Paddy is a critical global food crop, and its productivity is directly dependent on the number of panicles. Accurate panicle counting is essential for effective crop management. Existing localization-based methods rely on intermediate representations like density maps, which are often counter-intuitive and prone to errors. To quantify and locate panicles directly rather than sequentially, we propose P2PNet-Paddy, a novel approach based on the Point-to-Point Network (P2PNet) architecture, which was initially developed for crowd counting and localization. While the original P2PNet uses the Hungarian algorithm for point matching, it is found to result in uncertain matching results without context, reducing the counting efficiency. It is found that P2PNet encounters significant performance limitations when applied to agricultural datasets characterized by highly imbalanced panicle density distributions. We suggest integrating a density-based sampler to mitigate the impact of panicle density imbalance in the dataset and the k-nearest neighbors Matching Objective (KMO) Hungarian matcher to enhance the matching process by incorporating additional contextual information. Our proposed approach led to notable improvements, reducing MAE by 36% and MAPE by 48.3% compared to the baseline P2PNet model. The proposed method achieved an MAE of 7.31, MAPE of 39.18, andR2score of 0.87 on the test data. The dataset and code will be made publicly available at https://github.com/tejasri19/P2PNet-Paddy.

Automation of crop yield estimation is crucial to cultivate efficient breeding techniques to fulfill the increasing population demands and adapt to climate change. The panicle count of paddy is directly associated with the yield of crop. In this work, we implement computer vision-based image segmentation methods for segmenting panicles by utilizing Unmanned Aerial Vehicle (UAV) captured multispectral imagery. The developed algorithms in this study could be used as a reference for panicle counting models during the vegetative and heading stages of the paddy crop. In addition, we introduce a semi-automatic image annotation method for effortless creation of labeled paddy crop datasets. With this method, our developed tool collaborates with an annotation tool to offer preliminary image annotations, which users can subsequently refine the annotations.

Water is essential for any crop production. Lack of sufficient supply of water supply causes abiotic stress in crops. Accurate identification of the crops affected by drought is required for achieving sustainable agricultural yield. The image data plays a crucial role in studying the crop's response. Recent developments in aerial-based imaging methods allow us to capture RGB maize data by integrating an RGB camera with the drone. In this work, we propose a pipeline to collect data rapidly, pre-process the data and apply deep learning based models to segment drought affected/stressed and unaffected/healthy RGB maize crop grown in controlled water conditions. We develop an ensemble-based framework based on U-Net and U-Net++ architectures for the drought stress segmentation task. The ensemble framework is based on the stacking approach by averaging the predictions of fine-tuned U-Net and U-Net++ models to generate the output mask. The experimental results showed that the ensemble framework performed better than individual U-Net and U-Net++ models on the test set with a mean IoU of 0.71 and a dice coefficient of 0.74.

The monitoring of growth stages of a crop is vital for farmers to optimize the use of agronomic inputs and crop-management. Manual observation of the growth stages of a crop in large fields is a time consuming and labor-intensive task. To reduce human efforts in this tedious work, Unmanned Aerial Vehicle (UAV)-based remote sensing with the emergence of different technologies like deep learning is helping in monitoring the health of a crop. However, Convolutional Neural Network (CNN) based models need a lot of labeled data and computations to get trained to automate the process. In this paper, a pixel-based segmentation method has been proposed for tassel detection, and estimation of growth stages like tasseling, and day to 50% tasseling of maize crop. The performance analysis shows that the proposed method reduces time in developing the dataset for the training of CNN models. It also gives an advantage over training-time and computational complexity when compared to CNN models like YOLO and Faster-RCNN.

In conventional telesonography, the ultrasound video is encoded at a constant bit rate and transmitted to the expert side for diagnosis, this is highly bandwidth demanding and less adaptable to the varying network conditions. In contrary, Internet based videophone applications adapt to the network conditions with variable bit rate encoding and error concealment algorithms. Since portable ultrasound scanners are coming with Internet connectivity, this motivated us to evaluate the viability of using existing Internet based videophone services for real-time telesonography. Popular videophone services like Skype, Facebook and WebRTC is chosen for this purpose. The performance of these services is analyzed by varying network parameters like bandwidth, packet loss, delay and packet error in a controlled set up. Liver ultrasound video is considered for the study. A total of 273 different network settings is analyzed for evaluating the performance of each videophone service, constituting a total of 819 instances for evaluating three services. Three medical experts were participated in the subjective study. From an extensive analysis, an inference is made that the Internet based videophone services are reliable enough for telesonography provided a network with minimum bandwidth of 1600 kHz, delay up to 200 ms, packet loss up to 3% and packet error up to 3%.

Telesonography suffers from inherent limitations due to the need of all time availability of experts in cloud and data connectivity to the device. Computer-aided diagnosis (CAD) used for automatic detection of abnormalities without manual intervention can overcome these limitations. Commercially available ultrasound scanners restrict the installation of new softwares and hence CAD algorithms cannot be integrated into the existing ultrasound scanners. There is a need for an external computing device, which can acquire image data from ultrasound scanners, perform CAD and generate result. Smart-phones are now widely used in personalized healthcare due to its ubiquitous computing capability. Smartphones with embedded CAD can be used as a computing device for automated diagnosis. In this paper, we have developed an Application (APP) for a smartphone to automatically diagnose the kidney in the ultrasound image. With the developed APP, the smartphone can acquire images from any ultrasound scanner, process it and give the diagnostic result. Automatic abnormality detection of kidney is based on Viola Jones algorithm, texture feature extraction followed by SVM classifier. Stones and cysts are the abnormalities detected using the algorithm. The developed APP resulted with an accuracy of 90.91% in detecting the abnormalities.

Fast diagnosis plays a crucial role in treating the patients, which can be life saving manytimes. Ultrasound scanning have the capability to image the organs in real time, thus offering quick diagnosis. Recent advancements in computing platforms greatly reduced the size of an ultrasound machine to portable level. Portable ultrasound machines can be used for point of care and remote diagnosis, this comforts the patients by making them not going to the ultrasound scanner for diagnosis. Diagnosing the patients at their bedside reduces the diagnosis and medication time. Lack of sonographers limits the benefits of using portable ultrasound machines in remote diagnosis. Tele-radiology can be effectively used as a solution to provide better health-care to more number of patients with less number of experts. Portable ultrasound in remote diagnosis is concerned with wireless connectivity for web-based diagnosis, security, data aggregation of patients etc., In this paper, we propose a working prototype of portable ultrasound scanning system integrated with wireless connectivity, biometric authentication, Global Positioning System (GPS). Additional interfaces to the portable ultrasound system enables secured cloud based diagnosis, improving the health-care and reducing the geographical separation between patients and doctors.

High sampling rate is necessary for a quality ultrasound image, which demands expensive data-acquisition and computing devices. Compressive sensing(CS) can reconstruct high quality image with less data. It can give optimal solution to high sampling problem in ultrasound imaging. Ultrasound imaging is performed using beamforming of transducer array elements. In this work we present time and frequency domain beamforming matrices and demonstrates how it can be used as CS-matrix to reconstruct ultrasound images. Feasibility of CS with the beamforming matrices are studied using transfer point spread function. Compared to previous work in ultrasound using CS where signal reconstruction is used from undersampled data, we present direct ultrasound image reconstruction from highly undersampled received data. Image reconstruction with time and frequency domain beamformed CS-matrix are showed. Through our results it is clear that compressive sensing in ultrasound imaging can significantly reduce sampling rate by maintaining same image quality as traditional ultrasound imaging.

In this paper, we propose a generalized model for underlay based cognitive radio (CR) that is based on the information theoretic interference channel model. Utilizing the achievable rate region for the two user interference channel proposed by Han and Kobayashi, we establish achievable rates for the unlicensed or the Secondary User (SU) that depends upon a given target rate for the licensed or the Primary User. Hence, the transmission of the SU is rate limited due to the PU achievable rate. Unlike the traditional approach for underlay, our results bring out the impact of SU transmission on the set of achievable rates for the PU and also of the effect of PU target rate (which may be called its QoS) on the set of achievable rate for the SU.

