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With the dawn of the COVID-19 age, the communication industry has witnessed a large spike in users as home networks, workplaces and even conferences have gone online. This has led to a rise in the number of victims of cyber network attacks due to lack of ample security measures being taken in most network environments. Hence the introduction of Intrusion Detection Systems (IDSs) is proven to provide an increased security level. Machine Learning (ML) algorithms have been put into extensive use in tasks of intrusion detection. An ML technique that adds to the performance of standard IDS is the Support Vector Machine (SVM) algorithm, owing to their decent generalization nature and the capability to surpass the barriers of dimensionality. The objective of the project is to determine and compare the performance and accuracy of several ML algorithms like k-means clustering, SVM and KNN. The data set used to derive these results is "kddcup99", which contains 41 features. Data preprocessing is the first step towards achieving this goal, by performing feature extraction, followed by calculating the variance of features. This facilitates the filtering of relevant features from the non-linear dataset. Final objective is to separate the dataset into dissimilar classes based on the attack type faced by the network.

COVID 19 disease rooted in China, spread across other parts of the world and became a devastating pandemic. The detection of COVID-19 has become a crucial task in the medical sector because of the soaring cases and the paucity of pharmaceutical supplies for detection. Considering the urgency, an immediate auxiliary automatic detection system is required for early diagnosis of the disease and helps the affected patients to be under immediate care. In this work, we aimed to propose an automatic detection system based on lung X-ray images, as radiography modalities is a promising way of faster diagnosis. In this work, we built a machine learning model considering X-ray images taken from publicly available data sets of 2000 images. The relevant features from the images were taken for building the model, prior that proper segmentation was applied to the X-ray images. The X-ray images are prone to noise and spatial aliasing which leads the boundary to be indistinguishable, so proper image segmentation is required Comprehensive validation has been performed on different segmentation techniques, among those, Sobel demonstrated an accurate result, which is not only effective in detecting edges but also good in removing noises within the image. Further, the preprocessed image is fed to a support vector machine (SVM) model, which accomplished the maximum classification accuracy of 99.17%, also SVM achieved precision, recall, and F1 score of 99.24%,98.13%,98.68% respectively in predicting the COVID-19 versus other pulmonary diseases. Taking the advantage. the model can be helpful to medical persons that can be used as an initial screening of individuals.

Agriculture is an important sector for self-sustainability and plays a major role in a nation's economy and growth. Lack of timely identification of plant disease may result in huge loss in yield and in the economy. The objective of the research work is to support a large community of farmers particularly involved in paddy farming to understand and predict the disease affected to the crop. This research work demonstrates the robustness of classifying the paddy leaf disease using deep neural networks. Pre-processing techniques such as data augmentation and median filter have been applied to the dataset to avoid overfitting and to improve the model performance and accuracy. A model has been generated and analyzed its performance using deep learning approach. Also, the feature extracted, and preprocessed data set was fed to several models and analyzed their performance using various accuracy metrics.

In recent years, the higher educational institutes trough out the nation has stated incorporating structural facilities to become user-friendly for people with special needs. Still, campuses lack a system that can provide easy navigation for visually impaired people. Through this paper, we propose a system named "Edge-eye ", a campus navigation system for visually impaired people. The system requires the user to carry a smart cane. The cane will be attached with a camera device that will be reading the name boards or location identifiers the person comes across. From that location identifier, the system will detect the person’s location as previously set in the system. Once the user provides a destination point, the system will calculate the shortest path to the destination and give an audio output to the user. The major peculiarity of the system is that it is an independent system and works without the aid of the Global Positioning System. The system has been implemented in a controlled environment and found to be convenient for the users.

