Typically an Intelligent Transportation System (ITS) employs various types of infrastructure-based technologies into vehicles and roadways for monitoring traffic. But these solutions have high installation, maintenance, and operational costs. Further, most of these solutions are based on the assumptions of lane-based organized and homogeneous traffic, due to which these are ineffective in less organized traffic conditions which are common in developing countries. In this paper, we have proposed a cost-effective approach to infer the traffic state of the road by analyzing the cumulative acoustic signal collected from the microphone sensor of the user’s smartphone. To capture the distinctive characteristics of various traffic scenes, we explored two different types of features: Mel Frequency Cepstral Coefficients (MFCCs) and Wavelet Packet Transform (WPT). Based on the understanding of acoustic signals of different traffic scenes, various parameters of these features such as window size and MFCC dimensions are tuned for better detection accuracy and robustness. To validate the approach, field experiments were conducted in varied conditions on the roads of City X. Experimental results revealed that for binary traffic scene classification (‘busy-street’ vs. ‘quiet-street’), MFCC features are sufficient to get an overall accuracy of 100%. However, for ‘congestion’ vs. ‘medium-flow’ vs. ‘free-flow’ traffic scene classification, MFCC features yield a bit lower accuracy of 77.64%. In this scenario, it was observed that WPT features can be used to reduce the false positive rate, thereby providing an absolute gain of 11.38% in the classification accuracy over the MFCC baseline. Also, it has been observed that by crowdsourcing the traffic state information from multiple users’ smartphones, an effective accuracy improvement can be achieved for each traffic scene.

Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Today’s fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions.

Road surface monitoring is an important problem in providing smooth road infrastructure to the commuters. The key to road condition monitoring is to detect road potholes and bumps, which affect the driving comfort and transport safety. This paper presents a smartphone based sensing and crowdsourcing technique to detect the road surface conditions. The in-built sensors of the smartphone like accelerometer and GPS 1 have been used to observe the road conditions.

The traffic and number of vehicles on roads are increasing with an unstoppable pace, which in turn leads to the problem of traffic congestion. We propose the use of Acoustics to determine

Traffic congestion occurs when the number of the vehicles increases more than the existing space of the road. This deleterious problem is increasing at an alarming rate in the whole world. For any effective Intelligent Transportation System, early detection of traffic congestion is very important to take corrective action. Several techniques have been developed to detect traffic congestion, most of which are infrastructure based. Even though these techniques are widely used, but they have many downsides as well. They require large capital input for installation as well as for maintenance. In this paper, we propose an efficient and cost-effective method using smartphones to determine the traffic state of the road. The acoustic data collected from commuter’s smartphone is segmented into fixed size frames. Various time and frequency based features such as (MFCC, Delta & Delta-Delta, ZCR, STE, and RMS) are extracted from each frame and used for detecting traffic state as ’busy street’ or ’quiet street’. We have compared the accuracy of two classifiers Support Vector Machines and Neural Network by using acoustic data collected from 320 different recording sessions. Experiments have shown that feature set having features MFCC, STE and RMS, results in better classification accuracy of 91.8% with Neural Network and 93% with SVM. Furthermore, various relevant factors affecting the classification accuracy are also tested like frame size, window functions, overlapping size and different combination of features. The frame size of 8192 and hamming window function proved to be more efficient than others.

This paper presents a comprehensive study of the state-of-the-art acoustic signals for vehicular feature extraction and classification. Acoustic signals can be used as powerful means to analyze and monitor traffic. Various other sensors are also used for analysis, but they have certain limitations which are overcome by acoustics. The acoustic classification process includes acoustic data input, sensing unit, segmentation, feature extraction, classification, and outputs decision. Multi sensor real time environment consists of an acoustic classification system as its part for traffic monitoring and surveillance. In this paper, a comprehensive review of various acoustic features along with acoustic classifiers and the datasets used is presented. Different challenges related to acoustic vehicle recording data are also addressed.

In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control losses in the case of a rare but extreme event. Conditional Value-at-Risk (CVaR) is a popular risk measure for modelling the aforementioned objective. We consider the problem of estimating CVaR from i.i.d. samples of an unbounded random variable, which is either sub-Gaussian or sub-exponential. We derive a novel one-sided concentration bound for a natural sample-based CVaR estimator in this setting. Our bound relies on a concentration result for a quantile-based estimator for Value-at-Risk (VaR), which may be of independent interest.

Consider a bottleneck in a road through which only one car can pass through. Suppose that at a time the car passing will have the most aggressive driver in queue and that the aggressiveness of an individual is measured by an attribute A ≡ Nτ σ where the quantity N varies randomly from person to person in the range 0 to 1, τ is the time for which the driver is waiting in the bottleneck and the parameter σ is the same for all individuals. Thus, we assume that the aggressiveness depends on the nature of the individual and increases with waiting time in a traffic jam. In support of the algebraic form of A, we show (numerically and analytically) that our hypothesis implies that the probability of waiting for a time τ will be P(τ ) ∝ τ α with the value of α fixed by σ. Empirical studies confirm such variation in P(τ ) with an exponent of 3.0 to 3.5 in two different cities of India and 1.5 for a traffic intersection in Germany. There is a possibility that the parameter σ (and hence α) is characteristic of a geographical region.