Smart phones or mobile phones enabled with global positioning system (GPS), different types of sensors, and communication technologies have become ubiquitous application development platform for Internet of Things (IoT) and new sensing technologies. Improving sensing area coverage, reducing overlap of sensing area, and energy consumption are important issues under mobile phone sensing. This paper presents human mobility-based mobile phone sensors sampling algorithm. Human mobility patterns and geographical constraints have an impact on performance of mobile phone sensing applications. The real-outdoor location traces of volunteers, collected using GPS-enabled mobile phones are used for performance analysis of proposed work. The proposed mobile phone sensor sampling algorithm considers velocity of human mobility as an important parameter for improving sensing area coverage and reduction of energy consumption. To an extent overlap between sensing area coverage is allowed to overcome, the reduction of sensor data samples caused by spatial regularities of human mobility. The performance is analyzed and evaluated by considering general regular sampling and proposed sampling method for mobile phone sensing activity. The results show that for normal human walking velocity (<;1.5 m/s) proposed mobile phone sensor sampling algorithm performs better in terms of sensing area coverage and reduction of battery energy consumption for mobile phone sensing activity.

In commercial ultrasound systems, the transmit module typically generates the time delayed excitation pulses to steer and focus the acoustic beam. However, the ultrasound transmitter module in these systems has limited access to medical ultrasound researchers. In this paper, we have presented the development of a programmable architecture for 8-channel ultrasound transmitter for medical ultrasound research activities. The proposed architecture consists of 8 transmit channels and Field Programmable Gate Array (FPGA) based configurable delay profile to steer acoustic beam, transmit frequency and pulse pattern length depending on the medical application. Our system operates in pulse-echo mode, with ultrasound transmit frequency up to 20 MHz, excitation voltage up to 100 Vpp, and individual channel control with single high speed Serial Peripheral Interface (SPI). Pre-calculated delay profiles per scanline are generated in Matlab, based on physical parameters of 8 element linear transducer array which are used to steer and focus the ultrasound beam. An experiment is carried with our transmit module to transmit ultrasound into gelatin phantom, acquired echoes and processed for B-mode imaging. The results show that this transmit platform can be used for ultrasound imaging researches and also for medical diagnosis.

Carbon Monoxide (CO) induced by traffic pollution is highly dynamic and non-linear. In a pilot research, we collected some fine-grained 1Hz CO pollution data from a residential road and a busy motorway in Hyderabad, India, in preparation of the deployment of a larger scale, longer term wireless sensor monitoring system. Power conservation is an important issue as the sensor nodes are battery operated. We studied the characteristics of the collected data and designed an adaptive sampling algorithm, Bi-Scale temporal sampler, which adapts the sampling frequency to the statistics collected in real time. This design has incorporated practical engineering considerations including minimising electronic noise, sensor warm-up time and data characteristics. Results show that Bi-Scale sampler achieves better energy saving and statistical deviation ratio for our requirements than burst sampling and eSENSE sampling strategies, which are techniques popularly used in environmental monitoring applications.

In this demo, we demonstrate integration of cognitive radio capabilities, on the fly, with GSM base station (BTS) using OpenBTS architecture. The demonstration has following novel features: Cognitive sensing of the channel, in use by secondary user, while transmitting, On the fly shifting to free GSM subchannel without call drop, Works with existing GSM phones without any modification in hardware and software. Trials at IIT Hyderabad over the last one year has validated the viability of the system.

This paper investigates the sequential opportunistic channel sensing and access problem of an unlicensed or secondary user (SU) in presence of multiple licensed or primary users (PU). The SU senses one channel at a time for the presence of PU. The sensing is considered to be imperfect, i.e, the presence or absence of PU may not be detected correctly. The problem of selection of best channel amongst the sequentially scanned channels for the transmission of SU is modeled as an Optimal Stopping Problem (OSP) which also takes into account the cost of scan per channel. A simple threshold based policy is presented as a solution to OSP in which SU after sensing the channel to be free of PU activity checks the channel quality by comparing the channel gain with a threshold and if the channel gain is more than the threshold then SU selects this channel for transmission. The threshold is seen to be dependent on probabilities of channel occupancy and detection errors. The variation of threshold with respect to detection errors is analyzed and supported with plots. The plots for the variation are presented which match closely with the analytical results.

This paper addresses distributed sub-carrier bandwidth sizing and power allocation for the Secondary Users (SUs) in a Cognitive Radio (CR) network. The system entails an Orthogonal Frequency Division Multiplexing (OFDM)-based multi-user environment which deploys overlay spectrum sharing by the Primary Users (PUs) and SUs, and Frequency Division Multiple Access (FDMA) for the SUs. The objective of the formulated problem is maximizing each SUs throughput within a node power budget, interference threshold on the PU band, consideration of the interference caused to the neighboring SUs, as well as the coherence bandwidth of each SU. Motivated by the nonconvex structure of the problem and the need to execute the resource allocation in a distributed manner, we propose a sub-optimum algorithm to achieve the objective within the specified constraints. Simulation results highlight the contribution of the proposed distributed resource allocation algorithm and demonstrate its effectiveness.

The paper presents a new paradigm from the perspective of pervasive on-body computing through an innovative polymerized textile, which exhibits sensing and radiation properties. A radical, first of its kind, sensor fabricated from unsaturated polymer resin textile, establishes a dynamic link connecting human thermodynamics to electrical ambiance. A dynamic fabrication process of esterification and η-polymerization is developed, which is articulately arrested using an innovatively formulated retardant, yielding a permanent thermally unstable partially oriented yarn (tuPOY). A prudently established nontrivial interchange phenomenon is founded, presenting an inimitable calibration mechanism of the sensors and charting a novel relationship of exuberated energy to lattice kinetics of tuPOY. This meticulously researched conducting medium of tuPOY, fabricated from aromatic polyamides, also presents an avant-garde architecture for proliferation of electrical and thermal signals concomitantly between the sensors and its transmission circuit. A power generating unit (PGU) delineates the power mining from thermal energy dissipated from the body, presenting a new dimension in operational power dynamics. A textile composite antenna is premeditated exclusively from radiating tuPOY-based patch and substrate, an archetype reporting in published literature. The judiciously designed antenna, with tuPOY coupled as its patch, and substrate operate as shields against the radiations directed towards the body leading to a self-sustained sculpt. The back-end hardware of the test setup conceptualizes an automated physician machine (APM) presenting a standalone architecture. The artificial intelligence core of APM is modeled on weighted multiclass support vector machines (wmSVMs). The capturing of signal variations, devoid of any metallic components, presents a singular facet of research and amalgamates various interdisciplinary fields, while providing a robust architecture with minimum tradeoffs.

The major contribution of this paper is distributed power allocation for a multi-user multi-channel Cognitive Radio (CR) network which deploys overlay spectrum sharing by the Primary Users (PUs) and Secondary Users (SUs), and Frequency Division Multiplexing Access (FDMA) for the SUs. The transmit power of each SU is constrained by an individual node power budget, the interference threshold on the PU band, as well as the interference caused to the neighboring SUs. Motivated by the non-convex structure of the problem and the need to execute the power allocation distributedly, the problem is cast within a game theoretic frame-work. Pay-off functions for the SUs are formulated to suit the system model, and the Nash Equilibrium for the game is analyzed. Simulation results are provided to validate the mathematical concepts and to evaluate the performance of the proposed distributed power control algorithm in comparison with other relevant schemes.