In today's world as vehicles are increasing on the roads, reducing road accidents is a major concern. Neglecting the rules while driving is a major cause for accidents. In this scenario, this paper proposes a suitable method to reduce the road accidents to a great extend due to over speed. Unlike the existing methods, which controls the speed of the vehicle at a constant rate, this work proposes an integrated approach to effectively control the speed of the vehicle at varying rate based on the maximum permissible speed limit of the protected or restricted zones such as hospitals, schools and crowded areas. A passive RFID integrated with GPS locator in the vehicle provides a promising solution to control the speed of the vehicle based on the allowable speed limit of the particular location. A prototype resembling an electronically controlled road vehicle with customized speed limits is used for our work. RFID reader inside the vehicle captures the unique encoded signal from the RFID tag placed at the speed limit zone and automatically controls the speed of the vehicle, when the vehicle approaches the proximity of RFID tag. The work also simulates speed control through GPS data which overcomes the limitation of RFID, and also proposes a solution to enhance the security of RFID tags.

This research work focuses on identifying a specific hand gesture from the given EMG signal, acquired by sensor-based band. Surface EMG and machine learning techniques are used for the identification and classification purpose. The raw EMG signal captured using the sensor is initially passed through suitable preprocessing steps to avoid the noise artifacts. Followed by this, 8 different time-domain features are collected from these raw EMG signals, using which a feature matrix is created. SVM and KNN are the machine learning classifiers used here. The entire system is implemented in MATLAB 2019a. Using these methods, a promising accuracy of 93% is obtained through KNN and an accuracy of 83% using SVM.

The objective of this work is to choose the best tool for diagnosis and detection of Hepatitis as well as for the prediction of life expectancy of Hepatitis patients. In this work, a comparative study between various machine learning tools and neural networks were carried out. The performance metric is based on the accuracy rate and the mean square error. The Machine Learning (ML) algorithms such as Support Vector Machines (SVM), K Nearest Neighbor (KNN) and Artificial Neural Network (ANN) were considered as the classification and prediction tools for diagnosing Hepatitis disease. A brief study on the above algorithms were performed based on the prediction accuracy of disease diagnosis. All the ML algorithms were implemented and validated using MATLAB software.

In this epoch of technology, where recommendations are the backbone of E-commerce and Media-services, one of the extensively used approach for implementing recommender systems is Collaborative Filtering. This approach recommends items by identifying users who share similarity in rating the items by observing the user-item ratings matrix. In the real-world, users only rate a limited number of items from the catalogue leading to high sparsity in user-item ratings, which makes it difficult to identify similar users and recommend items accurately. To handle sparsity in user-item matrix, which is one of the major challenges encountered while implementing recommender systems, the paper proposes a technique of Collaborative Filtering using Regression to predict the ratings of unrated items on the basis of the user's history of ratings and similarity to other users, combined with - a nature-inspired algorithm called - Grey Wolf Optimization (GWO) algorithm applied to optimize the loss function. Personalised recommendations can be provided to the users for those highest predicted ratings. The proposed technique is observed to perform well in highly sparse data scenarios in terms of accuracy, user coverage and different variants of hit rate.

MIMO-OFDM is used to increase the capacity in wireless communication in the present scenario. For coherent detection, the performance of a MIMO-OFDM system greatly depends on the channel estimation. In this paper, we made an investigation on the performance of channel estimation based on superimposed pilots and null subcarriers. This leads to two main advantages, cancel the mutual interference between OFDM symbols and the second is accurate channel estimation. The channel estimation is performed in a Rayleigh fading channel using Least Minimum Mean Square Error (LMMSE) algorithm. Results show that channel estimation based on null carriers and superimposed pilot insertion gives better performance than conventional MIMO-OFDM systems.

The detection and recognition of road speed limit signs is an important task in advanced driver assistance system (ADAS). The speed limit signs are important in informing the driver about allowable speed in a particular area. It increases the safety, as it provides information about the circumstances of the road. Different systems are being implemented by the government authorities to prevent accidents due to over speed. Here the proposed system provides efficient detection and identification of speed limit signs. The system operates in the following way: first it involves the detection of the sign board and then performs segmentation followed by geometric detection. Hough transform algorithm is employed to detect the sign board with saliency based approach. Second the system detects the characters from the extracted sign board. Finally it involves recognition of speed limit sign using Naive Baye’s employed classifier. The algorithm is successfully tested and shows 94% accuracy.

