We present a novel deep learning method for fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. The deep convolutional neural network consists of three parallel networks (TriPodNet) independently trained with different initialization parameters. They also have a built-in set of novel directional filters that highlight the Cervical Vertebrae edges in X-ray images. Outputs of the three parallel networks are combined using a fully connected layer. 1018 cephalometric radiographs were labeled, divided by gender, and classified according to the CVM stages. Resulting images, using different training techniques and patches, were used to train TripodNet together with a set of tunable directional edge enhancers. Data augmentation is implemented to avoid overfitting. TripodNet achieves the state-of-the-art accuracy of 81.18% in female patients and 75.32% in male patients. The proposed TripodNet achieves a higher accuracy in our dataset than the Swin Transformers and the previous network models that we investigated for CVM stage estimation.

Patient pain can be detected highly reliably from facial expressions using a set of facial muscle-based action units (AUs) defined by the Facial Action Coding System (FACS). A key characteristic of facial expression of pain is the simultaneous occurrence of pain-related AU combinations, whose automated detection would be highly beneficial for efficient and practical pain monitoring. Existing general Automated Facial Expression Recognition (AFER) systems prove inadequate when applied specifically for detecting pain as they either focus on detecting individual pain-related AUs but not on combinations or they seek to bypass AU detection by training a binary pain classifier directly on pain intensity data but are limited by lack of enough labeled data for satisfactory training. In this paper, we propose a new approach that mimics the strategy of human coders of decoupling pain detection into two consecutive tasks: one performed at the individual video-frame level and the other at video-sequence level. Using state-of-the-art AFER tools to detect single AUs at the frame level, we propose two novel data structures to encode AU combinations from single AU scores. Two weakly supervised learning frameworks namely multiple instance learning (MIL) and multiple clustered instance learning (MCIL) are employed corresponding to each data structure to learn pain from video sequences. Experimental results show an 87% pain recognition accuracy with 0.94 AUC (Area Under Curve) on the UNBC-McMaster Shoulder Pain Expression dataset. Tests on long videos in a lung cancer patient video dataset demonstrates the potential value of the proposed system for pain monitoring in clinical settings.

Finding the accurate location of a mobile device based on images it acquires usually requires applying structure from motion (SFM) for 3D camera position reconstruction. Since the convergence of SFM depends on effectively selecting among the multiple retrieved images, we propose an optimization framework to do make the selection using the criterion of the highest intra-class similarity among images returned from retrieval pipeline. The selection process should consider only images with distinct GPS-tags. The selected images along with the query can be used to reconstruct a 3D scene and obtain relative camera positions. Experimental results demonstrate our method achieves a higher convergence rate in the SFM processing.

Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database collected from social sharing websites like Flickr or services such as Google Street View. This paper proposes a new method for reliable estimation of the actual query camera location by optimally utilizing structure from motion (SFM) for three-dimensional (3-D) camera position reconstruction, and introducing a new approach for applying a linear transformation between two different 3-D Cartesian coordinate systems. Since the success of SFM hinges on effectively selecting among the multiple retrieved images, we propose an optimization framework to do this using the criterion of the highest intraclass similarity among images returned from retrieval pipeline to increase SFM convergence rate. The selected images along with the query are then used to reconstruct a 3-D scene and find the relative camera positions by employing SFM. In the last processing step, an effective camera coordinate transformation algorithm is introduced to estimate the query's geo-tag. The influence of the number of images involved in SFM on the ultimate position error is investigated by examining the use of three and four dataset images with different solution for calculating the query world coordinates. We have evaluated our proposed method on query images with known accurate ground truth. Experimental results are presented to demonstrate that our method outperforms other reported methods in terms of average error.

Image search engines commonly employ the Bag Of Features (BOF) method to represent each database image with a feature vector and retrieve the best candidate using a measure of similarity to a query image vector. The BOF vector, which specifies the occurrence frequency of features, is used with Soft Assignment (SA) to find the most similar candidates which are further analyzed using geometric information to determine the final location. In this paper, we propose a new method where partial geometric information captured in the scales of keypoints associated to feature descriptors is directly used in the feature vector entries, unlike the conventional BOF method which uses the frequency of features. The proposed method, referred to as Bag Of Scale-Indexed Features (BOSIF), is implemented with an algorithm devised to avoid the increased use of memory. A procedure for evaluating scale consistency between query and dataset images is also proposed. Experimental results demonstrate that BOSIF outperforms SA-based feature-indexed BOF method and has performance comparable to state-of-the-art approaches in terms of Recall and especially for the first retrieved images by removing false matches caused by quantization error.

Consider a scenario in which a source broadcasts a common content to a group of cooperating mobile devices that are within proximity of each other. Devices in this group may receive only partial content from the source due to packet losses over wireless broadcast links and these packet losses may differ for different devices. The remaining content missing at each device can then be recovered, thanks to cooperation among the devices by exploiting device-to-device (D2D) connections. In this context, the minimum amount of time that guarantees a complete acquisition of the common content at every device is referred to as the “completion time”. It has been shown that instantly decodable network coding (IDNC) reduces the completion time as compared with no network coding in this scenario. However, for applications such as video streaming, not all packets have the same importance and not all devices are interested in the same quality of content. This problem becomes more interesting and challenging when additional, but realistic constraints, such as strict deadline, bandwidth, or limited energy are added in the problem formulation. We assert that direct application of IDNC in such a scenario yields poor performance in terms of content quality and completion time. In this paper, we propose a novel Content- and Loss-Aware IDNC scheme that improves content quality and network coding opportunities jointly by taking into account the contribution of each packet to the desired quality of service (QoS) as well as the channel losses over D2D links. Our proposed Content- and Loss-Aware IDNC (i) maximizes the quality under the completion time constraint, and (ii) minimizes the completion time under the quality constraint. We demonstrate the benefits of Content- and Loss-Aware IDNC through simulations.