Advances in 3G and 4G technology have offered many possibilities for developing novel applications using sensors embedded in hand held devices like cell phones. Mobility of cell phone based wireless sensor network has a critical issue of gathering sensed information in an energy efficient and delay sensitive manner. In this paper we provide a human mobility based stable clustering algorithm for data aggregation in single hop cell phone based sensor network incorporating mobility of cell phone users. We present a human mobility aware weighted clustering algorithm for data aggregation under Truncated Levy Walk (TLW) mobility model. Our approach is to select stable Cluster Heads (CH) to save the energy expenditure of network back bone formation. We have compared our algorithm with WCA [9] of mobile adhoc network and with MRECA [6] algorithm of mobileadhoc sensor network which we consider to be closely related with our work. WCA algorithm's mobility parameter is not effectively capturing mobility of human walk. Our Human mobility aware Weighted Cluster based Data Aggregation algorithm (Hm-WCDA) effectively captures human walk characteristics and thereby stabilizes the back bone network. We have evaluated performance of our algorithm primarily with stability related parameters such as number of dominant set (DS) updates, number of reaffiliations and number of cluster heads, which directly effects the energy consumption of the algorithm. The simulation results show that our algorithm is more energy-efficient and reduces the energy consumption by 12.5 percent as compared to MRECA and by 7 percent as compared to WCA for cluster radius of 400m.

The prime focus of this work is in describing a symbiotic architecture for a Cognitive Radio network, called Cognitive Relaying with Frequency Incentive. The incumbent Primary User (PU) of the spectrum, with a weak transmission link, seeks cooperation from the cognitive Secondary User (SU) nodes in its vicinity, and in return rewards them with incentive frequency bands for their own communication. We propose two schemes (Band-Set Minimization and Edge-by-Band Minimization) to achieve this objective. Cross-layer optimization problems are formulated to create the frequency incentive while ensuring a guaranteed throughput for the PU, as well as for the SU's to efficiently access this incentive. The proposed schemes are extensively compared, and simulation results are furnished to demonstrate their differences and individual merits from the perspective of both the PU and SUs.

In this paper we derive the optimal space-time power allocation scheme that minimizes the BER for MIMO fading channels when ideal channel state information (CSI) is available at the transmitter and receiver. We then show that this scheme achieves exponential diversity. Simulation results corroborating the theoretical analysis are then presented for MIMO Rician fading channels where we show that for two transmit and two receive antennas the presented scheme achieves more than 3 dB gain over best known Power Allocation Schemes.

In this paper we present a spatial domain method to fuse multifocus images. Here the fused image pixel is selected from one of the source images based on a novel selection criterion that utilizes the statistical properties of the neighborhood. The eigen value of the unbiased estimate of the covariance matrix of an image block depends on the strength of edges in the block and thus provides a good base for selecting/ rejecting a pixel, giving preference to the pixel with the higher eigen value and thus the sharper neighborhood. To prevent a noise pixel from getting selected as a fused image pixel, a continuity constraint is imposed on the selection criteria. The performance of the method have been extensively tested on several pairs of multifocus images and compared quantitatively with existing methods. Experimental results show that the proposed method improves fusion quality by reducing loss of information by almost 50% and noise by more than 95%. It also show that evaluation based on widely used criteria like entropy, gradient, deviation, may not be enough; as in some cases, these criteria are not consistent with the ground truth. It demonstrates that Petrovic metrics are in correlation with the ground truth as well as visual quality.

The prime focus of this work is in developing a protocol for a symbiotic architecture called Cognitive Relaying with Time Incentive for Multiple Primary Users (CRTI-M). The rationale of CRTI-M is that the Primary Users (PUs) of a Cognitive Radio (CR) network, with weak transmission links, seek cooperation from the cognitive Secondary User (SU) nodes in their vicinity, and in return reward them with an incentive time for their own communication. Each PU has its own distinct bandwidth of operation; however, when relaying through the SU network it can use the bandwidth of the other PUs as well to enhance its throughput. A cross-layer optimization problem is formulated to that effect in a multi-hop multi-channel SU network. The time incentive that should be awarded to the SUs by each PU is analyzed by means of a utility-based decision-making process, and its efficient utilization is proposed. To make the CRTI-M scheme practically realizable, a MAC scheduling protocol is devised within a unified framework of the PUs and SUs. Simulation results are furnished to demonstrate the proof of concept.

This work is directed towards a symbiotic architecture called Cognitive Relaying with Time Incentive (CRTI) for multiple Primary Users (PUs) in a Cognitive Radio network. The rationale of CRTI is that the PU with a weak transmission link seeks cooperation from the cognitive Secondary User (SU) nodes in its vicinity, and in return rewards them with an incentive time for their own communication. Since each PU has its own distinct bandwidth of operation, it is crucial to decide the most favorable use of the spectrum when relaying through the SU network. We propose two schemes-Band-Restricted Relaying and All-Band Relaying to achieve this objective. Cross-layer optimization problems are formulated for the relaying schemes which create the time incentive, as well as for the SUs to efficiently access this incentive. The proposed schemes are compared, and simulation results are furnished to demonstrate their differences and individual merits from the PU and SU's perspective.

Cognitive Radio (CR) improves spectral efficiency by allowing unlicensed (secondary) users to concurrently operate with licensed (primary) users. The side-lobe interference from an OFDM-based secondary user (SU) causes unwanted interference to the primary user (PU) of the spectrum. While interference mitigation to the PU spectral band has received a lot of attention in recent literature, the novelty of our work is in considering sub-bands of the PU, and ensuring that the interference to each of them is below a specified threshold. The approach is realistic and more effective in protecting the PU from the SU interference. We formulate an optimization problem for power allocation for a single SU to maximize its throughput, within a power budget, and an interference constraint on each PU sub-band. After extensively analyzing the solution form, we propose an iterative algorithm to meet the aforementioned objective. A power allocation algorithm is also proposed for a multiple SU scenario, which involves assigning sub-carriers to each user, besides the power and PU interference constraint, to maximize the sum throughput. Simulation results are provided, for both single and multi-user cases, which indicate that the proposed algorithms outperform those which have been previously presented in literature.

Exact expressions are obtained for the bit error rate (BER) for coherent and noncoherent decode and forward (DF) cooperative systems with upto three relays between the source and destination. The piecewise linear (PL) combiner is employed at the receiver. BER analysis is done using a contour integral approach for evaluating the Gil-Pelaez integral involving the characteristic function (CF) of the decision variable. This removes restrictions on relay location, imposed by the direct approach. Simulation results are provided to support the analysis and the relay diversity gain is demonstrated through BER plots.

The statistics of gamma conditionally Gaussian (CG) random variables are derived in closed form. These variables can be loosely defined to be normally distributed with mean and variance proportional to a gamma random variable. In this paper, we provide exact expressions for the bit error rate (BER) for single relay maximum likelihood (ML) decode and forward (DF) cooperative systems in Nakagami-m fading for binary phase shift keying (BPSK). This is done by expressing the ML decision variable in terms of functions of gamma CG random variables. For the piecewise linear (PL) approximation to the ML detector, a closed form expression for the BER is obtained. Simulation results are provided to verify the validity of the derived analytical expressions.

In our work, we consider a querying application of a Multihop Cell phone Sensor Networks (MCpSN). It requires data to be sensed within a time window and further uploaded at the Querying Access Point within a time deadline. Considering a Random Waypoint distribution of cell phone users, we propose a novel, energy-efficient Spatio-Temporal Power Adaptive (STPA) protocol for this application. STPA is compared to purely spatially/temporally adaptive schemes through simulations.

This paper proposes new MAC layer based back-off and retransmission strategies for throughput enhancement, in broadcast transmissions of inter vehicular communication. We present adaptive back off strategy which is based on the concept that there exists an optimum contention window (CW) period in a broadcast CSMA/CA system, given the fixed number of users. We also propose a simple probabilistic approach for retransmission which is based on analyzing the local information. Simulation results shows the improvement in the throughput using adaptive back-off and probabilistic retransmission strategies compared to fixed back-off strategy.

