and some of its application were its used as well as older research is also taken into account which discuss various methodology for Predictive Maintenance using Multiple Classifier Approach by Machine Learning. Predictive Maintenance is introduced into various Industries and sectors. It helps to predict the condition of equipment of machine which are already in use this tells whether the maintenance is required or not. This technique ensures that cost saving has done, as compare to regular maintenance where unnecessary replacements have been done without proper utilization of resources. The main advantage of this Technique is that it can allow timely and convenient scheduling of corrective maintenance and can prevent unexpected failures. If in advance we know which that part needs maintenance we can plan and work accordingly. This is continuous and iterative process the model or algorithm continue to evolve as it used.

Microstrip patch antenna(MPA) gained acceptance worldwide in the field of communication. It offers several advantages such as light weight, low profile, easy to design & fabricate with circuit elements. It consists of a radiating patch imprinted on a substrate placed over a ground plane. The minimum dimension of patch antenna is of the order half a wavelength. But, their bandwidth is narrow. Due to the advancement in technology in recent years, the requirement of reducing the size of patch antenna was felt. In this review paper, the researcher has discussed some of the techniques that are being used to miniaturize the size of the patch antenna. These include slot-cutting in radiating patch, re-shaping ground plane and antenna, shorting and folding of patch antenna and use of meta-materials. Also, its major attributes and limitations are highlighted along with their effects on the antenna performance

Increasing demand of power has led to exploration of non-conventional sources of energy. Solar energy has been the most exploited source of energy in this regard. Effectiveness of power utilization depends on the power conversion from AC to DC. Further improvement in utilization of DC power depends on the DC-DC conversion steps involved. The planned methodology offers a study of power decoupling method while no further power semiconductor are used for a DC to 1-ɸ AC device. 1-ɸ voltage source bridge inverters have primarily two drawbacks, that's reduction in DC voltage usage and disparity in power among the incoming and outgoing sides. These difficulties have been looked upon and a topology has been implemented that uses just a film capacitance beforehand buck-boost convertor that is linked to the voltage-source bridge inverter. Reenactment and preliminary outcome confirmed the credibility of the planned power decoupling technique on the 2-stage 1-ɸ bridge inverter by buck-boost convertor.

The objective of the paper is to design an antenna with multiple resonant frequencies. The proposed antenna resonates at 12 different frequencies with a centre frequency at 5.3GHz. Microstrip patch antenna with conducting stips is used to achieved this purpose. Microwave substrate of size 30mm×30mm having a dielectric constant of 4.4 is used to design the antenna. The desired frequencies are obtained by cutting slots in the patch and impedance matching is done by providing defective ground. The multiband antenna is designed using high frequency structural simulator (HFSS) software with return loss of about −14.34dB and excellent gain of 20.51dB at 5.3GHz.

Structural damage and its extent can be detected by vibration-based techniques to avoid failure or to minimize maintenance. Among different damage identification techniques, modal curvature approaches are widely researched and applied one. On the contrary, wavelet transformation (WT) methods are gaining popularity in damage identification of real life buildings because of their suitability for non-stationary signals and non-dependency on baseline data. This paper presents a novel approach utilizing complex continuous wavelet to effectively locate change in physical properties of reinforced concrete (RC) buildings by virtue of variation in frequency and mode shapes due to small change in mass and stiffness. In this paper, the effect of variation of mass and stiffness of a building on the modal parameters is established analytically using theequation of motion for a multi-degree freedom system under forced vibration condition. A 3-D finite element model was developed for predicting the modal frequencies and mode shapes of the scaled down six storey RC building and the effect of addition of mass on a particular level of structure on the modal parameters was studied. Further, acceleration time histories were recorded with variation in mass on 3rd story of building using wireless tri-axial accelerometers and the time histories were processed to arrive at Curvature Damage Factor and wavelet coefficients for identification of the additional load on the particular floor. Vibration responses from all floors of RC building in ambient and loaded conditions were analyzed for frequency response spectra (FRS). Mode shapes were drawn for unloaded case and loaded cases. It was observed that the modal frequency of building decreases with the increase in mass at floors. It is observed that CDF approach could detect the change in mass in both numerical and experimental results. However, CDF algorithm could not detect the addition of load in case 1, 2 and 3, i.e. when load was less than 25 kg, i.e. only 2.6% of floor mass (960 kg). The acquired data for the above stated load cases were analyzed using complex Gaussian ‘cgau5’ wavelet in MATLAB toolbox to determine the singularity in the signal in terms of wavelet coefficient modulus. It is observed that the WT approach is able to precisely locate the change in physical parameters of the RC model building. However, it is seen that additional load could not be detected in case where only 9 kg, i.e. 0.93% of the total floor mass, was placed on 3rd floor. From the research work, it is observed that CDF technique is inefficient in damage detection and always demand prior baseline information, which is usually difficult to obtain in practice. However, the wavelet transform-based approach more accurately detects the location of change without relying on intact state vibration data.