Reduced execution time and increased power efficiency are important objectives in the distributed execution of collaborative signal processing tasks over wireless sensor networks (WSNs). Meanwhile, Fourier transforms are among the most widely used frequency analysis tools in WSNs for studying the behavior of sensed phenomena. Several energy-efficient in-network Fourier transform computation algorithms have been proposed for WSNs. Most of these works assume that the sensors are equally spaced over a one-dimensional (1D) region. However, in practice, the sensors are usually randomly distributed over a two-dimensional (2D) plane. Consequently, the conventional 2D Fast Fourier Transform (FFT) designed for data sampled on a uniform grid is not applicable in such environments. We address this problem by designing a distributed hybrid structure consisting of local Non-equispaced Discrete Fourier Transform (NDFT) and global FFT computations. First, the NDFT method is applied within suitably selected clusters to obtain the initial uniform Fourier coefficients within allowable estimation error bounds. We investigate both classical linear and generalized interpolation methods for computing the NDFT coefficients within each cluster. Second, a separable 2D FFT is applied over all clusters using our proposed energy-efficient 1D FFT computation method, which reduces communication costs by employing a novel binary representation mapping strategy for data exchanges between sensors. The proposed techniques are implemented on the SIDnet-SWANS platform, and the tradeoffs between communication cost, execution time, and energy consumption are studied.

With the rapid growth of electronic data repositories in diverse application domains, including healthcare, considerable research interest has been developed to solve issues related to extraction of hidden knowledge in these repositories. Electronic health record systems (EHRs) are the fastest growing in terms of size and data diversity. In this work, we focus on mining a high dimensional sparse dataset using nursing care data as an exemplar. To mine a high-dimensional and sparse dataset is a challenging task due to a number of reasons. There are several dimension reduction methods, however, they do not work well with contextual datasets. In our study, we have used association mining as a dimension reduction step and for extracting important features from the dataset. Our results show that association mining can be effectively used for dimension reduction and feature extraction step. Our predictive modeling results show that decision tree models generally have high accuracy and the results are easy to interpret and determine the influence of different variables.

Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database. This paper proposes an efficient method for limiting the search space for image retrieval engine by extracting and leveraging additional media information about Estimated Positional Error (EPE) to address complexity and accuracy issues in the search, especially to be used for compensating GPS location inaccuracy in dense urban areas. To test our procedure we have created a database by acquiring Google Street View (GSV) images and set of query images along with their EPE tag for down town of Chicago. Experimental results demonstrate how our proposed method can improve performance just by utilizing a data that is available for mobile systems such as smart phones.

Finding accurate positions of mobile devices based on visual information involves searching for query-matching images in a very large dataset, typically containing millions of images. Although the main problem is designing a reliable image retrieval engine, accurate localization also depends on a good fusion algorithm between the GPS data (geo-tags) of each query-matching image and the query image. This paper proposes a new method for reliable estimation of the actual query camera position (geo-tag) by applying structure from motion (SFM) with bundle adjustment for sparse 3D camera position reconstruction, and a linear rigid transformation between two different 3D Cartesian coordinate systems. The experimental results on more than 170 query images show the proposed algorithm returns accurate results for a high percentage of the samples. The error range of the estimated query geo-tag is compared with other related research and indicates an average error less than 5 meters that improves on some of the published works.

This paper considers the end-to-end mean squared error distortion in reconstructing a memoryless proper-complex Gaussian source transmitted over a set of parallel block-fading Gaussian noise channels, where the fading gains are modeled as correlated Rayleigh distributed random variables with different average powers, thus resulting in asymmetric average received signal noise ratios (SNRs). The distortion exponent (i.e., how fast the average distortion decays to zero as the average received SNR increases) of several coding strategies based on separate source and channel coding is characterized. The definition of distortion exponent commonly used in the literature for SNR-symmetric channels is generalized to the case of SNR-asymmetric channels. It is shown that fading correlation degrades the achievable mean squared error distortion, but does not affect the distortion exponent in the analyzed achievable schemes. The logarithm of the determinant of the fading correlation matrix is found to be a proxy for measuring the performance degradation due to correlation as compared with the case of independent fading. The proposed framework allows one to study any number of correlated parallel channels, contrary to the most of the literature that restricts attention to two channels only, with the same received SNR and with independent fading. In particular, a scheme based on the multiple description coding with more than two descriptions is analyzed; it is shown that determining the distortion exponent in this setting reduces to solving a linear program, which can be done numerically very efficiently. The proposed methodology relies on combining the ideas from linear innovation sequences and properties of determinant of sub-matrices. Interestingly, it is found that SNR-asymmetry is beneficial for multiple description coding when the total average received SNR in decibel is held constant. Even in the SNR-symmetric case, asymmetry in the compression rates is shown to lead to a larger distortion exponent than symmetric rates.

The problem of power-efficient data aggregation in wireless sensor networks (WSNs) using Compressive Sensing (CS) to reduce the amount of data communicated is addressed here. Existing CS-based data aggregation methods can be categorized as either those that apply CS spatially to minimize the amount of data to be communicated in the routing path, or those that seek to minimize the amount of data by applying CS temporally at each sensor. A recently reported scheme that is described as a Spatial-Temporal CS scheme randomly selects a subset of data but does not apply compression in the routing path. Here we formulate a spatial-temporal data collection model in WSNs and refer to it as Spatial-Temporal Hierarchical Data Aggregation using Compressive Sensing (ST-HDACS). The idea underlying ST-HDACS consists of two key components: Firstly, for each time snapshot of data collected in the network, a subset of nodes is randomly selected and designated for data sensing and transmission. A power-efficient Adaptive Hierarchical Data Aggregation (A-HDACS) scheme is incorporated in our work to compress the spatial data to be communicated in the routing path. Secondly, after performing data collection over a designated time period, a Matrix Completion (MC) problem is executed in the fusion center to recover the data for the entire network over the full data collection period. The performance of the proposed method is evaluated and it is demonstrated that ST-HDACS scheme reduces the amount of data for transmission and improves the associated energy consumption more effectively than existing CS-based data aggregation schemes.