In this paper, we study the problem of devising a signal control policy for road transportation networks that is fair and provides high throughput. In particular, we propose and study a novel Queue-Delay Backpressure algorithm with variable cycle length, that takes into account both the queue lengths and the head-of-line delay at a junction. Using a variety of simulations, we show that the proposed algorithm achieves a middle ground between maximizing throughput and minimizing the maximum delays incurred. We then optimize cycle lengths to achieve minimum weighted sum of delays. Finally, we also study the effect of explicitly considering start-up losses in headways while optimizing the cycle lengths, and conclude that it is beneficial to consider these effects while designing signal control policies.

Reaction delays are important in determining the qualitative dynamical properties of a platoon of vehicles traveling on a straight road. In this paper, we investigate the impact of delayed feedback on the dynamics of the Modified Optimal Velocity Model (MOVM). Specifically, we analyze the MOVM in three regimes – no delay, small delay and arbitrary delay. In the absence of reaction delays, we show that the MOVM is locally stable. For small delays, we then derive a sufficient condition for the MOVM to be locally stable. Next, for an arbitrary delay, we derive the necessary and sufficient condition for the local stability of the MOVM. We show that the traffic flow transits from the locally stable to the locally unstable regime via a Hopf bifurcation. We also derive the necessary and sufficient condition for non-oscillatory convergence and characterize the rate of convergence of the MOVM. These conditions help ensure smooth traffic flow, good ride quality and quick equilibration to the uniform flow. Further, since a Hopf bifurcation results in the emergence of limit cycles, we provide an analytical framework to characterize the type of the Hopf bifurcation and the asymptotic orbital stability of the resulting non-linear oscillations. Finally, we corroborate our analyses using stability charts, bifurcation diagrams, numerical computations and simulations conducted using MATLAB

In this paper, we deal with the problem of supply forecasting in the context of an application based taxi hailing service. We first propose a method to optimally partition the city space using a Voronoi tessellation. The generating points of the Voronoi regions are obtained as demand density cluster centers, from the taxi demand dataset. We also identify the optimal temporal resolution to use for forecasting supply in these Voronoi regions. We use a linear time-series based algorithm to forecast supply in each Voronoi region. Using this methodology for the city of Bengaluru, India, we obtained a supply forecast accuracy of about 90 percent for the heavily used Voronoi regions. This represents a substantial improvement in the forecast accuracy compared to similar time-series based approaches, employed over rectangular geohashes

The Optimal Velocity Model (OVM) is a carfollowing model that captures some interesting real-world phenomena. There are two key variants of this model, which depend on whether they capture the dynamics of a platoon of vehicles travelling on a circular loop, or on an infinitely long road. In this paper, we numerically compare the OVM analysed by Orosz et al. [8] (which considers a circular loop) and the recently-proposed Modified Optimal Velocity Model (MOVM) (which considers an infinitely long road). A key insight obtained is that the variant with periodic boundary conditions could lead to collisions, which appears to be absent in the MOVM. We also derive a necessary condition for non-oscillatory convergence of the MOVM for autonomous vehicles to avoid oscillatory movements

Algorithms for map matching are important for numerous transport applications. Typically, the first step in the development of such algorithms one needs techniques for finding candidate road segments. Most proposals for finding candidate road segments are either computationally very intensive, or not very precise, which puts additional burden on the rest of the algorithm. In this paper, we propose a new quantity called an Alignment Quotient (AQ) to find road segments in close proximity to a GPS data point. We also propose an algorithm that employs the AQ to find candidate road segments, and we conduct a preliminary analysis for the same

Multi-hop relaying is used for increasing the communication range of cognitive radio networks (CRNs). To circumvent the implied loss in throughput due to multi-hop relaying, in this paper, we propose incremental relaying (IR) for cooperative multi-hop cognitive radio networks (CMHCRNs). Two IR protocols that select a relay from the qualified relays (the relays whose received signal-to-noise-ratio (SNR) values are greater than a predefined threshold) in each hop are proposed to study the performance of the considered CMHCRN. The first protocol is named as IR based highest SNR (IR-HSNR) protocol that selects a relay with best SNR to the destination. Another one, termed as IR based highest transmit power (IR-HTP) protocol, selects a relay with highest available transmit power. Expressions for the exact outage probability and throughput are derived for both routing protocols by considering both peak-power and peakinterference constraints. Moreover, closed-form expressions for floor values of outage probability and throughput are derived for both the protocols. It is shown through numerical results that throughput of the considered system shows a concave behavior with respect to number of relaying hops for both the proposed protocols. It is further observed that the performance of the IRHSNR is superior to that of the IR-HTP, whereas the latter has lower implementation complexity.