A hybrid error concealment scheme for H.264/AVC that uses the concept of watermarking, Overlapping Boundary Matching Algorithm (OBMA) and Projection Onto Convex Sets (POCS) is proposed in this paper. The advantage of this scheme is that, watermarking is performed during the encoding process itself. The specific features of both intra coded and inter coded frames are exploited for this purpose. The proposed method chooses watermarking or OBMA or POCS or a combination of them selectively for error concealment which minimizes computational complexity. Simulations are performed to show that the proposed method works well with those videos which have abruptly moving blocks.

Cognitive relaying is a recent communication paradigm that increases network coverage and improves spectral efficiency by allowing unlicensed (secondary) users to concurrently operate with licensed (primary) users. The major contribution of this paper is in proposing a bit allocation algorithm to be used in a cognitive relay network. The dynamic spectrum access in an OFDM-based cognitive relay network may lead to unwanted interference to the primary user. Assuming knowledge of the instantaneous channel gains for all links in the network and the primary user band, the proposed algorithm meets the twofold challenge of achieving optimum secondary system throughput while ensuring minimum interference to the primary. In its first pass, the proposed algorithm achieves optimum number of bits per subcarrier. However, in the process of achieving integer granularity for practical constellation sizing, the interference constraint may be violated. A second pass of the algorithm tackles this condition by executing a interference-based greedy bit removal. Simulation results are provided, which are indicative of the fact that the proposed algorithm exhibits enhanced performance for both primary and secondary users in the cognitive radio network.

Energy optimized Path Unaware Layered Routing Protocol (E-PULRP) for dense 3D Underwater Sensor Network (UWSN) is proposed and analysed in this paper. In the proposed E-PULRP, sensor nodes report events to a stationary sink node using on the fly routing. E-PULRP consists of a layering phase and communication phase. In the layering phase, a layering structure is presented wherein nodes occupy different layers in the form of concentric shells, around a sink node. The layer widths and transmission energy of nodes in each layer are chosen taking into consideration the probability of successful packet transmission and minimization of overall energy expenditure in packet transmission. During the communication phase, we propose a method to select intermediate relay nodes on the fly, for delivering packets from the source node to sink node. We develop a mathematical framework to analyse the energy optimization achieved by E-PULRP. We further obtain expressions for throughput, delay and derive performance bounds for node densities and packet forwarding probabilities, for given traffic conditions. A comparison is made between the results obtained based on simulations and analytical expressions. The energy efficiency is also demonstrated in comparison with existing routing protocol for underwater sensor networks.

Maximum-likelihood (ML) detectors for single-input-single-output (SISO) relay systems are well known. In this paper, closed form expressions for the bit error rate (BER) for multiple-input-multiple-output (MIMO) relay systems employing ML based decode and forward (DF) cooperative diversity are obtained. The DF operation at the relay intelligently employs the multiple antennas available at the relay for receive diversity on the source-relay link and space-time coding (STC) on the relay-destination link resulting in an extension of the ML detection rule for SISO systems to MIMO systems as well. For the piecewise linear (PL) approximation to the ML detector, exact expressions for the BER are obtained for single-relay systems with both the source and relay supporting multiple antennas. For a multirelay system, each relay having multiple antennas, approximate expressions for the BER are obtained. This is done by finding the statistics of conditionally Gaussian (CG) random variables, that appear in the decision variable. Through numerical results obtained from the BER expressions, it is shown that using multiple antennas at the source as well as the relay leads to significant improvement in BER performance.

In this paper, we consider moving event localization using data collected from a Multihop Cellular Sensor Network (MCSN). The main contribution of the paper is a novel structure- free Distributed Velocity Dependent (DVD) waiting time based protocol. We compare the performance of the proposed DVD protocol with that of a Centralized approach and the existing Randomized Waiting time (RW) protocol. Simulation results show that DVD exhibits a performance superior to RW with respect to center of event localization error and end-to-end delay. We also present simulation results on the the sensitivity analysis based comparison between DVD, RW and the Centralized scheme.

The bit error rate (BER) performance analysis of maximum-likelihood (ML) based decode and forward (DF) cooperative diversity systems has been a subject of considerable interest. Exact analysis of ML-DF transmission has been considered a challenging problem due to the nonlinear characteristic of the ML detector. In this paper, we provide exact expressions for the BER of ML-DF cooperative systems employing a single relay. We extend these results to the case of multiple relays for the piecewise linear (PL) combiner, that is known to be a close approximation of the ML detector. This is done by using a novel theory of conditionally Gaussian random variables. By expressing the ML decision variable in terms of functions of conditionally Gaussian variables, exact expressions for the BER of the ML-DF system are obtained. Through simulation results, we verify the validity of the derived analytical expressions.

Tight closed form lower bounds for the average bit error rate (BER) are derived for a dual hop cooperative network employing nonregenerative relays for the multiple input multiple output (MIMO) relay channel experiencing Rayleigh fading. The bounds are obtained for three different nonregenerative relaying schemes. The lower bounds for the BER are obtained using the moment generating function (MGF) approach by evaluating the MGF of the end-to-end equivalent signal to noise ratio (SNR) of the system. From the BER expressions obtained, we also show that the diversity order for the MIMO relay cooperative system with each relay having M antennas increases approximately by a factor M from that of a system with single-antenna relays. Simulation results confirm that the analytical expressions for the lower bounds are very tight and can thus be used to get approximate values of the BER.

Closed form expressions for the bit error rate (BER) for a multiple input multiple output (MIMO) relay system employing maximum likelihood (ML) based decode and forward (DF) cooperative diversity are obtained. The DF operation at the relay involves maximal ratio combining (MRC) on the source-relay link and space-time coding (STC) on the relay-destination link. Exact expressions of the BER are obtained for the case of a single relay supporting two antennas. For a system employing a large number of relays, each having two antennas, approximate expressions for the BER are obtained using the piecewise linear (PL) approximation for the ML detector. This is done by finding the statistics of conditionally Gaussian random variables, that appear in the decision variable. The validity of the analytical expressions is then verified through simulations. Through numerical results obtained from the BER expressions for large number of relays, it is then shown that the loss in diversity order due to DF can be compensated by using multiple antennas at the relay.

In this paper we derive and propose constrained blind DS-CDMA UWB multiuser detection algorithm to suppress the multi access interference (MAI) in multipath channels, and studied its bit error probability performance in the presence of narrowband interference. Variance of the receiver output is minimized subject to appropriate constraints. Receiver does not require spreading code knowledge of all users other than that of the user of interest. Simulation results show that bit error probability performance of the proposed algorithm is better than that of adaptive minimum mean square error (MMSE) detector in multipath channels, and much better in the presence of multipath channels with narrow-band interference.

Finding an optimal routing path in adverse underwater environment in an Underwater Sensor Network (UWSN) has always been a challenging task. Maintaining the network life for a longer duration by utilizing nodes' energy is also critical. By taking these issues into consideration we propose an Energy optimized Path Unaware Layered Routing Protocol (E-PULRP) for dense underwater 3D sensor networks. An up-link transmission is considered, where a set of underwater sensor nodes report events to the stationary sink node. E-PURLP consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric spheres, around a sink node. The radii of the concentric spheres as well as the transmission energy of the nodes in each layer are chosen considering probability of successful packet transmissions and minimum overall energy expenditure. In the second phase (communication phase), we propose a method to choose intermediate relay nodes andontheflyrouting algorithm for packet delivery from source node to sink node across the identified relay nodes. The energy analysis of the E-PULRP has been carried out and shown that it is energy efficient. The performance in terms of throughput and delay is also analyzed.