We propose MedianPipes, a novel, FPGA based, highly pipelined and scalable architecture for median filtering. Median filters and its variants are widely used for noise suppression in image processing. All variants of median filter depend on the computation of median values. MedianPipe is a highly pipelined architecture and hence an ideal fit for FPGAs. It does not make any assumptions about the image to fit on the on-chip memory. Instead, the image is assumed to be streamed-in in the form of image slices. Multiple MedianPipe modules are used depending on the size of image slice and hence the overall hardware complexity of proposed technique scales linearly with image-slice size. The architecture for MedianPipe is based on the principle of merge sort and uses a median window of size 3 x 3. It consists of two stepped sorting process: The first step is to sort the pixels within each row of median window to get sorted rows. This sorting is done using a single comparator over multiple clock cycles. The sorted rows are saved in block memory based First-In-First-Out (FIFO) memory and reused to calculate the medians corresponding to three median windows. The second step is to merge these sorted rows to find the median using a merger block. The merger block consists of three comparators and read out a single value every cycle once the pipeline is filled. Without loss of generality, the pixels of an image slice are assumed to be read in a column major format. All the median values within the column of the image slice can be computed in parallel using multiple MedianPipes. The computation of median values in the following column is delayed by a clock cycle. Hardware resources scale linearly by varying the pixel sizes and number of MedianPipes. The pixel rate achieved for various pixel sizes is well above 124 MHz which is the standard for 1080p High-Definition.

This paper proposes an efficient architecture (InvArch) for computing matrix inversion using Gauss-Jordan Elimination method. The proposed architecture exploits parallelism through pipelined floating-point computational units and reduces the number of floating-point multiplication units required compared with the existing pipelined implementations. The reduction in multiplication units results in over 80% reduction in hardware for floating point computation units. The architecture performs in-place inversion and provides scalability across the rows and columns. Hardware efficiency is achieved by reaping benefit from regularity in computation and better utilization of pipelined computational resources. Multiple rows are normalized within an iteration of Gauss-Jordan algorithm that allows reduction in number of floating-point multiplication units in the elimination step. In addition to implementing the architecture, an analytical performance model is also developed for InvArch and some related works. InvArch achieves performance comparable to reference architectures in terms of clock cycles and throughput while using significantly less hardware resources.

Energy efficiency is one of the key objectives in data gathering in wireless sensor networks (WSNs). Recent research on energy-efficient data gathering in WSNs has explored the use of Compressive Sensing (CS) to parsimoniously represent the data. However, the performance of CS-based data gathering methods has been limited since the approaches failed to take advantage of judicious network configurations and effective CS-based data aggregation procedures. In this article, a novel Hierarchical Data Aggregation method using Compressive Sensing (HDACS) is presented, which combines a hierarchical network configuration with CS. Our key idea is to set multiple compression thresholds adaptively based on cluster sizes at different levels of the data aggregation tree to optimize the amount of data transmitted. The advantages of the proposed model in terms of the total amount of data transmitted and data compression ratio are analytically verified. Moreover, we formulate a new energy model by factoring in both processor and radio energy consumption into the cost, especially the computation cost incurred in relatively complex algorithms. We also show that communication cost remains dominant in data aggregation in the practical applications of large-scale networks. We use both the real-world data and synthetic datasets to test CS-based data aggregation schemes on the SIDnet-SWANS simulation platform. The simulation results demonstrate that the proposed HDACS model guarantees accurate signal recovery performance. It also provides substantial energy savings compared with existing methods.

Summary form only given. The Discrete Fourier Transform (DFT) can be viewed as the Fourier Transform of a periodic and regularly sampled signal as commonly defined in equation 1. The Non-Uniform Discrete Fourier Transform (NuDFT) is a generalization of the DFT for data that may not be regularly sampled in spatial or temporal dimensions. This flexibility allows for benefits in situation where sensor placement cannot be guaranteed to be regular or where prior knowledge of the informational content could allow for better sampling patterns than a regular one. NuDFT is used in applications such as Synthetic Aperture Radar (SAR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). The NuDFT definition is shown in equation 2. Here the sample locations are points si in the set S. Each point, si has a complex value consisting of location or frequency components six and siy. The location or frequency components are, of course, not restriced to a discrete sampling grid.

This paper proposes a novel FPGA-based accelerator for the memory and compute-intense re-gridding process used in computation of Non-uniform Fast Fourier Transform (NuFFT). The re-gridding process interpolates arbitrary sampled data onto a uniform grid using an interpolation kernel function. This regridding step is considered one of the most time consuming step in entire NuFFT computation. We propose a memory-efficient technique based on the novel use of customizable hardware components such as FPGA block memory in First-In-First-Out (FIFO) configuration, fill-rate based arbiter, distributed RAM and an array of pipelined single precision floating point multipliers and adders. The proposed architecture exhibits high performance over a wide range of configurations and data-sizes. A speed-up of over 9.6 was achieved when compared with existing FPGA-based technique at a 7 times higher MFLOPS per watt. Compared to GPU based technique, over 6 times higher MFLOPS per watts were achieved.

Compressive Sensing (CS) has been applied successfully in a wide variety of applications in recent years, including photography, holography, optical system research, facial recognition, and Medical Resonance Imaging (MRI). In wireless sensor networks (WSNs), significant research work has been pursued to investigate the use of CS to reduce the amount of data communicated, particularly in data aggregation applications and thereby improving energy efficiency. However, most of the previous work in WSN has used CS under the assumption that data field is smooth with negligible white Gaussian noise. In these schemes signal sparsity is estimated globally based on the entire data field, which is then used to determine the CS parameters. In more realistic scenarios, where data field may have regional fluctuations or it is piecewise smooth, existing CS based data aggregation schemes yield poor compression efficiency. In order to take full advantage of CS in WSNs, we propose an adaptive aggregation scheme referred to as Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS). The proposed schemes dynamically determines sparsity values based on signal variations in local regions. We prove that A-HDACS enables more sensor nodes to employ CS compared to the schemes that do not adapt to the changing field. Also, the simulation results demonstrate improvement in energy efficiency and accuracy in signal recovery.

A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix.

Compressive sensing (CS) is a burgeoning technique being applied to diverse areas including wireless sensor networks (WSNs). In WSNs, it has been applied in the context of data gathering and aggregation, particularly aimed at reducing data transmission cost and improving power efficiency. Existing CS-based data gathering work in WSNs utilize the property that under certain conditions, only O(K log N) CS random measurements can represent a K-sparse signal of length N. In previous work fixed and identical compression thresholds were assumed for the entire network resulting in less efficient solutions. In this paper, we present a novel data aggregation architecture model that integrates a multi-resolution hierarchical structure with CS to further optimize the amount of data transmitted. Our key idea is to set up multiple compression thresholds adaptively based on the cluster sizes at different levels. The advantages of the proposed aggregation model in contrast to other state-of-the-art related work are measured in terms of total amount of data for transmission, data compression ratio and energy consumption. We implement the proposed data aggregation scheme on a SIDnet-SWANS platform, a discrete event simulator commonly used for WSN simulations. Our simulation results demonstrate that the proposed CS-based hierarchical data aggregation model guarantees accurate signal recovery performance; meanwhile, it also obtains substantial energy savings compared to other existing methods.