This letter investigates the performance of an analog network coding based underlay cognitive two-way energy harvesting relay network. The amplify-and-forward relay harvests energy using the power-splitting relaying (PSR) protocol. Using approximate expressions for the terminal outages, throughput, and ergodic sum-rate, we derive insights into performance. Specifically, we derive closed-form expressions for the throughput maximizing interference temperature apportioning parameter (ITAP) and the PSR parameter, and show that they can be optimized separately. We also derive a closed-form expression for the ergodic sum-rate maximizing PSR parameter, and show that an ITAP of half maximizes ergodic sum-rate. Simulations demonstrate the accuracy of the derived expressions.

In this letter, we derive an expression for the secrecy outage probability of a multiuser underlay downlink in which a selected idle secondary user serves as a friendly jammer to enhance physical-layer secrecy of secondary downlink communication in the presence of a passive eavesdropper. Secondary nodes constrain their transmit powers to ensure that the interference caused to the primary network is below an interference temperature limit IP . We show that careful apportioning of IP between the secondary source and the selected jammer (which transmit simultaneously), and judicious choice of peak powers, is the key to improving secrecy performance. Computer simulation results demonstrate accuracy of the derived expressions.

In this paper, we consider a novel energy-harvesting (EH) cognitive radio framework in which the primary source beamforms excess available energy to an EH secondary transmitter (ST) so as to enable it to share the spectrum. In the first phase of transmission, the multi-antenna primary source optimally beamforms symbols to its decode and forward relay while beamforming all excess energy to a selected EH ST. In the second phase while the relay transmits symbols to a selected primary receiver, the EH ST transmits symbols using underlay principles to its secondary receiver. Assuming peak interference constraints at the STs, we derive approximate expressions for outage probability and ergodic rate of the secondary link with the optimal beamformer. Using asymptotic expressions for outage and ergodic rate, we derive useful insights into performance of the system.We show that diversity equal to the number of secondary transmitters is achieved by the considered system. Computer simulations confirm accuracy of the derived expressions.

In this paper, we investigate the performance of a three-node dual-hop cognitive radio network (CRN) with a halfduplex (HD) decode-and-forward (DF) buffer-aided relay. We derive expressions for the average rate and symbol error rate (SER) performance of an adaptive link selection based channelaware buffer-aided relay (CABR) scheme that imposes peakpower and peak-interference constraints on the secondary nodes, and compare them with those of conventional non-buffer-aided relay (CNBR) and conventional buffer-aided relay (CBR) schemes for a delay-tolerant system. For a finite-sized buffer, we analyze the performance of a modified threshold-based scheme for fixedrate transmission. We analyze the trade-offs between the delay, throughput and SER. Computer simulation results are presented to demonstrate accuracy of the derived expressions. Keywords - Underlay Cognitive Network, Adaptive Link Selection, Buffer-Aided Relay.

in this paper, we study the performance of a cluster-based multi-hop underlay cognitive radio network using an ad-hoc routing protocol (called max-link-selection protocol) with decode-and-forward relays over Rayleigh fading channels. Both peak-power and peak-interference constraints are considered in the system. At first, the end-to-end exact and asymptotic outage probabilities of the considered system are derived. By using the derived asymptotic outage, an approximate expression for the optimum number of hops that minimize the outage probability is derived for fixed-rate and fixed-distance between the source and destination nodes. Later, the exact and asymptotic expressions are derived for end-to-end symbol-error-rate (SER) and ergodic-rate of the considered system. Numerical results are presented to validate the derived analytical expressions. It is shown by analysis that with increasing number of relaying hops between the source and destination, the SER performance improves but the ergodic-rate declines

We consider a two-hop underlay cognitive relay network with an energy harvesting (EH) relay. A simple improved time-switching protocol is proposed in which energy is transmitted in subslots until the relay node is sufficiently charged. The number of charging energy subslots required is fed back to the source by the relay node after the first energy subslot. We demonstrate that the proposed EH transmission scheme can yield better secondary throughput performance as compared to that achieved by an optimal EH transmission protocol using a fixed charging duration, while at the same time eliminating the need for feedback of channel estimates. We first analyze throughput when the source does not possess first-hop channel knowledge, but the relay possesses second hop channel knowledge. We then analyze throughput performance in the important case when the second hop channel estimate is in error, or the practical case when this channel estimate is not available. We show that when the second hop channel estimate is poor, it might be beneficial to use knowledge of only its statistical value. We show that the proposed scheme is energy efficient in terms of energy required per transmitted bit. Computer simulation results validate the derived expressions.

