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A wireless capsule endoscopy (WCE) is a good option for the screening of the digestive system. To better facilitate detection of diseases at an early stage, gastroenterologists seek both higher frame rate and frame resolution than what is offered in the current WCE standards. This necessitates higher data rate links through the human body. One possible way to obtain a high data rate without significantly increasing the transmission power is to use a large bandwidth, making ultra-wideband communication (UWB) a good candidate. However, in order to properly utilize the large bandwidth available through UWB communication, it is important that the channel attenuation does not increase too rapidly with frequency. In this paper, we investigate obtainable rates and necessary transmission power for an in-body to on-body UWB channel model in the 1-6 GHz range based on measurements taken in the abdominal region of several porcine subjects. We consider both the theoretically attainable minimum power needed to obtain a given capacity and the power needed to obtain the same rate with practically implementable modulation- and coding schemes for a target bit error rate. The presented results show that huge savings in transmission power are possible by communicating in the lower frequency ranges (1 - 2 GHz) where the attenuation is less severe. Further, about a 10-fold increase in transmission power has to be expected when practically implementable schemes are considered compared with the minimum achievable power derived from a capacity perspective. It is also illustrated that little is gained in terms of lowered power consumption (or increased rates) by using a bandwidth larger than 1 GHz due to the rate of decline of the channels transfer function with frequency. Copyright 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.

Ultra wideband (UWB) radio technology for wireless implants has gained significant attention. UWB enables the fabrication of faster and smaller transceivers with ultra low power consumption, which may be integrated into more sophisticated implantable biomedical sensors and actuators. Nevertheless, the large path loss suffered by UWB signals propagating through inhomogeneous layers of biological tissues is a major hindering factor. For the optimal design of implantable transceivers, the accurate characterization of the UWB radio propagation in living biological tissues is indispensable. Channel measurements in phantoms and numerical simulations with digital anatomical models provide good initial insight into the expected path loss in complex propagation media like the human body, but they often fail to capture the effects of blood circulation, respiration, and temperature gradients of a living subject. Therefore, we performed UWB channel measurements within 1-6 GHz on two living porcine subjects because of the anatomical resemblance with an average human torso. We present for the first time a path loss model derived from these invivo measurements, which includes the frequency-dependent attenuation. The use of multiple on-body receiving antennas to combat the high propagation losses in implant radio channels was also investigated. This is the authors accepted and refereed manuscript to the article. (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

In this paper, we propose a new feature for finding lesions in gastrointestinal tissues. Polyps or cancerous parts have different capillary pattern compared with normal parts. There are polyps which have higher density of vessel or capillary pattern. This feature leads us to extract remote photoplethysmogram signal from different parts of videos from gastrointestinal tissue. Due to the fact that hemoglobin absorbs more light than surrounding tissues, more changes are expected to be observed in the parts with higher density of vessels and capillaries. In the experimental results, rPPG signals is extracted from colonoscopy and endoscopy videos. This feature is used to distinguish between normal and abnormal tissues. It is shown that power of rPPG signal can be used to find lesion areas.

In this paper, a robust and power-efficient transmission scheme of a time-discrete signal with continuous amplitude over parallel fading channels is proposed. A block of source samples is decomposed into N layers, so-called layered source coder. Each layer is transmitted over its own fading subchannel. The end-to-end mean square error (MSE) distortion, including distortion caused by the quantization process, the lossy compression, and channel errors, is adopted as the performance metric. We derive a power allocation strategy across the layers to minimize the end-to-end MSE distortion. The proposed transmission scheme gives higher robustness over the entropy-constrained source-channel coding scheme. In addition, the proposed transmission scheme requires much lower transmission power for a given source signal to noise ratio (SNR s ) than the entropy-constrained scheme in the case of bandwidth compression. This power saving is meaningful for wireless communications with battery-operated devices.

The authors propose a low delay and low complexity communication system for wireless sensor networks based on a so-called direct-sum source coder. The source encoder converts a continuous-amplitude signal into an n-tuple binary representation directly by a scalar quantisation. The authors derive the mean-square error distortion of the proposed scheme from which the power allocation strategy can be optimally designed for each of the quantisation bits. The authors also consider the performance of a conventional system where the Lloyd–Max source coder/decoder is employed. The simulation and analytical results show that the proposed system offers superior performance compared with the conventional system with low-system complexity and low delay. Furthermore, this investigation reveals that there is no performance gain obtained from the optimal power allocation over the equal gain allocation for low signal-to-noise ratio, when no channel state information is available at the transmitter side. Based on this observation, a well-structured form of power allocation for the whole SNR range is achieved.