Nonuniform Discrete Fourier Transform (NDFT) has been well-investigated for performing fast computation of the frequency content of nonequispaced data samples. This nonuniform formulation is also well-suited for Fourier analysis of data samples collected in randomly deployed wireless sensors. However, most of the existing NDFT formulations employ global communication patterns and thus are inefficient in terms of energy consumption and execution time for in-network realization of NDFT. In this paper, we investigate NDFT implementation that leverages compressive sensing (CS) to reduce the amount of data and global communication, thus allowing the use of a few random measurements to adequately represent sparse signal. Our main idea is to organize 2D random deployment of sensors into a hierarchy of clusters. A local interpolation step is performed in the clusters at the lowest level to convert a nonuniform grid into a uniform grid. The global 2D Fast Fourier Transform (FFT) is then implemented using a multiresolution data aggregation architecture and exploiting CS to reduce data transmission. Using theoretical analysis as well as SIDnet-SWANS based simulations, we demonstrate significant advantages of the proposed method over existing state of the art, in terms of execution time, transmission energy efficiency, signal-to-noise ratio and communication overhead.

Reduced execution time and increased power efficiency are important objectives in the distributed execution of collaborative signal processing tasks over wireless sensor networks. The power-efficient implementation of the Fourier transform computation is an exemplar of distributed data communication and processing task widely used in the signal processing field. Past work has presented some energy-efficient in-network Fourier transform computation algorithms devised only for uniformly sampled one-dimensional (1D) sensor data. However the circumstance that sensors are randomly distributed over a 2D plane may be more practical, therefore the conventional two-dimensional Fast Fourier Transform (2D FFT) defined for data sampled on uniform grids is not directly applicable in such environments. We address this problem by designing a distributed hybrid structure consisting of local Nonequispaced Discrete Fourier Transform (NDFT) and global FFT computation. Firstly, NDFT method is applied in a suitable choice of clusters to get the initial uniform Fourier coefficients with allowable estimation error bounds. We experiment with classical linear as well as generalized interpolation methods to compute NDFT coefficients within each cluster. A separable 2D FFT is then performed over all these clusters by employing our proposed energy-efficient 1D FFT computation that reduces communication costs using a novel bit index mapping strategy for data exchanges between sensors. The proposed techniques are implemented in a SID net-SWANS platform to investigate the communication costs, execution time, and energy consumption. Our results show reduced execution time and improved energy consumption when compared with existing work.

We address the problem of efficient in-network processing of hybrid spatial queries in Wireless Sensor Networks (WSN), where the data may correspond to different physical phenomena in different regions. We propose space and communication efficient schemes capable of correlating spatial dimension with different physical values. To trade-off (im)precision vs. energy consumption, the proposed schemes combine rank order statistics, regular sampling, and bitmap representation. We present a proof of concept implementation of the proposed methodology and quantify the benefits of our approach through simulations.

Photoreceptor cells in the human eye play a vital role in vision. Certain retinal diseases cause the photoreceptor cells to degenerate and may lead to vision loss. Quantification of photoreceptor cell density from adaptive optics (AO) retinal images can provide valuable information and aid in the screening, diagnosis, and follow-up of retinal diseases. In this paper we describe an image model using a windowed two-dimensional (2D) lattice of pulses representing the cells and characterize the frequency content as decaying frequency domain pulses on the reciprocal lattice. Based on this model we propose a novel method for detection of cone photoreceptor cells by analyzing the discrete-space Fourier transform (DSFT) of AO retinal images. This method uses a small-extent block-based 2D discrete Fourier transform (DFT) to determine cell frequency content in order to obtain parameters of an adaptive circularly symmetric band-pass filter that is applied to the image. The filter extracts the underlying cellular structure and removes high-frequency noise as well as very low frequency contamination manifested as slow variations in the image. Subsequent detection yields an automated cell count that compares well with actual and manual counts on test and retinal images and demonstrates the accuracy of the method.

Improvements in transmitted video quality are achievable by utilizing multiple-input multiple-output (MIMO) systems for transmission of multiple description coded (MDC) video. For a particular MDC scheme, the improvements heavily depend on the selection of the underlying MIMO system. This paper proposes using a MIMO system with power control based antenna selection. Simulations show that the proposed system significantly outperforms the existing MDC/MIMO combinations in the literature and extends the range of channel signal-to-noise ratio (SNR) values for which video of given quality can be transmitted. Quality improvements result from MIMO power control which allows transmissions over available MIMO antennas only for sufficiently high channel gains (effectively preventing transmissions that result in video packet loss) and guarantees equal channel performance in terms of the bit error rate (BER) for all the balanced descriptions of equal importance.

We propose a novel standard-compliant multiple description coding (MDC) method that exploits the H.264/AVC redundant slice tool, performing rate-distortion optimization at the slice level. The strategy to allocate redundancy to each slice jointly takes into account its contribution to distortion, its position in the GOP, the effect of decoder error concealment, and the transmission conditions. This makes the algorithm more accurate with respect to previous frame-based solutions, and experimental results show that it compares favorably with other state-of-the-art standard-compliant MDC techniques.

This paper considers the end-to-end mean-square distortion in reconstructing a memoryless proper-complex Gaussian source transmitted over parallel block-fading Rayleigh AWGN channels. We characterize the distortion exponent of several source and channel coding strategies; that is, we characterize how fast the distortion decays to zero as the SNR increases. Unlike previous works, we consider networks with different received SNR's and with correlated fading. We generalize the definition of distortion exponent to SNR-asymmetric channels. We show that fading correlation degrades the achievable mean-square distortion but does not affect the distortion exponent. The performance degradation is measured in terms of power-offset; that is, the power increment needed to achieve the same performance as the uncorrelated case. We show that the power-offset is proportional to the determinant of the fading correlation matrix. Our proposed methodology allows us to study any number of parallel channels and we are no longer restricted to two channels (as was commonly done in the previous literature). Finally, we show that determining the distortion exponent of multiple description coding (MDC) schemes in high SNR reduces to solving a linear programming problem.