Finding a parking spot in a busy indoor parking lot is a daunting task. Retracing a parked vehicle can be equally frustrating. We present BluePark, a collaborative sensing mechanism using smartphone sensors to solve these problems in real-time, without any input from user. We propose a novel technique of combining accelerometer and WiFi data to detect and localize parking and un-parking events in indoor parking lot. We validate our approach at the basement parking of a popular shopping mall. The proposed method outperforms Google Activity Recognition API by 20% in detecting drive state in indoor parking lot. Our experiments show 100% precision and recall for parking and un-parking detection events at low accelerometer sampling rate of 15Hz, irrespective of phone’s position. It has a low detection latency of 20s with probability of 0.9 and good location accuracy of 10m.

Devices like smartphones come with 3G/4G and WiFi radios, which creates possibilities of heterogeneous network access. We investigate scenarios where internet access to a device is available only via the cellular network. However, not every user may connect directly to it. Users in the network may be split into hotspots and clients. Hotspots are the users that connect directly to the cellular network and may provide connectivity to the internet to other users by allowing them to connect to their WiFi interface. Clients connect to the cellular network only via hotspots. The optimization problem is to find the split of hotspots and clients, and the association between clients and hotspots, that maximizes the sum of the link rates of users. Importantly, the users must get at least the link rate they get when all are directly connected to the cellular network. In this paper, we formulate the optimization problem. We provide insights into the interplay of WiFi connectivity amongst users, their link rates to the cell tower, and the split that maximizes sum rate. We propose a novel heuristic approach to split the network. Median gains of 1.5× are observed over networks of up to 40 nodes.

Large-scale and dense WiFi networks are expected to provide high performance through data throughput of Gbps order, seamless mobility, and uninterrupted voice and video services. Corresponding to these requirements, we analyse 6 months worth of WiFi traffic from live and uncontrolled WiFi network deployment to establish the existence of three problems - (1) Aggressive Active Scanning, (2) Interrupted Mobility, and (3) Degraded quality of streaming voice and video services. We find that careful enhancements of several MAC layer mechanisms such as active scanning, handoffs, and frame retransmissions can solve these problems. With respect to these problems, our aim is to develop a system that can (1) detect the cause of performance drop in a large-scale and dense WiFi network and (2) provide a solution to either prevent that cause from occurring or restore the performance, if it is already degraded. We leverage controlled and uncontrolled experiments to quantify these causes, their effects, and the efficacy of proposed solutions for performance enhancements.

WiFi clients and access points (APs) in large-scale and dense WiFi networks transmit millions of MAC layer frames at granularity of microseconds. Thus, resulting in performance problems due to complex MAC layer interactions that may not be useful. Literature suggests existence of three such problems-(1) Useless and Aggressive Active Scanning (AAS), (2) Interrupted Mobility, and (3) Degraded quality of streaming voice and video services. We analyse 6 months worth of WiFi traffic captured from WiFi network deployed at the academic building of IIIT-Delhi to (1) detect and quantify the effect of AAS - WiFi clients loose upto 10X of throughput, (2) detect the 7 causes of AAS, and (3) suggest 2 ways to control AAS. We propose to develop a system that operates on global view of WiFi network through live measurements. This system will incorporate the mechanisms to detect and mitigate the causes of above mentioned problems.

It is well known that the Euclidean norm is sensitive to outliers; yet it is widely used for minimizing it is easy. Dictionary learning is no exception – the l2-norm allows for easy update of the basis/dictionary atoms. In this work, we propose a robust dictionary learning method that is based on minimizing the robust l1-norm. The ensuing optimization is solved using the Split Bregman approach. We apply the proposed technique to signal (energy and water) disaggregation and show that it excels over existing dictionary learning techniques (based on l2-norm).

This paper proposes a sparse regression based approach for accurate continuous Blood Pressure (BP) monitoring. ECG and Finger PPG signals serve as the input; from which 32 parameters are extracted. Not all parameters are indicative of BP; to automatically trim the redundant parameters a sparse regression based approach is proposed. To build the BP predicting model the necessary parameters and their corresponding weights are learned using data from 99 subjects. The learned model is applied on 10 test subjects. The ground truth BP is measured using a clinically proven, professional automatic digital BP monitor OMRON HBP1300., The BP prediction results show that the SBP/DBP mean absolute error and error standard deviation, with OMRON monitor as a reference, is 4.43/2.46 and 4.90/3.31 mmHg respectively, which falls under the standard allowable error mentioned by Association for the Advancement of Medical Instrumentation for estimation of BP. We have compared our work with other BP prediction techniques (Linear Regression and Feed Forward Neural Network) and have seen that our proposed method yields considerably better results, especially for diastolic BP.