The Finite Impulse response (FIR) filter in direct and transposed form based on pipelined multiplier array is introduced. The proposed scheme is the folded FIR filter based on pipelined carry-save and carry-propagate multipliers. Initially, the Multiplier-accumulator is pipelined at the bit-level based on carry-save and carry-propagate multiplier and then folded the FIR structure, which can avoid the clock skew problem in synchronous circuit. Folding also reduces the hardware complexity. But it has the disadvantage of reducing the sample rate even though it reduces the hardware complexity. Pipelining of the folded FIR structures in direct and transposed form FIR filter increases the throughput of the filter. Verilog and Xilinx implementation of folded array FIR filter design based on pipelined carry propagate multiplier and pipelined carry save multiplier, in both direct and transposed form has been carried out in this work. These proposed schemes are then compared with each other based on the complexity of hardware. The most efficient design among the proposed designs can be selected on the basis of the hardware complexity.

Most courses on Data Science offered at universities or online require students to have familiarity with at least one programming language. In this paper, we present, “Milo”, a web-based visual programming environment for Data Science Education, designed as a pedagogical tool that can be used by students without prior-programming experience. To that end, Milo uses graphical blocks as abstractions of language specific implementations of Data Science and Machine Learning(ML) concepts along with creation of interactive visualizations. Using block definitions created by a user, Milo generates equivalent source code in JavaScript to run entirely in the browser. Based on a preliminary user study with a focus group of undergraduate computer science students, Milo succeeds as an effective tool for novice learners in the field of Data Science.

Most of the prevailing system to control vehicle speed has a fixed speed control mechanism, which is not sufficient in places of high alert. The objective of this paper is to reduce the speed of the vehicle automatically in comparison with the maximum permissible speed limit of particular location. The Real Time Automatic Speed Control Unit we proposed is highly driver friendly and the mechanical systems do not lose their functional integrity. The controlling part incorporated in this system fully avoids any kind of human intervention, and integrating both mechanical and electronic systems. In this prototype, the zonal position and the associated maximum speed limit is collected using mobile GPS. The algorithm incorporated continuously monitors and compares the speed raised by manual acceleration and the maximum permissible speed of the particular location. Based on this, the speed of the motor is brought down to the speed limit assigned to that particular zone. The hardware complexity of the prototype presented in this paper is less since the speed of the vehicle motor is measured using an IR sensor and controlled electronically through PWM technique. LabView simulation software is used for developing virtual model of the system that enables embedding the prototype model directly on the vehicle. For testing, the prototype modeling was developed on Arduino board.

Orthogonal Frequency Division Multiplexing (OFDM) is a pertinent multi-carrier modulation approach which is more immune to frequency selective fading. In 5G waveform, in order to reduce the traffic in OFDM based technology, it is significant to re-size the bandwidth. Consequently, a spectrally localized waveform technology called Filtered Orthogonal Frequency Division Multiplexing (F-OFDM), which is primarily an approach to sub-band based filtering is introduced. Each of the different sub-bands can be handled according to the traffic scenario. This work focusses on analyzing the performance of Filtered OFDM and OFDM in terms of Power Spectral Density (PSD) and Bit error rate (BER). The spectral efficiency in F-OFDM is increased by the reduction of out-of-band (OOB) emission. Simulation for the performance analysis of OFDM and F-OFDM in terms of PSD and BER have done in MATLAB. The working of OFDM has carried out in Verilog and the performance of all components is verified by giving different inputs.

Radiation dosimeters based organic thin film transistors have been developed using zinc (II) naphthalocyanine and zinc (II) tetra tert butyl-2,3 naphthalocyanine. The present investigation deals with the optical characteristics of both organic transistors. The optical characterization of as-deposited thin film transistors and thin film transistors exposed to various levels of γ radiation dose clearly show that the optical band gap decreases with increase of dose upto a certain level. The observed linear response between the absorbance at the center of the Q band and the γ radiation dose validated the use of both zinc (II) naphthalocyanine and zinc (II) tetra tert butyl-2,3 naphthalocyanine based organic thin film transistors for room temperature γ radiation dosimeter.