A cross-layer method is proposed for optimizing, controlling and improving the quality of video transmission over wireless networks using scalable video coding (SVC) and multiple-input multiple-output (MIMO) transmission with channel state feedback (CSI). Multiple video sub-streams are created by a content-based partitioning and sorting of the enhancement layers produced with the SVC extension of H.264/AVC. Unlike in existing methods, the prioritized bit-streams are transmitted by actively performing power adjustment and antenna selection using a bit-stream prioritization matrix to modify the power allocation procedure of a recently proposed MIMO scheme. The power allocation strategy results in different bit error rate (BER) experienced by the bit-streams. Simulation results show an improved performance compared with power allocation that equalizes BER over the different channels.

The large peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) transmission translates to system performance degradation due to low power efficiency in the presence of nonlinear power amplification. To reduce PAPR, many OFDM systems have adopted the peak windowing method. However, existing peak windowing schemes suffer from performance limitations especially in the presence of multiple closely-spaced peaks. In this paper, two new peak windowing schemes are proposed to improve performance by taking care of closely-spaced peaks. Simulation results show that the two proposed schemes outperform existing schemes in attaining significantly lower out-of-band radiation given the same in-band distortion constraints.

This paper presents an image authentication scheme based on compensated watermarking employing a Lagrangian-based closed-form solution to compensate for signature perturbation due to the embedding operation. The proposed scheme uses a spread-spectrum based watermarking technique and a blind detector, thus making it attractive for applications that may not have the original image available at the time of authentication. Existing compensated signature embedding frameworks use an iterative mechanism to reach a desired compensation. The iterative approach is time consuming and less effective than the closed-form approach proposed in this paper, which performs an accurate compensation in one step while meeting the minimum distortion criteria of image least mean square distortion to guarantee image fidelity. Simulation results are presented to show the proposed scheme's efficiency and accuracy.

The problem of optimal rate allocation to streams constituting multiple description coding (MDC) has largely been addressed under the assumption that the multiple paths available for transmission are uncorrelated. In this paper, we model the transmission paths as correlated erasure channels and then examine the problem of allocating a given coding rate to the multiple descriptions in order to minimize the average distortion of the reconstructed message. We also investigate the relationship between the optimal average distortion and correlation of channels and prove that the bound on the average distortion will decrease as the correlation increases. Furthermore, we derive a closed-form solution for the contour which determines the region where multiple description coding (MDC) or single description coding (SDC) yields the minimal bound on the average distortion. Relying on this contour, we present a heuristic of the optimal rate allocation problem to determine the number of descriptions and relative rates.

In this paper, we study the optimal rate allocation of multiple description coding over multiple erasure channels in order to attain the minimum average distortion of the recovered message at the receiver. The results of this investigation are subsequently used to determine conditions under which the optimal rate allocation is characterized by transmission of one description over a single channel, i.e. single description coding. We formulate the optimal rate allocation problem by using rate- distortion theory under the constraint that the total coding rate is fixed and solve the problem by using numerical methods. Furthermore, we derive a closed-form analytical solution to the optimal rate allocation problem under four conditions and verify that the analytical solution is consistent with the numerical results. Also, we present a heuristic that captures the solution to the optimal rate allocation problem and can be used to determine the number of descriptions and relative rates required to achieve optimality.

A novel audio watermarking scheme based on frequency-selective spread spectrum (FSSS) technique is presented. Unlike most of the existing spread spectrum (SS) watermarking schemes that use the entire audible frequency range for watermark embedding, the proposed scheme randomly selects subband(s) signal(s) of the host audio signal for watermark embedding. The proposed FSSS scheme provides a natural mechanism to exploit the band-dependent frequency-masking characteristics of the human auditory system to ensure the fidelity of the host audio signal and the robustness of the embedded information. Key attributes of the proposed scheme include reduced host interference in watermark detection, better fidelity, secure embedding and improved multiple watermark embedding capability. To detect the embedded watermark, two blind watermark detection methods are examined, one based on normalised correlation and the other based on estimation correlation. Extensive simulation results are presented to analyse the performance of the proposed scheme for various signal manipulations and standard benchmark attacks. A comparison with the existing full-band SS-based schemes is also provided to show the improved performance of the proposed scheme.

Digital content authentication and preservation is an extremely challenging task in realizing decentralized digital libraries. The concept of compensated signature embedding is proposed to develop an effective multimedia content authentication system. The proposed system does not require any third party reference or side information. Towards this end, a content-based fragile signature is derived and embedded into the media using a robust watermarking technique. Since the embedding process introduces distortion in the media, it may lead to authentication failure. We propose to adjust the media samples iteratively or using a closed form process to compensate for the embedding distortion. Using an example image authentication system, we show that the proposed scheme is highly effective in detecting even minor modifications to the media.

A novel technique is proposed for data hiding in digital audio that exploits the low sensitivity of the human auditory system to phase distortion. Inaudible but controlled phase changes are introduced in the host audio using a set of allpass filters (APFs) with distinct parameters of allpass filters, i.e., pole-zero locations. The APF parameters are chosen to encode the embedding information. During the detection phase, the power spectrum of the audio data is estimated in the z-plane away from the unit circle. The power spectrum is used to estimate APF pole locations, for information decoding. Experimental results show that the proposed data hiding scheme can effectively withstand standard data manipulation attacks. Moreover, the proposed scheme is shown to embed 5-8 times more data than the existing audio data hiding schemes while providing comparable perceptual performance and robustness

The phenomenon of reduced spatial resolution perceived away from the point of gaze (foveation point) in a scene by the human visual system can be gainfully exploited in image and video compression. Interest has recently evolved from single foveation points to dynamic and multiple points of foveation, implementing which entails significantly increased complexity in the encoder. In this paper, a novel idea of efficiently combining adaptive multipoint foveation with salvaged high-resolution information for reuse in real-time video to maintain higher resolution in peripheral regions is proposed. The idea is implemented with a fast algorithm for multi-foveation processing, in conjunction with standard-compliant decoding. The new multi-foveation algorithm is integrated with the H.264/AVC standard for testing. Simulation results show a compression gain ranging from 2.25% to more than 11%, without degrading the perceived quality and PSNR and with minimal addition to the complexity of a standard uniform-resolution codec.