Hypertension (high blood pressure) is the leading cause for increasing number of premature deaths due to cardiovascular diseases. Continuous hypertension screening seems to be a promising approach in order to take appropriate steps to alleviate hypertension-related diseases. Many studies have shown that physiological signal like Photoplethysmogram (PPG) can be reliably used for predicting the Blood Pressure (BP) and Heart Rate (HR). However, the existing approaches use a transmission or reflective type wearable sensor to collect the PPG signal. These sensors are bulky and mostly require an assistance of a trained medical practitioner; which preclude these approaches from continuous BP monitoring outside the medical centers. In this paper, we propose a novel touchless approach that predicts BP and HR using the face video based PPG. Since the facial video can easily be captured using a consumer grade camera, this approach is a convenient way for continuous hypertension monitoring outside the medical centers. The approach is validated using the face video data collected in our lab, with the ground truth BP and HR measured using a clinically approved BP monitor OMRON HBP1300. Accuracy of the method is measured in terms of normalized mean square error, mean absolute error and error standard deviation; which complies with the standards mentioned by Association for the Advancement of Medical Instrumentation. Two-tailed dependent sample t-test is also conducted to verify that there is no statistically significant difference between the BP and HR predicted using the proposed approach and the BP and HR measured using OMRON.

Metamaterials with negative index of refraction has created great opportunities for novel applications at Radio Frequency. Metamaterial finds applications as filters, diplexers and as frequency selective surfaces for gain enhancement in antennas. In this work open loop filters with circle, triangle and pentagonal geometries are proposed which can be configured as metamaterial for various wireless applications. These metamaterials can be energised using different feeding mechanisms like edge coupling, electromagnetic coupling, inductive coupling, capacitive coupling etc. Proposed filters are energised using edge coupled feeding mechanism which has reduce complexity compared to other techniques. Edge coupling also ensures the tunability of the filters and make them applicable for low frequency, where the conventional planar filters consumes large area. The geometrical variations in the loop are found to influence the filter characteristics and this is studied in detail. Design equations are furnished for the filters based on the extensive analysis carried out using ANSYS High Frequency Structure Simulator (HFSS) using finite element method. The current distribution and electric field of the proposed filters are also analysed in detail. Filter prototypes with optimum performance as seen in the simulations are fabricated and measured using Keysight ENA 5080A Network Analyser. Good agreement is found to exist between experimental results and the simulated results for the designed filters. A comparative study is carried out based on the frequency of operation, bandwidth and the attenuation of the three designed filters among themselves and the previous works that authors referred into.

For efficient monitoring and tracking of vehicles, aircraft or ships GPS (Global Positioning System) satellite based navigation system plays an inevitable role. For long range GPS communication and tracking, high gain antennas usually array antennas are preferred. Realized using butler matrix phased array antennas are potential candidate for providing long range communication providing directional beam. Butler matrix provides equal amplitude to the antennas by means of hybrid coupler, necessary beam orientation using phase shifter and ensures signal purity when transmission lines overlap by crossover circuit. Thus crossover will mitigate the GPS tracking of signal when transmission lines overlap each other maintaining high SNR. This paper focuses on the investigations of an L band planar crossover realised using microstrip technology. The crossover characteristics like transmission and isolation are analysed in detail. For enhancing the isolation between the decoupled ports, parent crossover is further modified maintaining good transmission between coupled ports. The analysis is done using ANSYS HFSS and the optimised crossover is fabricated on FR4 substrate and measurements are taken by using Keysight ENA 5080A Vector Network Analyser. The simulated and measured results are in good agreement with each other. This proposed crossover is the possible solution for long range wireless communication and GPS Applications.

Present work proposes an Asymmetric Coplanar (ACPW) fed antenna providing wide band and omnidirectional radiation. Gain enhancement is achieved by the introducing perturbations on the shorted ground plane. The proposed antenna offers a 2 : 1 VSWR bandwidth of 741MHz at 5.2 GHz with the enhanced gain of 6.4 dBi. The parametric analysis and optimization of the antenna is done by using ANSYS HFSS. Optimized prototype is analyzed experimentally using Keysight E5080A Vector Network Analyzer. The proposed antenna will have wide range of applications in different wireless communication systems.

In this paper a hybrid cavity substrate integrated waveguide filter is proposed and demonstrated for Ku band application. Hybrid cavity which is the combination of both circular and rectangular cavity, showcases a better frequency selectivity and Quality Factor in comparison with other cavity based substrate integrated waveguide filters. Quality Factor requirements are met by the introduction of shorting posts at specified points inside the cavity. Filter operates at a frequency of 13GHz with an optimized Q factor of 377.096.The detailed analysis of the proposed structure is simulated and studied using the ANSYS HFSS software.

An antenna is an integral component in a wireless communication system. The choice of the antenna geometry of these systems depends on the preferred specifications. Depending upon the variation in the geometry and feeding technique, the performance of the antenna can be modified. For any communication system design an antenna with a better transmission range, improved reflection and radiation characteristics at the designed frequencies is desired. An asymmetric coplanar strip fed antenna is designed and analyzed in this paper. This structure gives better performance compared to microstrip and coplanar waveguide (CPW) fed omni-directional antennas. The proposed antenna is compact with reduced complexity, providing appreciable gain. A monopole antenna system with a metamaterial ground plane on the same plane is proposed. This antenna operates in the 2.45 GHz Wi-Fi band. The radiating element and the metamaterial ground plane are optimized for the best performance of the antenna system. For optimum performance of the antenna, open loop resonator is also studied as the ground plane. Miniaturization is achieved with metamaterial ground plane without changing the characteristic of the monopole antenna structure. The monopole antenna with the metamaterial ground plane is found to be compact with resonance at 2.45 GHz offering a 2 : 1 VSWR bandwidth of 217 MHz. The radiation pattern of the designed antenna is omni-directional with a gain of 3.124 dBi at the resonant frequency. The antenna is designed and optimized using ANSYS HFSS 14. The optimized antennas are fabricated using FR4 substrate and are experimentally validated using Vector Network Analyzer E5080A.