In this paper analytical relationship between transmission range and the network connectivity is obtained as a function of number of nodes for CDMA based multihop cellular network (MCN). We show that for a network of n uniformly distributed nodes in a single cell of unit radius, the transmission range r should be sufficiently larger than radic(2 ln n/n-1) to achieve asymptotic full connectivity. The distribution of the nodes may not be uniform in case of mobility. In such case a mobility model dependent lower bound on transmission range is obtained. We show that for RWP model the lower bound for the nodes which lie completely inside the cell is same as the corresponding uniform node distribution case. However, for nodes on boundary the transmission range lower bound is3radic(ln/n) which is larger than the corresponding uniform node distribution case. Our findings show that more transmission range is required at the boundary to establish better connectivity and hence a variable transmission range control mechanism is necessary. In addition to lower bound, we also propose a method to choose the optimal value of transmission range using a scheduling mechanism and ensure that the optimal value is always greater than the lower bound. Thus obtained transmission range could be used to select the transmission power of the nodes in a meaningful way and hence the nodes' isolation could be avoided and the spectral efficiency can be increased. We demonstrate empirically, that the proposed transmission range control mechanism increases the network connectivity as well as spatial reuse of the resources.

We propose a path unaware layered routing protocol (PULRP) for dense underwater 3D sensor networks. An uplink transmission is considered, where a set of underwater sensor nodes report events to the sink node. PURLP algorithm consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric spheres, around a sink node. The radius of the concentric spheres is chosen based on probability of successful packet forwarding as well as packet delivery latency. In the second phase (communication phase), we propose a method to choose the intermediate relay nodes and an on the fly routing algorithm for packet delivery from source node to sink node across the chosen relay nodes. The proposed algorithm, PULRP finds the routing path on the fly and hence it does not require any fixed routing table, localization or time synchronization processes. Our findings show that the proposed algorithm has a considerably better successful packet delivery rate compared to the under water diffusion (UWD) algorithm proposed in the paper by Lee et al. (2007) and Dijkstra's shortest path algorithm. In addition the delay involved in PULRP is comparable with that of UWD.

Antenna sharing by multiple users using cooperative diversity has been shown to mimic the performance of traditional multiple input, multiple output (MIMO) systems. While this was originally done using a single antenna at each relay, the benefits of cooperative diversity based on multiantenna relays has been a subject of considerable interest. Maximum likelihood (ML) detectors for cooperative diversity through single antenna relays are well known. In this paper, we propose to combine the benefits of cooperative diversity, and transmit and receive diversity offered by MIMO systems. This is done within the ML based decode and forward (DF) cooperative diversity framework, by using multiple antennas at each relay, through ML based processing on the source-relay link and space-time coding on the relay-destination link. Through simulations, we then show that the performance of MIMO relay based systems is superior to those having a single antenna at each relay.

Watermark retrieval in wavelet domain has been proved to be more robust from significant coefficients than high absolute coefficients when a single copy of the watermark is embedded in the original data. The highest absolute coefficients refer to the coefficients with the highest absolute values in any selected band and significant coefficients refer to the coefficients with the highest significance factor with respect to their inter-band dependencies in a wavelet transformed image. The watermark energy can be maximised at each of the selected coefficient by quantising it to the maximum allowable level suggested by a human visual system model. However, as the attacks become very severe, a single copy of the watermark is not sufficient for correct retrieval. Hence, the authors propose a method of optimum retrieval of the watermark from multiple embedded copies under very severe attacks. The authors propose to use the Chair–Varshney decision fusion rule to decide each bit in the watermark instead of the majority rule for optimum watermark retrieval. Simulations are performed to show the superiority of the method with different numbers of watermark copies under various attacks. Extensive simulations are carried out to plot the receiver operating characteristics in order to compare the proposed method with the majority rule.

Abstract We propose path unaware layered routing protocol (PULRP) for 2D underwater sensor networks (UWSNs) with mobile nodes. The steady‐state distribution of mobile nodes in UWSNs is nonuniform in general. Hence, we use a mobility model‐dependent node distribution. The proposed PULRP algorithm consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric circles, around a sink node. The radii of the concentric circles are chosen based on equal distribution of nodes in every layer. A distributed power control mechanism is also introduced. The power level of nodes in a particular layer is chosen such that communication occurs only with nodes in the next layer. In PULRP, we consider multihop communication from source to sink. Therefore, in the second phase (communication phase), we propose a method to choose the intermediate relay nodes and an on the fly routing algorithm for packet delivery from source node to sink node across the chosen relay nodes. The proposed algorithm, PULRP finds the routing path on the fly and hence, it does not require any fixed routing table, localization, or time synchronization processes. We demonstrate the performance of PULRP using random waypoint (RWP) mobility model in a simulated underwater environment. Our findings show that the proposed algorithm has considerably better throughput (successful packet delivery rate) compared to the underwater diffusion (UWD) algorithm for various node densities as well as node velocities. In addition, the delay performance of PULRP is also better than that of UWD. Copyright © 2008 John Wiley & Sons, Ltd.

In order to reduce the complexity of the mobile receiver, we develop a linear preceding filter based only on the statistical knowledge of the channel. Moreover, the proposed prefilter (precoder) is based on minimizing the probability of error in downlink multiuser transmission. We investigate two approaches for the proposed algorithm. In the first approach we consider a common FIR preceding filter for all users, and jointly minimize the probability of error of all users. In the second approach, we assume separate precoders for each user which are obtained by minimizing the probability of error for the respective user. In order to fully utilize the knowledge available at the transmitter, in both the approaches, the filter weights are computed conditioned on the transmitted bit vector sequence. This also makes the computation of the optimal prefilter coefficients linear in the number of users. We compare the results of the proposed approach, with results based on assuming complete knowledge of the channel. Simulation results clearly show that precoders based only on the statistical knowledge of channel, do provide acceptable BERs. Moreover, individual precoders provide better BER as compared to joint precoders.

We propose a CDMA-OFDM access mechanism for Multihop Cellular Networks (MCN). We construct groups within the MCN, where each group comprises of a source node, a destination node, their intermediate relay nodes and assign a correlated PN sequence to each such group. Within a particular group, a single carrier is assigned to each intermediate hop. The sub carriers assigned to the intermediate hops in a given group are mutually orthogonal. Hence the proposed OFDM is FDMA in nature. The sub carriers and transmit power levels to the relay nodes are assigned in such a way as to maximize the end-to-end throughput. The end-to-end throughput is formulated by assuming a Rayleigh flat fading channel between nodes. We present a method to reuse the PN codes assigned to the groups in a particular cell. We also introduce a novel architecture for MCN. Simulation results show that the proposed access mechanism achieves better end-to-end throughput and bit error rate (BER) performance as compared to standard access mechanisms like CDMA and OFDM-FDMA. Furthermore the proposed access mechanism has considerably higher BER performance in the presence of multiple transmit sources.

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using a priori information about the target dynamic. We propose a neural-network-based tracking algorithm, incorporating a interacting multiple model and show that it is possible to track both maneuvering and nonmaneuvering targets simultaneously in the presence of dense clutter. Moreover, it can be used for real-time application. The proposed method overcomes the problem of data association by using the method of expectation maximization and Hopfield network to evaluate assignment weights. All validated observations are used to update the target state. In the proposed approach, a probability density function (pdf) of an observed data, given target state and observation association, is treated as a mixture pdf. This allows to combine the likelihood of an observation due to each model, and the association process is defined to incorporate an interacting multiple model, and consequently, it is possible to track any arbitrary trajectory

We propose an effective technique using a wavelet-based temporal decomposition algorithm to detect single-pixel targets with motion from frame to frame. We next integrate the proposed detection algorithm with an interacting multiple-model method and multiple filter bank approach to provide an effective solution for tracking multiple single-pixel nonmaneuvering and maneuvering targets. Through Monte Carlo simulations, we establish the efficiency and robustness of the proposed approach. Based on exhaustive empirical study, we demonstrate the effectiveness of our proposed approach in tracking multiple single-pixel targets in a sequence of infrared images with clutter and occlusion due to moving clouds in airborne applications.