Maximum regular wavelet filter banks have received much attention in the literature, and it is a general conception that they enjoy some type of optimality for image coding purposes. To investigate this claim, this article focuses on one particular biorthogonal wavelet filter bank, namely, the 2-channel 9/7. As a comparison, we generate all possible 9/7 filter banks with perfect reconstruction and linear phase while having a different number of zeros at z=−1 for both analysis and synthesis lowpass filters. The best performance is obtained when the filter bank has 2/2 zeros at z=−1 for the analysis and synthesis lowpass filters, respectively. The competing wavelet 9/7 filter bank, which has 4/4 zeros at z=−1, is thus judged inferior both in terms of objective error measurements and informal visual inspections. It is further shown that the 9/7 wavelet filter bank can be obtained using gain-optimized 9/7 filter bank.

In this paper, we propose an algorithm to extract heart rate frequency from video camera using the Multiple SIgnal Classification (MUSIC) algorithm. This leads to improved accuracy of the estimated heart rate frequency in cases the performance is limited by the number of samples and frame rate. Monitoring vital signs remotely can be exploited for both non-contact physiological and psychological diagnosis. The color variation recorded by ordinary cameras is used for heart rate monitoring. The orthogonality between signal space and noise space is used to find more accurate heart rate frequency in comparison with traditional methods. It is shown via experimental results that the limitation of previous methods can be overcome by using subspace methods.

In this work, localizing a capsule endoscope within the gastrointestinal tract is addressed. It is assumed that the capsule is equipped with a magnet, and that a magnetic sensor network measures the flux from this magnet. We assume no prior knowledge on the source location, and that the measurements collected by the sensors are corrupted by thermal Gaussian noise only. Under these assumptions, we focus on determining the Cramer-Rao Lower Bound (CRLB) for the location of the endoscope. Thus, we are not studying specific estimators, but rather the theoretical performance of an optimal one. It is demonstrated that the CRLB is a function of the distance and angle between the sensor network and the magnet. By studying the CRLB with respect to different sensor array constellations, we are able to indicate favorable constellations.

Real-time lossy compression of voltage and current measurements in an electrical grid is considered. It is assumed that a compressed version of measurements needs to be transmitted to the control center to be used for line voltage compensation. This necessitates tight delay and high accuracy requirements. Acquiring high compression, while also maintaining a high-quality reconstruction is achieved via differential quantization of consecutive samples. Zero delay is made possible using sample-by-sample processing of signals. The proposed scheme consists of two adaptive quantizers on transformed versions of voltage and current signals using Park's transform. The quantizers incorporate a predictor which is based on spectral characteristics of the transformed signals. The objective is to compress the measurements as efficiently as possible, while keeping the voltage within the stipulated values by grid standards. The suggested scheme is then applied in a model of a micro-grid with a voltage source converter based controller. The trade-off between compression ratio and quality of the grid voltage and current in terms of deviation from nominal values is studied and discussed.

This paper presents a power-saving communication system for the link from an in-body sensor to an on-body sensor over the abdominal channel in medical wireless body area networks. The idea of the work is to exploit the time diversity of the channel coding and the high dimensional signal constellation of M-ary pulse position modulation (M-PPM) to improve the system performance. In addition to the illustration of the significant performance improvement of the proposed communication system, the severe fading effect of the abdominal channel compared with the conventional Rayleigh channel is investigated through the theoretical capacity and bit error rate (BER) performance.

When designing coding algorithms for the wireless endoscope, achieving good performance with very low complexity is of great importance. This is due to the fact that hardware implementation is much constrained by the small size of the endoscope capsule and available power is also very limited. In this paper, we propose a very low complexity image coder for the wireless endoscope which operates within these constraints, with special emphasis on low bit rate performance. The scheme encodes the captured images frame-by-frame using simple DPCM (Differential Pulse Coded Modulation) coding, combined with multi-rate processing, dead-zone quantization and efficient run-length coding of the quantization indices. Simulation results show that proposed coder is able to meet our objectives.