This paper presents an efficient blind watermark detection scheme for additive embedding (AE) based on underdetermined independent component analysis (ICA) framework. The proposed detector assumes that the host signal and the watermark obey non-Gaussian distributions and watermark embedding follows AE model. The proposed blind watermark detector employs blind source separation (BSS) for underdetermined mixtures for watermark estimation. Simulation results are presented showing that the proposed detector performs significantly better than existing correlation based blind detectors operating without suppressing the host signal interference at the detector.

While antenna selection has been shown to be effective in improving bit error rate (BER) performance in multiple-input-multiple-output (MIMO) systems, further performance gain can be obtained by transmitting only when the channel gains are sufficiently high and by using power control to compensate for channel variations. Truncated channel inversion is an adaptive power control scheme that compensates for fading above a certain cutoff fade depth: below the cutoff level the transmission is stopped. In this paper, we propose a novel truncated channel inversion power control based antenna selection scheme to improve the BER performance of spatial multiplexing (SM) systems with linear receivers. Power control based optimal and sub-optimal antenna selection criteria are proposed to dynamically select the number of active transmit antennas and the mapping of data substreams to transmit antennas. Simulations show that, in flat Rayleigh fading channels, the proposed power control based antenna selection scheme significantly outperforms the existing schemes in the literature.

Spatial multiplexing (SM) proves effective in increasing data rate in a narrowband multiple-input multiple-output (MIMO) system. Past work has shown that unitary precoding with limited feedback can be used at the transmitter to improve the bit error rate (BER) performance. Further performance gain can be achieved if power control is used along with unitary preceding at the transmitter. In this paper we propose a novel power control scheme called modified spatial-temporal truncated channel inversion (MST-TCI) for limited feedback MIMO systems in block Rayleigh fading channels. A low-complexity algorithm called codebook optimum index loading (COIL) is proposed to generate near-optimum codebooks for quantizing MST-TCI power control information. Simulation results show that the proposed scheme significantly outperforms the limited feedback unitary precoding scheme

A new method for reducing the peak-to average power ratio (PAPR) in orthogonal frequency-division multiplexing is proposed. The new method, called erasure pattern selection (EPS), is based on the use of frame expansion for adding redundancy to the data. Part of the redundancy is removed by examining several patterns for erasing subcarriers, and selecting the pattern that produces the lowest PAPR for transmission. The residual redundancy in the data is exploited at the receiver to aid both the reconstruction of the erased data and correction of errors. The implementation is aided with the incorporation of a projection-onto-convex-sets (POCS) method in EPS, in order to select and assign the best values to the unused subcarriers and further reduce the PAPR. Complexity analysis and simulation results show that the POCS-based EPS method outperforms existing probabilistic methods, while requiring lower overall complexity.

A new method for reducing the peak-to-average power ratio (PAPR) in orthogonal frequency-division multiplexing is proposed. The new method, called erasure pattern selection (EPS), is based on the use of frame expansion for adding redundancy to the data. Part of the redundancy is removed by examining several patterns for erasing subcarriers and selecting the pattern that produces the lowest PAPR for transmission. The residual redundancy in the data is exploited at the receiver to aid both the reconstruction of the erased data and correction of errors. The implementation is aided with the incorporation of a projection-onto-convex-sets (POCS) method in EPS in order to select and assign the best values to the unused subcarriers and further reduce the PAPR. Complexity analysis and simulation results show that the POCS-based EPS method outperforms existing probabilistic methods, while requiring lower overall complexity

Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficient to handle clutters (e.g., Kalman filter). On the other hand, distributions that are complex enough to incorporate all a priori knowledge can make the problem computationally intractable (e.g., particle filters (PF)). This paper proposes a new formulation of visual tracking where every piece of information, be it from a priori knowledge or observed data, is represented by a set in the solution space and the intersection of these sets, the feasibility set, represents all acceptable solutions. Based on this formulation, we propose an algorithm whose objective is to find a solution in the feasibility set. We show that this set theoretic tracking algorithm performs effective face tracking and is computationally more efficient than standard PF-based tracking.

A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.

This paper presents an efficient blind watermark detection/decoding scheme for spread spectrum (SS) based watermarking, exploiting the fact that in SS-based embedding schemes the embedded watermark and the host signal are mutually independent and obey non-Gaussian distribution. The proposed scheme employs the theory of independent component analysis (ICA) and posed the watermark detection as a blind source separation problem. The proposed ICA-based blind detection/decoding scheme has been simulated using real-world audio clips. The simulation results show that the ICA-based detector can detect and decode watermark with extremely low decoding bit error probability (less than 0.01) against common watermarking attacks and benchmark degradations.

A new particle filter-the Kernel Particle Filter (KPF)-is proposed for visual tracking in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function. Particles are allocated based on the gradient information estimated from the kernel density estimate of the posterior. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident in scenarios of small system noise or weak dynamic models where the standard particle filter usually fails.

In this paper, the problems of redundancy allocation for providing effective error-resilience and service class distribution for enhanced quality of service (QoS) in real-time MPEG-2 video transport are addressed. A real-time low-complexity content-based adaptive error-resilient approach is proposed for the transport of MPEG-2 video streams, encapsulated using real-time transport protocol (RTP) and delivered over heterogeneous networks. An algorithm is derived using spatial and temporal properties of MPEG-2 video for assigning weights to each packet based on the estimated perceptual error. These weights, which indicate the relative importance of RTP packets, together with the communication channel characteristics are used to determine the allocation of resources for providing improved error-resilience and for assigning data packets to various classes of service in order to enhance the quality of transmission. Parameters extracted from the RTP header are used to determine the weights, so that the proposed algorithm can be implemented in real-time. This algorithm is used for adaptively allocating redundant forward error correction packets as well as for marking and forwarding of RTP packets in differentiated services (DiffServ). Simulation results are presented to show the significant improvement in performance based on our proposed approach to video transport.