The FFT processor is an essential part utilized for implementing the frameworks of OFDM. Due to its computational necessities, it involves large area and devours high power if implemented in hardware. In this paper, a low power and area efficient pipelined FFT processor using higher radix architecture and folding transformation is presented. The implemented low power FFT has a simple structure as that of radix 2 while utilizing the advantages of higher radix architecture. Higher radix algorithm minimizes the number of multiplication with twiddle factor which in turn reduces the large power utilization in FFT. Radix 22 multipath feedforward architecture is proposed in this paper. As various sequence are computed parallel, a high throughput can be achieved. Hardware resources needed for the presented architecture is much less than the multipath feedback architecture. Reduced chip area and lower power consumption are the merits of this FFT processor. Verilog coding for designed architecture is simulated and synthesized in Xilinx ISE Design Suite 12.1.

Organic field effect transistor using copper phthalocyanine and zinc phthalocyanine as a source material are synthesized and investigated for Y-radiation dosimetry purposes. Samples are fabricated using thermal evaporation technique. The present investigation includes the studies on characteristics of both copper phthalocyanine and zinc phthalocyanine based organic transistor. The linear response to Y-radiation in the optical properties of copper phthalocyanine and zinc phthalocyanine were observed upto a certain level. The observed response suggest that copper phthalocyanine and zinc phthalocyanine based organic transistor can be considered as effective materials for room temperature Y-radiation dosimeters.

In wireless communication, high data rate transmission can be achieved through Orthogonal Frequency Division Multiplexing (OFDM) which has emerged as the effective multicarrier technique. OFDM systems possesses merits in terms of spectrum efficiency, reduced intersymbol interference and multipath fading mitigation. The major challenge associated with an OFDM system is high Peak-to-Average Power Ratio (PAPR). This paper focuses on efficient lowering of high Peak to Average Power Ratio (PAPR) of OFDM, based on principal component analysis and Discrete wavelet Transform (DWT) based compression technique. Initially, OFDM symbol is pre-processed using PCA (Principal component analysis) to reduce PAPR, and then further lowering is done using DWT Compression. This results in a remarkable reduction of PAPR with the reduction of bit error rate. Simulation results prove that the proposed method provides less PAPR compared to normal OFDM. This paper delivers the design and simulation of various modules in an OFDM receiver along with PAPR reduction techniques. Simulation of each modules of an OFDM receiver is carried out using Xilinx ISE. The verilog HDL is used to program all the components. The final simulation results for PAPR are evaluated using Matlab tool.

To fulfill the requirement for dynamic execution of customer's applications in cloud, efficient VM (virtual machines) forecasting techniques are required. Current researches are unable to accurately predict VMs usage for user's applications. Hence, we need a mechanism to overcome this problem so that VMs in cloud environment do not suffer from being unutilized. We propose a Bayesian model to determine VMs requirement for applications run in the cloud environment on the basis of workload patterns across several data centres in the cloud for different time interval during days of the week. The model is evaluated by considering CPU and memory benchmarks. The model is evaluated by using SamIam Bayesian network simulator and Benchmark traces collected from CloudHarmony benchmarking services. The simulation results indicate that the proposed model involving random demand scenarios provide insights into the feasibility and its applicability to predict the VM and its utility for customer applications, which helps in proper capacity planning. Further, it is able to predict VMs in Cloud environment with accuracies in 70% to 90% range, as compared to existing prediction models.

Effective utilization of resonating structure in RFID tags is studied in this paper. Petal shaped semicircular resonating structure is direct coupled with the strip line. For enhanceing the characteristics, the single base resonator is modified. Useing ANSYS HFSS the resonator structure is optimized for performance analysis. Validation of the optimized resonator is done, both the measured and simulated results are found to be in compromise with each other.