One of the key issue in microfinance is credit risk assessment. In this paper, exploiting fuzzy symbolic clustering, we have developed a fast turn around time microcredit risk scoring system. From field studies we identified a large number of parameters for credit risk assessment; most of them were subjective and thus our preference for fuzzy symbolic approach. We have proposed two cell phone based models to execute the developed microcredit risk assessment scheme. Experimental results are presented to validate our approach

Interactive mobile robots are an active area of research. This paper presents a framework for designing a real-time vision based hand-body dynamic gesture recognition system for such robots. The said framework works in real world lighting conditions with complex backgrounds, and can handle intermittent motion of the camera. We present here a novel way in which the motion history image (MHI) and the motion energy image (MEI) is built. We propose a robust combining of the motion and color cues and we call this image as motion color image (MCI). The input signal is captured by using monocular color camera. Vision is the only feedback sensor being used. It is assumed that the gesturer is wearing clothes that are slightly different from the background. Gestures are first learned offline and then matched to the temporal data generated online in real time. We have tested this on a gesture database consisting of 11 hand-body gestures and have recorded recognition accuracy up to 90%. We have partially implemented and testing the system for Sony's Aibo robot dog using remote framework (RFW) SDK by Sony

In order to transmit pre-encoded digital video over heterogeneous networks, it becomes necessary to employ transcoding techniques that convert pre-encoded video streams into streams having different bit rates and quality. Video transcoders save computational overheads incurred in the IDCT and DCT operations, by performing motion compensation (MC) in the frequency (block-DCT) domain (MC-DCT). Most of the computational complexity of the frequency-domain video transcoder comes from the MC-DCT operation. MC-DCT involves pre- and post-multiplication of 8 /spl times/ 8 DCT blocks with 8 /spl times/ 8 matrices derived from the motion vectors of that block. The specific contribution of this work is towards developing a unified framework for handling all MC-DCT modes for MPEG-2 video input to a frequency-domain video transcoder. Use of this framework can lead to a potential saving of up to forty-eight 8 /spl times/ 8 block multiplications and sixteen 8 /spl times/ 8 block additions per reconstructed 16 /spl times/ 16 macroblock.

Interactive mobile robots are an active area of research. This paper presents a framework for designing a real-time vision based hand-body gesture user interface for such robots. The said framework works in real world lighting conditions, with complex background, and can handle intermittent motion of the camera. The input signal is captured by using a singular monocular color camera. Vision is the only feedback sensor being used. It is assumed that the gesturer is wearing clothes that are slightly different from the background. We have tested this framework on a gesture database consisting of 11 hand-body gestures and have recorded recognition accuracy up to 90%.

Demodulators based on the minimum mean squared error (MMSE) criterion are optimal for 'ideal' AWGN channels. However, in the case of wireless multipath channels, wherein ISI is inherent, MMSE based algorithms do not offer the optimal framework for demodulation. Minimum probability of symbol error (MPOSE) based detectors have been shown to perform significantly better than MMSE based approaches in these scenarios under a variety of modulation and multiplexing schemes. High computational complexity, however, prohibits the use of these algorithms in practical communication systems. The major challenge now is to reduce the computational burden of the MPOSE algorithms. This paper, as a step in this direction, discusses two already published MPOSE algorithms and proposes a modification to them whereby we not only achieve significant gains in computational complexity but also improve the BER performance. The system is also found to be robust to error propagation due to wrong estimation of the ISI term.

The two major challenges encountered in the design of high speed wireless networks are the impairments caused by the propagation channel and scarcity of the electromagnetic spectrum. Orthogonal frequency division multiplexing (OFDM) simplifies the former problem by transforming a frequency selective channel into a frequency flat one. On the other hand, spectral efficiency can he achieved by space division multiple access. These two techniques can therefore he combined to achieve reliable wireless communication at high data rates. The resulting system, however, suffers from co-channel interference. In an earlier paper we proposed a class of demodulators that effectively combat this interference. These demodulators are based on the principle of minimizing the error probability in linear demodulation of the received symbols. The demodulators show considerable improvement in BER compared to the MMSE and adaptive RLS demodulation techniques. These demodulators however assume a known channel at the receiver. In this paper we dispense with this assumption by developing training based adaptive channel estimator, which is then coupled with these demodulators. The demodulators still show considerable gain in performance over the MMSE and RLS demodulators even with the estimated channel.

Detection and tracking of approaching targets in an infrared (IR) image sequence is important for surveillance applications. In this paper an algorithm is proposed which provides a complete solution (track while scan) for detection and tracking for IRST system. The proposal method uses only motion as a cue to detect the target. Detection is followed by tracking. In a real scenario, the movement of a target is arbitrary and no a priori information is available. We propose a tracking method which tracks maneuvering and nonmaneuvering targets simultaneously using a filter bank. The switch-over amongst the filters is based on a single-step decision logic.

We propose an algorithm for detection and tracking of small and fast moving objects, like a ping pong ball or a cricket ball, in sports video sequences. For detection, the proposed method uses only motion as a cue; moreover it does not use any texture information. Our method is able to detect the object with very low contrast and negligible texture content. Along with detection, we also propose a tracking algorithm using the multiple filter bank approach. Thus we provide a complete solution. The tracking algorithm is able to track maneuvering as well as non-maneuvering movements of the object without using any a priori information about the target dynamics

Orthogonal frequency division multiplexing (OFDM) is attractive for wireless communication because it simplifies the problems posed by the frequency and time selective nature of a wireless channel. This simplification is achieved by converting a frequency selective channel into a frequency flat one. On the other hand, space division multiple access (SDMA) leads to spectral efficiency. These two techniques, when combined, can therefore achieve reliable wireless communication at high data rates. The resulting system however suffers from co-channel interference. In this paper we develop two linear demodulators that effectively combat this interference. The demodulators are based on the principle of minimizing the probability of error in linear demodulation of the received symbols. The first demodulator, minimum probability of error (MPOE), minimizes the probability of error of each transmitting source separately. The second demodulator, minimum joint probability of error (MJPOE), minimizes the joint error probability of the sources. The demodulators are computed iteratively by using the method of gradient descent. Both demodulators show considerable improvement in BER compared to the MMSE demodulator and enjoy fast convergence. At a BER of 10-3, the MJPOE shows a gain of over 7 dB SNR over the MMSE demodulator and 10 dB SNR over the training based adaptive RLS demodulator. For the MPOE demodulator, the corresponding gains are 5.5 and 8.5 dB SNR.

Direct-sequence code-division multiple-access (DS-CDMA) is a popular multiple-access technology for wireless communications. However, its performance is limited by multiple-access interference and multipath distortion. Multiuser detection and space-time processing are two signal processing techniques employed to improve the performance of DS-CDMA. Two minimum probability of error-based space-time multiuser detection algorithms are proposed in this paper. The first algorithm, minimum joint probability of error (MJPOE), aims to minimize the joint probability of error for all users. The second algorithm, minimum conditional probability of error (MCPOE), minimizes the probability of error of each user conditioned on the transmitted bit vector, for each user individually. In both the algorithms, the optimal filter weights are computed adaptively using a gradient descent approach. The MJPOE algorithm is blind and offers a bit-error-rate (BER) performance better than the nonadaptive minimum mean squared error (MMSE) algorithm, at the cost of higher computational complexity. An approach for reducing the computational overheads of MJPOE using Gram-Schmidt orthogonalization is suggested. The BER performance of the MCPOE algorithm is slightly inferior to MMSE, however, it has a computational complexity linear in the number of users. Both blind and training-based implementations for MCPOE are proposed. Both MJPOE and MCPOE have a convergence rate much faster than earlier known adaptive implementations of the MMSE detector, viz. least mean square and recursive least squares. Simulation results are presented for synchronous single path channels as well as asynchronous multipath channels, with multiple antennas employed at the receiver.