A novel perception-based data hiding technique for digital audio is proposed. It exploits the lower sensitivity of the human auditory system (HAS) to phase distortion in audio compared with magnitude distortion. Audio is decomposed into subband signals, some of which are selected for embedding data with a controlled alteration of phase using suitable allpass digital filters. The proposed scheme is robust to standard data manipulations yielding less than 2% error probability against compression, re-sampling, re-quantization, random chopping and noise addition. The proposed method is also robust to desynchronization attacks.

The paper presents an automatic initialization procedure for visual tracking of human motion. Instead of relying merely on low-level image features to give a single estimate of the initial human posture, the system seeks to find a set of samples that carries multiple hypotheses of the pose. By accumulating different image cues in the first 3-15 consecutive frames and combining dynamic information regarding human motion, the system builds a human body model for the person to be tracked from a video sequence and produces a sample set as an estimate of the posterior distribution of the initial posture. The sample set provides a good starting point for tracking with sequential Monte Carlo methods.

A new method is proposed for robust audio watermarking using direct-sequence spread spectrum in combination with the subband decomposition of the audio signal. The method exploits the frequency masking characteristics of the human auditory system (HAS) and inserts the watermark into a randomly selected frequency band of the input audio signal. Performance of the proposed system is evaluated for robustness to signal manipulations such as contamination with additive noise, resampling, compression, filtering, multiple watermark insertion, and random chopping. Experimental results show that the capacity of the proposed watermarking scheme is relatively high compared with existing spread spectrum based audio watermarking schemes.

A novel high capacity data hiding technique for digital audio is proposed. Imperceptibility of the embedded data is ensured based on the masking property of the human auditory system (HAS). Audio signal is decomposed into subband signals, some of which are selected for embedding data using finite-length impulse response approximations to allpass digital filters. Data detection is based on finding the filter pole-zero locations, which is achieved by power spectrum estimation of the data embedded audio signal. Performance of the proposed scheme is evaluated for different data encoding strategies. The proposed method is robust to desynchronization attacks as well as other standard data manipulation attacks.

A set of techniques are presented for Bayesian tracking of cyclic human motion based on decomposing a complex cyclic motion into component motions. Phases of the component motions are defined and two different mechanisms for coupling the phases are described: importance sampling and an observation model. The intensity of coupling is adaptively adjusted during tracking such that strong coupling is triggered during self-occlusion. Tracking of a walking human using motion decomposition and phase coupling is performed with an improved particle filter called the approximate kernel particle filter. We show that our approach handles foreign object occlusion and self-occlusion with improved accuracy and efficiency compared with conventional tracking without decomposition.

Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel particle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modes in a likelihood function, we redistribute particles by invoking kernel-based representation of densities and introducing mean shift as an iterative mode-seeking procedure, in which particles move towards dominant modes while still maintaining as fair samples from the posterior. Experiments on face and limb tracking show that the algorithm is superior to conventional particle filter in handling weak dynamic models and occlusions with 60% fewer particles in 3-9 dimensional spaces.

Cyclic motion underlies several human activities including exercising, running, and walking. Accurate tracking of such motion in video data helps in developing computer-aided applications such as gait analysis, person identification, patient rehabilitation, etc. This paper presents a set of novel techniques for tracking cyclic human motion based on decomposing complex cyclic motion into simpler motion components and introducing phase coupling between the components. The intensity of coupling is adaptively adjusted during tracking such that a strong coupling is triggered when self-occlusion occurs. In our experiments we use sequential Monte Carlo methods for tracking a walking human. We show that this adaptive phase coupling of component motions handles occlusion and self-occlusion with significantly improved accuracy while avoiding the limitations caused by a poorly trained dynamic model.

We present an algorithm for real-time adaptive forward error correction (FEC) of MPEG-2 video stream, encapsulated using real-time transport protocol (RTP) and delivered over best-effort networks. Our algorithm provides an efficient method to determine the allocation of redundancy to the MPEG-2 video stream. The redundancy is allocated such that the resulting estimated degradation density function for video (DDF) is uniformly distributed. A weight, which indicates the relative importance of RTP packets, together with the communication channel characteristics and FEC scheme are used to model the density function of the video stream and allow us to determine the allocation of FEC packets. The weight is based on the content of RTP packets in the video stream. Parameters extracted from the RTP header are used to determine the weights, so that the proposed algorithm can be implemented in real-time. In our simulations, we have relied on motion compensation and group of picture (GOP) data to determine the relative weights. Simulation results provided establish the significant improvement in performance based on our proposed approach to adaptive FEC.

This paper describes a computationally efficient method for estimating the predominant pitch in audio recordings. The proposed method is intended for building a content-based indexing and retrieval system that can search in a audio database using the melody line of a complex input audio sample. Available pitch estimation methods are effective primarily when dealing with recordings of human voice that is either unaccompanied or accompanied with one or two musical instruments. These methods perform poorly when applied to pitch estimation in complex music signals due to their reliance on directly estimating the fundamental frequency (F/sub 0/), a task that is affected by the overlapping presence in frequency of instrumental sounds such as those of guitar, piano, etc. In our method we exploit the higher harmonic structure of the human voice to develop a low-complexity system for estimating predominant pitch. Experimental results show that this computationally efficient method provides a robust estimate of predominant pitch in real-world audio signals with 85% success rate.

A new streaming protocol is proposed for multimedia applications. The proposed protocol, referred to as Lightweight Streaming Protocol (LSP), is an application layer protocol that sits atop UDP. The protocol is intended to improve the quality and reliability of media stream by borrowing features from reliable protocols such as retransmission and flow control while not sacrificing performance. The protocol offers semi-reliable transport. Instead of trying to guarantee 100% data delivery the protocol simply recovers as many packets as possible within a specified deadline. In addition the protocol incorporates features such as probabilistic redundant NAK transmission and flow control through selective frame dropping. Preliminary simulations show that LSP performs extremely well in channels with random packet loss such as congested networks. The protocol also performs reasonably well in channels that have short bursts of lost packets such as wireless networks.