Pattern classification and data clustering has emerged as a potential mechanism that classifies data elements based on respective feature homogeneity. Although K-Means clustering has exhibited appreciable performance for data clustering, it suffers from poor optimality when dealing with multi dimensional datasets. In this paper, an evolutionary programming based Bisecting K-Means algorithm called Canonical Genetic Algorithm based Bisecting Clustering (CGABC) has been developed. The proposed model incorporates min-max normalization based feature normalization of the high dimensional data sets, which is followed by T-Test analysis. The fitness value has been assigned based on inter-cluster (heterogeneous distance) and within-cluster (homogeneous distance) distances. The conventional GA has been modified by applying multistage reproduction process, enhanced crossover and mutation. By incorporating the optimized cluster center information, the Bisecting K-Means clustering has been performed, which has exhibited optimal solution for highly accurate and efficient clustering with high dimensional data sets.

One-dimensional (1D) carbon chains or carbynes consist of unique electronic properties that are highly suitable for electrical sensing of biomolecules such as DNA based on conductance change. In addition, the single atomic width of a carbyne enables the possibility of ultra-high spatial resolution in the measurement of individual DNA bases when compared to other carbon-based nanostructures such as graphene and carbon nanotubes. The present paper describes a quantum simulation of the interaction between a carbyne and different DNA bases using the density functional theory (DFT) and non-equilibrium Green's function (NEGF). Specifically, the transmission probability functions and density of states (DOS) of the carbyne are first computed and then used to determine the I–V characteristics of the carbon chain in the presence of DNA bases. The simulation results indicate that the carbyne's conductance increases when a DNA base is adsorbed onto its surface. This increase is the most significant with base A for a positive biasing voltage, and base T for a negative voltage. The result of the numerical study suggests that 1D carbon chains can be utilized as electrical sensing elements in DNA sequencing devices due to their capability in discriminating DNA bases.

With over billion users logging into Social Networking sites (SNS) like Facebook and Google everyday, world has truly become a global village. Huge amounts of personal data is being generated and collected each day during this process. Current business model of SNS involves in analysing this user generated data; displaying customized content and tailor made advertisements. Along with this, they may leverage on external revenue generating mechanisms as well. This gives rise to several data security and privacy concerns. Some of the litigations that SNS have been involved in recent years are a witness to it. Hence, there is an urgent need to define holistic privacy preserving regulations to curb this. Technically feasible solutions that empower users to protect their privacy are need of the hour. Solution should be robust and secure while providing scope for service providers to sustain and grow their business and revenue. Current work is a step towards achieving privacy preserving social networking. Proposed novel framework describes an architecture to provide users of service an option to securely opt-out of sharing personal data with SNS, there by avoiding being targets of behavioral advertising; yet ensuring revenue generation for SNS. We also try to bring out the necessity of updating consumer data privacy and security laws around the world.

This paper aims to reduce improper disposal of waste. The system exploits the technical need for Wi-Fi by the general population. The idea is to inspire people to properly dispose of waste. Here the trash bin acts like Wi-Fi hotspot. Once the user puts waste of certain quantity and enters his/her mobile number, the system sends him a SMS containing a OTP to access the Wi-Fi. This can help us to reduce the waste and also encourage the public to make use of proper waste disposal system.

This paper discusses design, fabrication and characterization of a 3D stacked small form factor (SFF) system-in-packages (SiPs) suitable for wearable electronics systems. In order to achieve very small size SiP, two SFF multi-chip packages were designed and fabricated utilizing embedded wafer-level ball grid array (e-WLB) technology. The package consisted of six radio frequency (RF) and digital integrated circuits (ICs), and 24 passive components of varying sizes. 3-D stacked system in package was of size 6.0 × 5.5 × 1.9 mm and was 55% smaller than the same system fabricated using conventional printed circuit board technology. The measured results showed that system performance was on par with or better than the previous systems.