A coplanar waveguide (CPW) fed rectangular loop antenna with dual frequency characteristics applicable Wi-Fi bands is presented. The studies to calculate the dependency of frequencies on the antenna structure is carried out using ANSYS HFSS. From this, the antenna is optimized to have an enhanced gain of ˜ 8 dBi at 2.45 GHz and 3.83 dBi at 5.32 GHz. The radiation characteristics of the antenna are studied and the radiation pattern confirms the monopole characteristics at both the frequencies. The optimized antenna performance is confirmed by measurements using Vector Network Analyser. Since energized using coplanar waveguide the proposed antenna finds applications in Microwave Monolithic Integrated Circuits (MMIC) also.

Artificially engineered materials, like metamaterials can be used to enhance the performance of devices. Split-ring resonators (SRRs) is the most widely used metamaterial at microwave regime. In this paper, edge coupled metamaterial filter using SRR with band stop characteristics in the frequency spectrum is proposed. Exhaustive simulations are conducted to understand the properties of the filter. The effects of the geometrical parameters on the resonance frequency are investigated. The proposed filter prototypes are tested using Vector Network Analyser and thus simulated results are validated.

There are several resource-poor communities in the world that lack access to respiratory diseases, due to insufficiency in medical expertise and less availability of medical diagnostic devices. Respiratory diseases like tuberculosis, pneumonia and asthma are becoming major problem throughout the world. Analysis of cough sound helps in detection of respiratory diseases because cough is one of the most common symptoms among all respiratory diseases. These diseases are differentiated on basis of frequency of cough and cough pattern. The identification of wet and dry cough is an important measurement to diagnose the respiratory diseases like tuberculosis, asthma and pneumonia. Though there are existing systems in analyzing cough signal, still there is a need in developing tool which analyses cough signal and capable of detecting the disease at earlier stages as well as monitoring the recovery of patients suffering either tuberculosis or asthma. In this paper we developed a method for automatic recognition of cough from continuous speech and differentiate cough as wet or dry cough. This method uses the (DSP) Digital Signal Processing techniques to extract the sound events by using event extraction technique and classify them as cough and non cough by using K-means algorithm. The extracted coughs are classified into dry and wet by using (GMM) Gaussian Mixture Model. The software tool analysis is based on a machine learning algorithm that uses cough sound to diagnose the respiratory condition and this can be easily integrated with an expert system which provides respiratory digital health services which provide low-cost diagnostics to base populations and to connect patients with the physicians.

These days most of the people especially children are attacked by pulmonary diseases such as asthma and chronic bronchitis throughout the world. Early identification of these diseases is necessary and regular monitoring is compulsory to reduce severity of these diseases. Respiratory sound analysis plays a major role in early diagnosis. Analysis of respiratory sounds helps in the detection of these diseases, as respiratory signal carries information of the lungs which is used to detect the adventitious lung sounds. Wheeze is one of the most common symptoms for these diseases like asthma and chronic bronchitis. This paper presents an approach for lung sound classification as normal, abnormal sounds. The abnormal sounds are of types wheeze, crackles, rhonchi etc. Our primary focus is on wheeze sounds, so abnormal sounds are classified as wheeze, nonwheeze. The proposed system consists of techniques such as feature extraction and classification algorithms. Feature extraction methods commonly used here is MFCC. The MFCC’s of different sounds is classified using K-means as normal and abnormal lung sounds. The abnormal sounds features are classified using the Gaussian mixture model algorithm as wheeze and nonwheeze. We collected real time data consisting of different lung sounds under the supervision of pulmonologist. The code is developed in MATLAB environment and performs the wheeze classification satisfactorily.

As per WHO recent estimations, the four major potentially fatal respiratory diseases (Asthma, Pneumonia, Tuberculosis and Chronic Obstructive Pulmonary Disease-COPD will account for about one in five deaths worldwide. Respiratory diseases are therefore likely to remain a major burden on society for decades to come. Both the prevention and treatment of lung diseases will need to be improved if their impact on longevity and quality of life of individuals, and their economic burden on society, are to be reduced worldwide. Early detection and effective diagnosis is the only rescue to lessen respiratory diseases fatality. Soft computing approaches are gaining importance in medical disease diagnosis because of their classification performance. In this paper we have developed the expert system which is based on artificial neural network that models human abilities in analyzing and diagnosing respiratory diseases like Asthma, Pneumonia, COPD and Tuberculosis. The objective of developing the system is to diagnosing the potential disease that one may suffer is done by providing relevant inputs through a consultation in which the patient has to answer the set of questions related to signs and symptoms. Index Terms: Disease diagnosing, respiratory diseases, expert system, inference technique.