Gesture and speech combine to form a rich basis for human conversational interaction. To exploit these modalities in HCI, we need to understand the interplay between them and the way in which they support communication. We propose a framework for the gesture research done to date, and present our work on the cross-modal cues for discourse segmentation in free-form gesticulation accompanying speech in natural conversation as a new paradigm for such multimodal interaction. The basis for this integration is the psycholinguistic concept of the coequal generation of gesture and speech from the same semantic intent. We present a detailed case study of a gesture and speech elicitation experiment in which a subject describes her living space to an interlocutor. We perform two independent sets of analyses on the video and audio data: video and audio analysis to extract segmentation cues, and expert transcription of the speech and gesture data by microanalyzing the videotape using a frame-accurate videoplayer to correlate the speech with the gestural entities. We compare the results of both analyses to identify the cues accessible in the gestural and audio data that correlate well with the expert psycholinguistic analysis. We show that "handedness" and the kind of symmetry in two-handed gestures provide effective supersegmental discourse cues.

A simple and effective method for modifying the pitch of recorded speech units is described. This method was developed to overcome some limitations in the promising residual-excited linear prediction (RELP) technique. The key difference is that the choice of filter parameters in the new method is driven by a need for reducing sensitivity to pitch modification, rather than creating a residual with minimum energy as in RELP. Speech modifications using this method are superior in quality to those obtained with RELP, while at the same time being less sensitive than RELP to errors in pitch marking.

A new method for modifying the pitch of units of recorded female speech is described. This method was developed to overcome limitations in an otherwise promising technique called residual-excited linear prediction (RELP). In the new method, the stored speech unit is processed with a suitably shaped time-varying filter. The filtered signal is modified according to the required change in the fundamental frequency. The modified filtered signal is applied to the inverse of the above-mentioned prefilter. Based on observations of spectra of multiple recordings of the same speech unit at different pitch frequencies, the magnitude response of the inverse filter was chosen to have a significantly less peaky structure than that which is typically obtained in LPC. Speech modifications using this method were found to be superior in quality to those obtained by RELP, while at the same time being less sensitive than RELP to changes in pitch marking.

Computer-aided detection and enhancement of microcalcifications in mammogram images are considered. The mammogram image is first decomposed into subimages using a 'subband' decomposition filter bank which uses nonlinear filters. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. All regions with high positive skewness and kurtosis are marked as a regions of interest. Next, an outlier labeling method is used to find the locations of microcalcifications in these regions. An enhanced mammogram image is also obtained by emphasizing the microcalcification locations. Linear and nonlinear subband decomposition structures are compared in terms of their effectiveness in finding microcalcificated regions and their computational complexity. Simulation studies based on real mammogram images are presented.

A new method for detecting microcalcifications in mammograms is described. In this method, the mammogram image is first processed by a subband decomposition filterbank. The bandpass subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. The detection method utilizes these two parameters. A region with high positive skewness and kurtosis is marked as a region of interest. Simulation results show that this method is successful in detecting regions with microcalcifications.

The effect of prefiltering and resizing on the diagnostic quality of compressed images at various compression ratios is examined in this paper. A key object of the work is to get an insight on selecting an optimal combination of resolution and quantizer coarseness for a given ratio of compression of medical images. Test images were decimated by several decimation factors, and compressed to achieve a target file size. The decompressed images were assessed for their diagnostic quality. The authors' results demonstrate two things: (1) the higher sampling rates allow a quality margin that may offset the effects of subsequent quantization; and (2) gains in quality due to higher resolution levels may eventually be outstripped by the degrading effects of compression as the compression ratio increases. The result suggests a more gradual reduction in resolution would provide better control over loss of quality.

Medical image scanners produce digitized information at different spatial resolutions, and this affects the perceived image quality after image compression. Quality of decompressed medical images is assessed by the extent to which diagnostic information is affected by compression, and the medical interpretation requires participation of medical experts in observer studies. We undertook a study to investigate the effect of resolution from digitization on medical image compression, in the context of enabling image transmission using low to moderate bandwidth telecommunication facilities. The test material consisted of a series of adult chest radiographs containing multiple subtle abnormalities. In this paper, the effects of sampling resolution and frequency component quantization on image quality are compared, and issues for further investigation in general image compression are discussed.>

A class of biorthogonal systems leading to linear-phase wavelets is presented. A notable feature of this structure is that the wavelets are derived from a filter bank where the lowpass analysis filter is constrained to be a halfband filter. The authors derive finite impulse response (FIR) biorthogonal solutions from a pair of Lagrange halfband filters. They also consider infinite impulse response (IIR) biorthogonal solutions based on a pair of zero-phase halfband filters derived from Butterworth halfband filters.>

The authors have described the framework underlying continuous and discrete families of nonseparable two-dimensional wavelets and an M-channel nonrectangular multiresolution wavelet representation for L/sup 2/(R/sup 2/) functions and I/sup 2/(Z/sup 2/) sequences. Focusing on the case of quincunx sampled signals, solutions of digital filter banks for implementing the decomposition with different characteristics of linear phase and regularity for smoothing and wavelet functions are provided.>

In this paper, a new iterative method for any shaped pattern synthesis for one- and two-dimensional antenna arrays is described. The object is to meet prescribed constraints on the power pattern, with an assumption on the number of antenna array elements at specified locations. The synthesis problem is solved using the method of projection on convex sets, by modeling the constraints in terms of convex sets whose elements are vectors representing the excitation coefficients. Two underlying frameworks used in solving the problem are the l2Hilbert Space and an inner product space based on convolution. The synthesis procedure is implemented using a Fast Fourier Transform algorithm.

A new technique for the design of FIR fractional-slope phase filters based on Chebyshev approximation is described and analyzed. The technique results from a formulation of the problem which satisfies the Haar condition, thus allowing the use of the efficient Remez exchange algorithm. The new design is implemented with a modification of the McClellan-Parks-Rabiner FIR filter design program. The resulting fractional-slope phase filters are shown to have a complex error function that is essentially equiripple in magnitude. The new technique may be used for designing parallel elements of a multirate filter, such as the polyphase interpolation filter, or for stand-alone filters used as fractional-slope phase shifters. The advantages of the new technique are the simplicity and numerical stability of the design program and the lack of restrictions on phase slope specification.

A new scheme for a transmultiplexer is described. Using the polyphase network approach a filter bank composed of only all-pass digital filter sections was designed. The use of all-pass filters as basic building blocks is shown to provide a transmultiplexer structure that has low computational requirements, low quantization noise, and high modularity.