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. We propose a sequential probabilistic multiple hypotheses tracking (PMHT) based algorithm using interacting multiple modelling (IMM), namely the IMM-PMHT algorithm. Inclusion of IMM enables any arbitrary trajectory to be tracked without any a priori information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the measurement origin. It operates in an iterative mode using an expectation-maximization (EM) algorithm. The proposed algorithm uses only measurement association as missing data, which simplifies E-step and M-step. It is computationally more efficient, and an important characteristic of our proposed algorithm is that it operates in a single batch model, i.e. sequential, and hence can be used for real time tracking.

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no a priori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion (/spl plusmn/20 pixels) using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds. The use of multiple filters is not new, but the novel idea here is that it uses single-step decision logic to switch over between filters. Our approach does not use any a priori knowledge about maneuver parameters, nor does it exploit a parameterized nonlinear model for the target trajectories. This is in contrast to: (i) interacting multiple model (IMM) filtering which required the maneuver parameters, and (ii) extended Kalman filter (EKF) or unscented Kalman filter (UKF), both of which require a parameterized model for the trajectories. We compared our approach for target tracking with IMM filtering using EKF and UKF for nonlinear trajectory models. UKF uses the nonlinearity of the target model, where as a first order linearization is used in case of the EKF. RMS for the predicted position error (RMS-PPE) obtained using our proposed methodology is significantly less in case of highly maneuvering target.

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no a priori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion (/spl mnplus/20 pixels) using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds. The use of multiple filters is not new, but the novel idea here is that it uses single-step decision logic to switch over between filters. Our approach does not use any a priori knowledge about maneuver parameters, nor does it exploit a parameterized nonlinear model for the target trajectories. This is in contrast to: (i) interacting multiple model (IMM) filtering which required the maneuver parameters, and (ii) extended Kalman filter (EKF) or unscented Kaiman filter (UKF), both of which require a parameterized model for the trajectories. We compared our approach for target tracking with IMM filtering using EKF and UKF for non-linear trajectory models. UKF uses the nonlinearity of the target model, where as a first order linearization is used in case of EKF. RMS for the predicted position error (RMS-PPE) obtained using our proposed methodology is significantly less in case of highly maneuvering target.

In this paper, we consider the problem of supporting video transmission over Bluetooth public access (Blue PAC) networks. BluePAC networks are an example of what are broadly called wireless personal area networks (WPAN). We compare BluePAC networks with WPANs based on the IEEE 802.11 technology. We compare the two based on the following performance measures: link delay distribution and packet loss rate. We also see how the performance of a video connection in a BluePAC network can be enhanced by reserving bandwidth by means of a deficit round robin (DRR) polling algorithm in Bluetooth. To perform the above the above study, we have used network models provided by the network simulator (NS-2), extended to model Bluetooth by Bluehoc from IBM. The results show that Bluetooth performs significantly better than IEEE 802.11 MAC in terms of packet loss rates, while 802.11 is better in terms of the packet delay distribution.

A major requirement for the mobile Internet is the wireless network access for handheld personal devices. As an alternative to the expensive 3G networks, which share 2 Mbps in several square kilometers, the use of short-range wireless technologies like Bluetooth (sharing 1 Mbps among just 7 users), has been proposed. This is much more economical as it works in the unlicensed band and leverages the existing wired broadband infrastructure. In such a Bluetooth network, however, the mobiles will move out from the range of one access point into that of another more frequently, requiring seamless handoff. We discuss a new algorithm to support fast handoff at the wireless layer. Our method exploits the continuity of the user's path and existing connection information at the older access point to reduce the handoff delay. It inter-works with IP micromobility protocols, such as cellular IP, for managing other mobility related issues at layer 3. Simulations reveal that our proposed method reduces the handoff delay by more than an order of magnitude and significantly enhances the bandwidth utilization.

DS-CDMA is a popular multiple access technology for wireless communication. The performance of DS-CDMA is limited by multiple access interference and multipath distortion. Multiuser detection and space-time processing techniques are employed to improve the performance of DS-CDMA. An adaptive space-time multiuser detector based on the minimum probability of error (MPOE) criterion is proposed here for an asynchronous multipath DS-CDMA channel, with the receiver employing multiple antennas. The receiver structure is derived for gradient descent based adaptation. A blind version of the receiver is also proposed. It is seen that the MPOE receiver offers BER performance comparable to the optimal non-adaptive MMSE detector in steady state, however with much lower computational complexity and a faster convergence rate compared to other adaptive implementations of the MMSE detector (LMS, RLS).

In this paper, we propose tracking and control algorithms for controlling network congestion with bursty traffic modeled by multifractal cascade processes. The multifractal multiplicative cascades are characterized by the Holder exponents. The value of the Holder exponent at an interval indicates the burstiness in the traffic at that point. This value has to be estimated and used for the estimation of the congestion and predictive control of the traffic policing in networks. The estimation of the local Holder exponent can be done by employing the wavelet transforms and a Kalman filter based predictor to predict the burstiness of the traffic.

In recent years, empirical analysis of data obtained from various systems have shown the existence of multifractal behavior. The traditional methods used for the analysis of multifractals are based on thermodynamical formalisms which are difficult to compute. In this paper we propose a new method based on entropy measurement to identify the presence of multifractal nature in physical systems.

Human face images form the important database in police departments, banks, security kiosks, and they are also found in abundance in day-to-day life. In these databases the important content, of course, is the face region. We present a highly efficient system that detects the human faces in the wavelet transform for discriminative quantization to achieve high perceptual quality content-based image coding technique. The proposed method gives superior subjective performance over JPEG without sacrificing the performance in the rate-distortion spectrum.

Clusters of microcalcifications which appear like small white grains of sand on mammograms are the earliest signs of breast cancer. In this work we employ a Gabor filter bank for texture analysis of mammograms to detect microcalcifications. A subset of the Gabor filter bank with a certain central frequency and different orientations is used to obtain the Gabor-filtered images. The filtered images are then subjected to a histogram based threshold to obtain binary images. Feature vectors are computed using the binary images. A k-means clustering algorithm with a variance scaled Euclidean distance is used for segmentation of the image.

A transform domain face recognition approach is presented. The DCT is coupled with the HMM to achieve a recognition rate of 100% on ORL face database of 40 subjects with 10 images per subject. The recognition time for ORL database is little over 2 Sec. 5 images of a subject are used to train HMM and remaining 5 are used for recognition test. The proposed method is tested on another face data base of 249 subjects with 3 training images and 4 test images per subject. The recognition rate is 90%. A test of recognition is carried out at different resolutions with recognition rate varing from 100% to 95% depending on the resolution. Further, a simple scheme is proposed to incorporate rejection of images of new subjects. On ORL database 100% rejection occurs for the images of new subjects.

In this paper we present a new hybrid compression scheme for videoconferencing and videotelephony applications at very low bit rates (i.e., 32 kbits/s). In these applications, human face is the most important region within a frame and should be coded with high fidelity. To preserve perceptually important information at low bit rates, such as face regions, skin-tone is used to detect and adaptively quantize these regions. Novel features of this coder are the use of overlapping block motion compensation in combination with discrete wavelet transform, followed by zerotree entropy coding with new scanning procedure of wavelet blocks such that the rest of the H.263 framework can be used. At the same total bit-rate, coarser quantization of the background enables the face region to be quantized finely and coded with higher quality. The simulation results demonstrates comparable objective and superior subjective performance when compared with H.263 video coding standard, while providing the advanced feature like scalability functionalities.

A definition of multiresolution analysis (MRA) of Gaussian processes is proposed. The problem, in a natural way, reduces to the MRA of the associated reproducing kernel Hilbert space. We then show that for processes synthesized from Gaussian white process by fractional integration of order /spl alpha//spl ges/1, this definition is applicable. The MRA results in an orthogonal expansion of these processes. The region of interest is the positive real line. Using this representation then a decomposition of a wider class of Gaussian processes is given. This representation is multiscale in two ways: firstly, the Gaussian process is split into various component processes characterized by the smoothness of their sample paths and secondly, each of these component processes has a MRA as defined in this paper.