Multilingual spoken dialogue systems have gained prominence in the recent past necessitating the requirement for a front-end Language Identification (LID) system. Most of the existing LID systems rely on modeling the language discriminative information from low-level acoustic features. Due to the variabilities of speech (speaker and emotional variabilities, etc.), large-scale LID systems developed using low-level acoustic features suffer from a degradation in the performance. In this approach, we have attempted to model the higher level language discriminative phonotactic information for developing an LID system. In this paper, the input speech signal is tokenized to phone sequences by using a language independent phone recognizer. The language discriminative phonotactic information in the obtained phone sequences are modeled using statistical and recurrent neural network based language modeling approaches. As this approach, relies on higher level phonotactical information it is more robust to variabilities of speech. Proposed approach is computationally light weight, highly scalable and it can be used in complement with the existing LID systems.

Community Question Answering (cQA) services like Yahoo! Answers1 , Baidu Zhidao2 , Quora3 , StackOverflow4 etc. provide a platform for interaction with experts and help users to obtain precise and accurate answers to their questions. The time lag between the user posting a question and receiving its answer could be reduced by retrieving similar historic questions from the cQA archives. The main challenge in this task is the “lexicosyntactic” gap between the current and the previous questions. In this paper, we propose a novel approach called “Siamese Convolutional Neural Network for cQA (SCQA)” to find the semantic similarity between the current and the archived questions. SCQA consist of twin convolutional neural networks with shared parameters and a contrastive loss function joining them. SCQA learns the similarity metric for question-question pairs by leveraging the question-answer pairs available in cQA forum archives. The model projects semantically similar question pairs nearer to each other and dissimilar question pairs farther away from each other in the semantic space. Experiments on large scale reallife “Yahoo! Answers” dataset reveals that SCQA outperforms current state-of-theart approaches based on translation models, topic models and deep neural network based models which use non-shared parameters.

. Internet users today prefer getting precise answers to their questions rather than sifting through a bunch of relevant documents provided by search engines. This has led to the huge popularity of Community Question Answering (cQA) services like Yahoo! Answers, Baidu Zhidao, Quora, StackOverflow etc., where forum users respond to questions with precise answers. Over time, such cQA archives become rich repositories of knowledge encoded in the form of questions and user generated answers. In cQA archives, retrieval of similar questions, which have already been answered in some form, is important for improving the effectiveness of such forums. The main challenge while retrieving similar questions is the “lexico-syntactic” gap between the user query and the questions already present in the forum. In this paper, we propose a novel approach called “Deep Structured Topic Model (DSTM)” to bridge the lexico-syntactic gap between the question posed by the user and forum questions. DSTM employs a two-step process consisting of initially retrieving similar questions that lie in the vicinity of the query and latent topic vector space and then re-ranking them using a deep layered semantic model. Experiments on large scale real-life cQA dataset show that our approach outperforms the state-of-the-art translation and topic based baseline approaches.

In this paper, we introduce Vaidya, a spoken dialog system which is developed as part of the ITRA1 project. The system is capable of providing an approximate diagnosis by accepting symptoms as freeform speech in real-time on both laptop and hand-held devices. The system focuses on challenges in speech recognition specific to Indian languages and capturing the intent of the user. Another challenge is to create models which are memory and CPU efficient for hand-held devices. We describe our progress, experiences and approaches in building the system that can handle English as the input speech. The system is evaluated using subjective statistical measure (Fleiss kappa) to assess the usability of the system.

Cognitive wireless sensor networks (CWSNs) provide better bandwidth utilization as compared to normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. It also helps the network to eliminate collisions and delays in data delivery. While doing so, however, CWSNs are subject to several security threats. The attack on network availability is known as Denial of Service (DoS) attack. The Spectrum Sensing Data Falsification (SSDF) attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the collaborative spectrum sensing decision become wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other’s network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In node cloning attack the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The Maximum-Match Filtering (MMF) algorithm is used for making a secure spectrum sensing decision and spectrum allocation in CWSNs. In this paper we have proposed the Cloned Node Detection (CND) algorithm to detect cloned nodes. This paper also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack. Our CND and MMF algorithms have O(n) and O(c 2n) worst case time complexities respectively. Our algorithms also have a very low false positive rate in the network. The MMF algorithm can produce good results with relatively small number of inputs, which reduces the traffic in the virtual control channel