To bridge the existing technological gap between age-old traditional methods and modern sophisticated cranes for material handling, the Central Building Research Institute (CSIR-CBRI) Roorkee previously developed mini climbing crane with a lifting capacity of 1000 kg at a maximum loading radius of one meter. The developed crane exhibits considerable saving in construction time besides a large saving in manpower. This machine has been awarded the best technology NRDC Award. In this paper, the kinematic model of the mini climbing crane has been developed. Crane workspace and boom tip trajectory are evaluated using MATLAB programming by varying different geometrical and motion parameters. Modal analysis of the full-scale mini climbing crane is performed for its overall stability. Further finite element model (FEM) of the crane shows the load-carrying capacity of the existing design of the mini climbing crane. The full-scale 3D CAD model of the crane is used in the finite element analysis (FEA) using ANSYS software. The study presented in this paper will help further in design and development of a newer version of mobile crane for fast civil construction work.

This paper presents an application of wavelet analysis for damage detection in steel structures. Wavelet analysis is a new mathematical and advanced signal processing tool that can be used to analyze the vibration data and the damage in the structure can be identified. It is found that spikes in the wavelet occurs either by damage or striking on the structure. The observed spikes in wavelet pattern are used for damage detection in multistory structures. A software application is developed that can process up to six sensors data and can locate the exact location of the damage. The application of Continuous Daubechies (Db8) wavelet in damage identification proved to be more robust in detecting the damage location. The experiments were conducted on a five-story steel structure at the CSIR-CBRI, Roorkee, India to verify the proposed method using two types of accelerometers.

Science Gateways have gained wide use by research community by providing a rich and easy to use web interface to access and utilize complex cyberinfrastructure. Science Gateways are consuming an increasing proportion of computational capacity provided by XSEDE. A typical approach employed by Science Gateways is to use a single community account with a compute allocation to process compute jobs on behalf of their users. The computation usage for Science Gateways is compiled from batch job submission systems and reported by the XSEDE service providers. However, this reporting does not capture and provide any information about the user who actually initiated the computation, as the batch systems do not have record this information. To overcome this reporting limitation, Science Gateways utilize a separate pipeline to submit job-specific attributes to XSEDE, which is then later co-joined with batch system information submitted by the Service Providers to create detailed usage reports. In this article we describe improvements to the Gateway attribute reporting system, which better serves the needs of the growing number of Science Gateways by providing their operators a simpler and versatile interface to easily report a richer set of attributes and ultimately publish this information via XDMoD that enables various stake holders to assess resource utilization and their potential impact.

In spite of modernization of humanity, in emergency situations like after any natural calamity our infrastructure-based network backbone gets affected first. Disaster relief volunteers face the immense pain to set up a communication system, due to the failure of telecommunication infrastructure and long-standing power. Smartphones are the most preferable devices, operating and acting in delay-tolerant mode through Bluetooth/Wi-Fi, for setting up a peer-to-peer network enabling communication in post-disaster network. It is now popularly known as delay-/disruption-tolerant network (DTN). In this network, a node can misbehave and fail to establish a route or direct the data to neighbor node due to its malicious activity to decrease the performance of overall network. If a node suddenly drops messages after receiving, then it is known as black hole node. Black hole nodes do not forward any messages and can also destroy the message forever. A node may also act as a partially black hole node with the character of selective dropping. However, black hole nodes can hamper the overall performance of the network. In this paper, we have proposed a method to detect and avoid the malicious nodes forming black hole attack in DTN. We also evaluate the scheme using ONE simulator.

In this paper, a time-optimal feedback solution to the game of two cars is obtained for the case where the pursuer is faster and more agile than the evader. It is shown that the saddle point strategies for both the pursuer and the evader are coincident with one of the common tangents from the minimum radius turning circles of the pursuer to the minimum radius turning circles of the evader when the evader is distant enough from the pursuer. Four valid tangents are identified using geometry and a $2\times 2$ matrix game is formulated corresponding to these tangents. The solution of this game, at each instant, provides feedback strategies for the pursuer and the evader. The computations required to implement this law are insignificant and hence the proposed feedback law is suitable for real time implementation.