The demand for high speed, reliable communication using available frequency spectrum results in a new technology called OFDM. Channel estimation has a vital role in finding the effect of the channel on OFDM signal. In this work, performance analysis and implementation of pilot aided channel estimation is investigated. Different channel estimation methods like Least Square, Minimum mean Square Error estimator, modified MMSE and LS methods are discussed in the proposed work. This paper concentrates on performance comparison of channel estimation in terms of Symbol error rate and Mean square error.

Orthogonal Frequency Division Multiplexing (OFDM) has become the most efficient multi carrier modulation technique for achieving high data rate transmission in wireless communication. OFDM technology possesses various advantages like high spectral efficiency, Immunity against multipath fading and negligible intersymbol interference. However, despite these advantages, OFDM performance has been limited by high Peak Power to Average Power Ratio (PAPR). In this paper, we propose a combined algorithm of Independent Component Analysis (ICA)/Principle Component Analysis (PCA) and Clipping method for reducing the PAPR of OFDM System. Firstly, OFDM signal is processed using ICA or PCA to reduce PAPR, and then further reduction is done by using clipping and filtering method. This constitutes a significant reduction in PAPR without the degradation of bit error rate (BER).

Efficient utilization of the available spectrum is of utmost importance as the demands for wireless service have significantly increased over the recent years. MIMO communication is a promising solution for this because of improved capacity and quality assurance. The link performance of MIMO system can significantly increase by the suitable interference rejection method. On the other hand beam forming is a signal processing technique to guarantee high directionality of receiving signals. MIMO and beamforming are two related but different aspects of wireless communication. Interference mitigation in MIMO-Beamforming systems is the prime focus and this paper reviews the adaptive beamforming algorithms used for interference rejection in MIMO-Beamforming Systems. The adaptive algorithms are simulated with the help of Matlab.

Distributed energy resources (DERs) are necessary due to the increasing demand of electrical energy and depletion of fossil fuels. When DERs are placed in the distribution system, various power quality problems arise including voltage unbalances and current harmonics. These problems arise due to the excess power which is fed back to the network when generation in a particular phase exceeds load demand owing to the presence of DERs. The paper proposes the use of shunt active power filter (SAPF) and unified power quality conditioner (UPQC) to reduce the power quality problems. SAPF performs as expected during normal conditions but when an unbalance is present in the system, undesirable effects are seen. These problems can be mitigated using UPQC. The system with DER and SAPF/UPQC has been modeled using MATLAB and the performance analysis of SAPF and UPQC has been studied and compared.

Carbon based nanostructures such as graphene and carbon nanotubes have received widespread attention due to their unique mechanical and electronic properties. This paper describes a quantum mechanics based study of the electronic band structures and transport properties of one-dimensional (1D) carbon chains, the thinnest nanowires available in nature. The study is based on the application of density functional theory and non-equilibrium Green's function where maximally localized Wannier functions and Landauer formalism are combined to compute the electronic band structures and quantum conductance of the 1D carbon chains. The simulation result indicates that the peak quantum conductance of 1D carbon chains is about five times smaller than that of carbon nanotubes. However, the quantum conductance is also a function of the length and chemical bonds of the carbon chains. When the carbon chains are mechanically strained at 3%, the quantum conductance is reduced by about 50%. This result suggests 1D carbon chains can provide ultra high-resolution electromechanical measurements of important biomolecules such as DNA.

Magneto-dielectric materials have been spotlighted as a new opportunity for effective antenna size reduction in many recent studies. In this paper, we start from first principles to describe the methodology used to synthesize the magneto-dielectric material. A combination of Bruggeman's effective medium model and Sihvola and Lindell model are used to find the right mixture of cobalt particles and fluoropolymer material for the effective antenna miniaturization. A three pole Lorentzian dispersion model is derived for fitting extracted material properties of magneto-dielectric composite material. The material properties are extracted using a cavity perturbation technique with substrate integrated waveguide cavity resonator. The Kramers-Kronig relations are used to demonstrate that the extracted material properties from the method are causal in this paper. A 900MHz PIFA on magneto-dielectric material substrate is designed, fabricated and measured. A 7.4% bandwidth and 61.7% radiation efficiency for the antenna were obtained. Human body effect to antenna was studied and Specific Absorption Rate (SAR) was calculated. The simulation results show that magneto-dielectric material can reduce the effect of human body on antenna performance and the magnetic loss enables a decrease in the SAR of the antenna.