The determination of probability of neurotransmitter release from neuronal release sites and their electrical characterization is an issue of central interest in neurophysiology. For autonomic nerves, this can be done by analysing the inflexions in the rising phases of the evoked junction potentials (EJPs) recorded from smooth muscle. Since these inflexions contain time-varying frequency information, we have applied methods of time-frequency analysis based upon wavelet transforms on EJPs to characterize autonomic neuronal function. We find that these methods allow accurate and convenient characterization of individual release sites, and that their probability of release falls between 0.002 and 0.003. These results are compared with those reported earlier using analogue filtering techniques. The present method is advantageous as regards automation, accuracy and suppression of noise.

Two new schemes are presented for finding human faces in a photograph. The first scheme approximates the unknown distributions of the face and the face-like manifolds wing higher order statistics (HOS). An HOS-based data clustering algorithm is also proposed. In the second scheme, the face to non-face and non-face to face transitions are learnt using a hidden Markov model (HMM). The HMM parameters are estimated corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. Experimental results are presented on the performance of both the schemes.

Image interpretation consists of interleaving the low-level task of image segmentation and the high-level task of interpretation. The idea being that the interpretation block guides the segmentation block which in turn helps the interpretation block in better interpretation. In this paper we develop a joint segmentation and image interpretation scheme using the notion of joint hidden Markov model (HMM) for probabilistic modeling of spatial relationship. We find the optimal interpretation labels, which are nothing but the optimal state sequence of the HMM.

A transform domain approach coupled with Hidden Markov Model (HMM) for face recognition is presented. JPEG kind of strategy is employed to transform input subimage for training HMMs. DCT transformed vectors efface images are used to train ergodic HMM and later for recognition. ORL face database of 40 subjects with 10 images per subject is used to evaluate the performance of the proposed method. 5 images per subject are used for training and the rest 5 for recognition. This method has an accuracy of 99.5%. The results, to the best of knowledge of the authors, give the best recognition percentage as compared to any other method reported so far on ORL face database.

This paper presents a new signal de-noising algorithm using wavelets. We have developed a filtering scheme in the wavelet domain, that involves selective smoothing at each scale of the time-frequency plot. The amount of smoothing is controlled by regularizing factors, and gradient-based switches are used to avoid distortion of signal features. The algorithm is seen to compare favorably to that of Mallat et al. (1992), as it is able to recover both the smooth portions as well as Brownian texture in the input, from the noisy signal.

The embedded zerotree of the wavelet coefficient (EZW) algorithm has become an effective way of compressing images. The use of multistage vector quantizer (MSVQ) provides successive-approximation coding for vectors. A new algorithm is proposed that, provides good quality of reconstructed images at very low bit rates. The algorithm uses successive-approximation quantization of both scalars and vectors on the wavelet coefficients of the image. Successive-approximation quantization of scalars and vectors is done using EZW and MSVQ algorithms respectively. The EZW algorithm is applied to wavelet coefficients belonging to coarser level subbands and MSVQ is applied to vectors of wavelet coefficients belonging to finer level subbands. The proposed method further uses static Huffman coding to achieve more compression.

Interpreting images is a difficult task to automate. Image interpretation essentially consists of both low level and high level vision tasks. In this paper, we develop a joint scheme for segmentation and image interpretation in a multiresolution framework, where segmentation (low level) and interpretation (high level) interleave. The idea being that the interpretation block should be able to guide the segmentation block which in turn helps the interpretation block in better interpretation. We assume that the conditional probability of the interpretation labels, given the knowledge vector and the measurement vector is a Markov random field (MRF) and formulate the problem as a MAP estimation problem at each resolution. We find the optimal interpretation labels by using the simulated annealing algorithm. The proposed algorithm is validated on some real scene images.

The problem of image interpretation is one of inference with the help of domain knowledge. In this paper, we formulate the problem as the maximum a posteriori (MAP) estimate of a properly defined probability distribution function (PDF). We show that a Bayesian network can be used to represent this PDF as well as the domain knowledge needed for interpretation. The Bayesian network may be relaxed to obtain the set of optimum interpretations.

Any imaging system has to deal with the problem of degradation of images due to blurring and noise. The authors consider the problem of blur identification, in the presence of noise. They use a stochastic model for the blur matrix. Then, they use the mean field approximation technique which enables to obtain a closed-form expression for the mean values of the blur elements. This technique is proven to be versatile enough to handle wide varieties of blur matrices. Simulation results for estimation of the blur matrix using this formulation have also been presented.

A model for the speckle process is proposed based on the underlying phenomena, which can take into account correlation present in radar images. The restoration problem is then formulated using the Bayesian framework. The new model is found to give significantly better results for correlated speckled images as compared to models which disregard correlation.

We address the problem of color image restoration. We model the image as a Markov random field (MRF) and propose a restoration algorithm in a multiresolution framework. The incorporation of multiresolution technique significantly reduces the computational complexity of the restoration algorithm. The energy function at each resolution being non-convex, is minimized using the simulated annealing algorithm. The parameters which describe the MRF model at each resolution are computed a priori using the homotopy continuation method. Simulation results are presented to validate the proposed scheme.

This paper presents a joint strategy for parameter estimation of Markov random field (MRF) model and image restoration. The proposed scheme is an unsupervised one in the sense that no a priori knowledge of the actual image is assumed. The technique of homotopy continuation method is employed to estimate the model parameters. The model considered involves line fields and is tested on real images. Simulation results are presented for gray level images.>

The problem of stereo vision is an ill-posed problem in the sense of Hadamard. Motivated by the work of Gamble et al. (1989), Clark and Yullie (1990), Toborg and Hwang (1990), and Barnard (1989), we formulate and setup the problem of stereo vision in an integrated multiresolution framework. The energy functions constructed are interactive in the sense that there is feedforward as well as feedback of information between the different modules. Simulations show that there is considerable improvement in the obtained depth map and reduction in computational time when the modules are integrated and a multiresolution approach is used.

In this paper, we present an alternate approach to estimate the parameters of a Markov random field (MRF) model for images using the concepts of homotopy continuation method. We also develop a joint parameter estimation and image restoration scheme where we have used a fairly general model involving the line fields and tested on a real image. Simulation results using gray level images are presented.>

This paper deals with the problem of depth recovery and image restoration from sparse and noisy image data. The image is modeled as a Markov random field and a new energy function is developed to effectively detect discontinuities in highly sparse and noisy images. The model provides an alternative to the use of a line process. Interpolation over missing data sites is first done using local characteristics to obtain initial estimates and then simulated annealing is used to compute the maximum a posteriori (MAP) estimate. A threshold on energy reduction per iteration is used to speed up simulated annealing by avoiding computation that contributes little to the energy minimization. Moreover, a minor modification of the posterior energy function gives improved results for random as well as structured sparsing problems. Results of simulations carried out on real range and intensity images along with details of the simulations are presented.>

A novel approach to the restoration of an image is considered when both the point spread function and the blurred image suffer from errors. The usual approaches such as l/sub 1/ and l/sub 2/ are found to be inappropriate. A robust method, the orthogonal l/sub 1/ approach, is suggested for the restoration of images corrupted by errors in the point spread function and measurement. Since the orthogonal l/sub 1/ error function has several local minima, the use of the Solis and Wets random optimization algorithm is proposed. Simulation results are presented to illustrate the merit of the approach.>

An algorithm for designing orthogonal digital filters using a purely state-space approach is developed. The algorithm consists of three parts: (i) orthogonal embedding, (ii) transformation of the embedded orthogonal transition matrix to the extended upper Hessenberg form, and (iii) factorization of this new form into into (2n+1) Givens rotations. Appropriately interconnecting the rotors leads to the pipelined orthogonal filter structure. As a consequence of this approach, an essentially orthogonal structure is obtained for the inverse filter, and only one Givens rotor gets replaced by a hyperbolic rotor.>