In Service Oriented Systems (SOS), implementation of business processes is accomplished through services in distributed, loosely coupled manner based on business process requirements of the users. Consequently, importance of business process requirements analysis for development of SOS is strongly highlighted in both academia and industry. Usually, traditional requirement engineering is competent enough to specify and analysis business requirements for development of software systems efficiently. However, Service Oriented Requirement Engineering (SORE) emerging for SOS development is differ from traditional requirement engineering due to complex nature of services. Yet, a serious gap is still exist between early and detailed specification of business process requirements in SORE and further mapping towards design of SOS from set of business processes. To address this issue, in this paper, a requirements analysis framework is proposed for development of SOS systems. The contribution of the proposed work is formal representation of business process requirements for SOS based on business scenario and Cause-Effect-Dependency (CED) graph in dimensions of six aspects of services - What, Why, How, Who, When and Where (5W1H). Both early and detailed level requirements analysis in the context of SORE is facilitated by the proposed approach. Beside, traceability of proposed approach towards design of business processes for development of SOS is also exhibited in this paper. Moreover, the practical utility of the proposed approach is demonstrated using a suitable case study.

The traditional approach to business process modelling frequently results in large models that are difficult to change and maintain. In cloud-based environment, business dynamics are mandating that business processes normally be increasingly responsive to changes. This demands business process should be highly modular, scalable and flexible for cloud-based applications. Further, in cloud-based business environments, besides describing new capabilities, process models should also define how those capabilities can be integrated with the existing systems. In this paper, a hierarchical graph-based specification called business component graph for SaaS (BCGS) has been proposed to address those issues. The proposed BCGS, formally, realises the business components for software as a service (SaaS)-based applications. BCGS represents the complex business logic design as a set of business components and their inter-relationships. Here, business component is defined as methodical integration of business processes and business rules. This proposed integration approach facilitates high scalability and reusability of constituent elements of business components and ensures the consistency between processes and business rules. Moreover, this paper also includes the service orientation of the proposed concepts in SaaS framework. A detailed case study of BCGS also has been illustrated to show the expressiveness of the proposed concepts.

Heterogeneous stream formats, related contexts, vocabularies and schema structures are key difficulties to facilitate sharing and extracting knowledge from stream databases. To resolve these heterogeneities, the key challenge is how to provide common semantic representation for context-dependent data stream formats along with streaming databases. To address such issues, this paper proposes an ontology driven formal semantics of context-dependent data streams together with a universal conceptualization of streaming databases. The novelty of this work is to handle heterogeneity, large volume and availability of streaming data, such as web content, commercial broadcasting data etc. It also facilitates to recognize evolving information from semantic representation of data streams at conceptual modelling level. Besides, the proposed conceptual model is flexible to represent finite partition of stream and thus help in data stream storing and further querying. The conceptualization is implemented using an ontology editorial tool Protégé for the initial validation of proposed set of formal semantics. Several crucial properties of the proposed conceptualization are specified in order to exhibit the benefits of the proposed work. The expressiveness of proposed model is illustrated using a suitable case study.

Effective modelling has helped in explain, formalize and understand Service Oriented Architecture (SOA) that is a complex architecture style inherently. Yet, a serious gap is still exist in modelling inter dependency between structural and behavioural characteristics of SOA. Beside, lack of precise semantics and formalization in modelling of SOA has made serious challenges in checking consistency over SOA models. In order to address these challenges, in this paper, an ontology driven meta-model has been proposed for SOA. The proposed metamodel is conformed towards an ontology driven meta-meta model called, Generalized Ontology Modelling (GOM). It can be further restricted towards distinct models of SOA based applications. The proposed meta-model is implemented using ontology editorial tool Protégé and illustrated using suitable case study.

Most NoSQL databases have been devised independently from each other with specific application requirements. This has resulted in developing separate own data model and query language for each NoSQL database. The lack of standards in data models and query languages make applications and data less portable using these databases. Further, absence of formal semantics in query languages inhibits a precise understanding of query over NoSQL databases. To handle these issues, in this paper, an ontology driven query language for NoSQL databases is proposed. The proposed query language provides an efficient and common abstraction over the operational aspects on various kinds of NoSQL databases. The language includes formal common syntax and semantics of distinct query operators of NoSQL databases and those are represented in Description Logic. Further, usefulness of proposed query operators are proved using a suitable case study.

Solutions in the domain of mobile computing face a typical problem of fragmentation due to permissible customization to android framework. Fragmentation is a problem at the device side where the expected behavior of an application is not exhibited identically over all other devices. Presently, cross-layer design is becoming essential aspect of the app development, targeted to deliver an energy-efficient and productive solution. Fragmentation is posing an enormous challenge for development community, and solutions are designed per case basis. There are solutions developed in the domain of graphics and Web access, but as far as the fragmentation with core framework is considered, the solution is still missing. In this paper, we are proposing an intermediate background app residing between the application and the core framework. The proposed app TelePatch generates a map between the intended calls with actually supported calls. The map so obtained can be used by the interested application to obtain the services from the core framework. In our case, we have deployed TelePatch with NeSen, used for capturing the network-state parameters using telephony API of the Android.