Cloud computing is a new platform in the field of information technology which faces the problem of load balancing as one among the other problems such as security and privacy. Efficient load balancing guarantees efficient utilization of resources by allocating the resources to users on demand. This paper presents a survey on various load balancing techniques and various task scheduling algorithms in order to perform load balancing in the cloud. The proposed system presented here is a replicated agent based system with DES encryption for security and the system uses multiple agents in order to handle multiple tasks to perform load balancing in the cloud. This enhances the efficiency on dealing with the load balancing problem.

Antenna size has fundamental limits based on the frequency of operation and performance required. In the past, various methods have been developed to miniaturize antennas with limited success. Magneto-dielectric materials, however, have been reported as providing new opportunities for effective antenna size reduction in many recent studies. In this paper, a novel material characterization method which is a cavity perturbation technique (CPT) with substrate integrated waveguide (SIW) cavity resonator is presented for measuring electric and magnetic properties of magneto-dielectric material. CPT formulas for extracting complex permittivity and complex permeability are explained and modification process using 3D EM simulation tool is discussed. Design and fabrication of SIW cavity resonators is presented. The frequency dependent properties of permittivity and permeability for synthesized magneto-dielectric material are extracted in the frequency range of 1-4 GHz. Planar inverted F antenna (PIFA) working at 1GHz on magneto-dielectric substrate has been designed and simulated in this paper.

This paper demonstrates, for the first time, a Wireless Local Area Network (WLAN) radio frequency (RF) front end module (FEM), incorporating the smallest, high-performance band-pass filter (BPF) on a 110μm-thin organic substrate with chip-last embedded actives and thin-film passives. The FEM consists of a power amplifier (PA) die, a switch die, and two low-noise amplifier (LNA) dies, integrated with a BPF and a low-pass filter (LPF). Full-wave electromagnetic (EM) simulations are employed to study the signal path loss, EM radiation and coupling. The BPF and LPF have 0.25dB and 0.5dB insertion loss respectively, with in-substrate dimensions of 1mm x 1mm x 0.05mm. The PA die shows a gain of around 10.8 dB at 2.4GHz. The path between the antenna and the amplifiers is also characterized to have a loss of 3dB. The electromagnetic coupling from the PA output to the LNA input and to the PA power supply is simulated using full wave EM solver HFSS and found to be higher than 60dB, indicating very good EM isolation. Each block of the FEM is individually characterized and combined using Agilent ADS to obtain the complete S-parameter performance. Both the transmitter and receiver chains have gain of 9dB.

This paper reports on the design, fabrication and characterization of active die embedded ultra slim system-in-packages suitable for integrating RF and digital integrated circuits, and discrete components. Portable communication devices such as Ultrabooks, tablets and smart phones require very small form factor radio subsystems. To address this need, several proof of concept (POC) packages with embedded active die have been designed and characterized. A 50% form factor reduction compared to packages currently used in radio system half minicards is achieved by using this packaging approach. Test results show that the radio frequency (RF) performance of this small form factor (SFF) SiP is within the system specification.

Antenna miniaturization without deteriorating performance is challenging because the performance is bounded by fundamental limits depending on the size of the antenna. Magneto-dielectric materials, however, have been reported as providing new possibilities for antenna miniaturization in many recent studies. In this paper, a novel material characterization method which is a cavity perturbation technique (CPT) with substrate integrated waveguide (SIW) cavity resonator is presented for extracting the properties of magneto-dielectric material. CPT formulas for extracting complex permittivity and complex permeability are explained and modification process using 3D EM simulation tool is discussed. Design and fabrication of SIW cavity resonators is presented. The frequency dependent properties of permittivity and permeability for synthesized magneto-dielectric material are extracted in the frequency range of 1-4 GHz. Microstrip patch antenna working at 1GHz on magneto-dielectric substrate has been designed and simulated in this paper.

In an era witnessing exponential growth in number of web services in similar domains being published in internet, redundancy is an inevitable issue. In this context, Quality of Service which is offered by these services forms the differentiating factor. Several works have been published describing different approaches to discover best in class services and it continues to be a hot research area. In this process, service providers who provide ordinary quality of service lose out to competition and eventually dissolve. Hence customers will have to choose service providers who provide better quality than they actually need and end up paying more money. In order to address this issue, we have proposed a new approach named Anveshana aiming to create a Win-Win result for both customer and service provider. Anveshana considers quality and priority requirements of customer and provides a relevance score to each applicable service and helps in rationally selecting the right service in an unbiased manner. This forms basis of further research on customer driven service selection leveraging on quality of service based computations.

Nowadays, as companies are adopting strategies that are in a better balance with environmental friendly practices, so "Going Green" is becoming an effective strategy in terms of costs benefits and customer satisfaction. The focus of this paper is on the study of recollection in Closed Loop Supply Chain (CLSC), using system dynamics approach, by identifying and establishing relationships between variables in a CLSC system. A system dynamic model has been developed to simulate the influence of recollection on various factors like demand, sales, uncontrollable disposal, recycling rate, serviceable inventory, and the total profit. The results have indicated that, on a long term basis, closed loop supply chain can be successfully adopted to improve the cost benefits and to reduce the uncontrollable product disposals.

Most of the current cloud computing platforms offer Infrastructure as a Service (IaaS) model, which aims to provision basic virtualised computing resources as on-demand and dynamic services. Nevertheless, a single cloud does not have limitless resources to offer to its users, hence the notion of an Inter-Cloud enviroment where a cloud can use the infrastructure resources of other clouds. However, there is no common framework in existence that allows the srevice owners to seamlessly provision even some basic services across multiple cloud service providers, albeit not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. In this paper we present a novel solution which aims to cover a gap in a subsection of this problem domain. Our solution offer a security architecture that enables service owners to provision a dynamic and service-oriented secure virtual private network on top of multiple cloud IaaS providers. It does this by leveraging the scalability, robustness and flexibility of peer- to-peer overlay techniques to eliminate the manual configuration, key management and peer churn problems encountered in setting up the secure communication channels dynamically, between different components of a typical service that is deployed on multiple clouds. We present the implementation details of our solution as well as experimental results carried out on two commercial clouds.

This paper presents processing, integration and characterization of a new class of magneto-dielectrics consisting of metal nanoparticles in a polymer matrix for antenna applications. These nanomagnetic materials exhibit several attractive features for antenna applications, such as simpler thick-film processing, higher permittivity and permeability, and lower loss. Extraction of permeability and loss for such materials is usually cumbersome because both the permittivity and permeability are unknown. The meander-shaped inductor structures developed and reported here allow simpler fabrication and characterization of permeability and magnetic loss. Inverted-F antenna structures were designed, fabricated and tested to verify this method.

System integration by die-embedding within electronic packages offers significant advantages in miniaturization, cost and performance for mobile devices. This paper presents the functional design and analysis of ultra-thin packages that combine embedded actives (GaAs Power Amplifier and a baseband digital IC) with embedded passives (band-pass filters), leading to an ultra-miniaturized WLAN sub-system. This chip-last design routes embedded dies in the outer build-up layer, using Embedded MEMS Actives and Passives (EMAP) technology being developed in the Georgia Tech PRC's industry consortium, as an alternative, lower cost approach over current chip-first and chip-middle methods. Electromagnetic (EM) simulations were performed in order to tune the electrical performance of interconnections based on die specifications and package configuration. The digital package was designed with multiple power-ground pair islands to enhance noise isolation, while improving overall signal and power integrity. The embedded module designs for RF transmitter and the baseband IC measure at 2.8mm × 3.2mm × 0.25mm and 10mm × 10mm × 0.25mm respectively, achieving over 4.5× volume reduction compared to existing wire-bond packages.