We study the communication problem in a sensor network which consists of multiple sensor nodes that observe memoryless Gaussian sources which are inter-correlated. The observations are transmitted over orthogonal additive white Gaussian noise channels, and all source symbols are to be recovered at the receiver. We focus on communication schemes which utilize direct source to channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. The distortion lower bound for the network with more than two sensors case is derived. Optimal linear schemes, both distributed and cooperative, are presented. Results show that the gap to the performance upper bound is large when there is high correlation and it increases significantly when the network size is large. We then present nonlinear mappings which can be implemented distributedly and show that they can provide substantial gain when the correlation is close to one. Examples are given for networks with two and three nodes.

This paper considers the well known problem of ringing distortion in subband coded images. Exploiting the fact that ringing artifacts are masked by edges and textures we propose a new method designed to remove all ringing distortion within the smooth image areas. The original image is used for classification purposes and the subband coded image is postprocessed using blockwise paraboloid fitting algorithms in the smooth image areas. Experimental results comparing subband coded images with and without application of the proposed method are presented. The results indicate that significant visual improvements can be made at the expense of a very small increase in bit-rat.

This paper proposes a limited feedback rate transmission scheme for wireless sensor networks over slowly fading channels. The design approach is based on joint source and channel coding, where source coding characteristics are taken into account to design the transmission protocol for fading channels. The transmission protocol uses an unequal protection regime for different bit groups according to their priority levels while taking into consideration of the variations within and among fading channels. Only group indices are transmitted on the feedback channel from the fusion center to the sensors. This means the feedback channel can operate at a low data rate. Furthermore, it is shown by both theoretical analysis and simulation that the proposed transmission scheme has better performances compared to the distributed beamforming counterpart in low signal-to-noise ratio (SNR) regime.

Innovative medical applications such as implant wireless sensors for health monitoring, automatic drug deliverance, etc. can be realized with the use of ultra wideband (UWB) radio technology. Nevertheless, for efficient design of wireless systems operating inside the human body a radio communication channel model is essential. Although a lot of research effort has recently been devoted to the characterization of the on-body UWB radio communication channel, just a few works describing the radio propagation inside the human body have been reported. To address this problem, a computational study of the propagation of UWB signals through human tissues in the 0.1–1 GHz and 1–6 GHz frequency bands is presented in this paper. This is based on numerical simulations using a heterogeneous anatomical model of the human body with frequency dependent tissue material properties. Subsequently, a statistical channel model is introduced for UWB in-body communications in the 1–6 GHz frequency band. The model is provided for two typical depths inside the human chest. This work contributes to the practical design of UWB medical implant communication systems.

Estimating an unknown parameter using a sensor network has been considered when the fusion center receives only one bit from each sensor. The network is divided to a number of groups and all sensors in a group use a fixed and equal threshold for quantization. A combined weighted estimation structure has been proposed. At the fusion center weights are assigned to each group of sensors based on their quantized bit entropy. The results have been presented and compared to maximum likelihood estimator (MLE), which had been proposed for the same scenario. The simulation results show that the proposed method has identical performance for large number of sensors and reaches Cramer-Rao lower bound(CRLB), while it outperforms MLE for limited number of sensors and when the distance between quantization levels increases. Moreover the proposed method has very low complexity compared to MLE. It is also considerably faster than MLE, which makes it more suitable for wireless sensor networks.

The utilization of wireless technology in traditional medical services provides patients with enhanced mobility. This has a positive effect on the recovery speed of a patient after major surgical procedures or prolonged illness. This paper presents the architecture of a healthcare wireless network that exploits the capabilities of ultra wideband technology (UWB) for medical sensing and in-body tracking and imaging. The communication requirements for an UWB wireless body area network (WBAN) are enlisted. Both on-body and in-body sensors are taken into consideration. Special attention is paid to the requirements of a capsule endoscope, which is one of the most throughput-demanding sensors in modern telemedicine.

A joint source channel coder (JSCC) for image transmission over flat fading channels is presented. By letting the transmitter have information about the channel, and by letting the code-rate vary slightly around a target code-rate, it is shown how a robust image coder is obtained by using time discrete amplitude continuous symbols generated through the use of nonlinear dimension changing mappings. Due to their robustness these mappings are well suited for the changing conditions on a fading channel.

Use of wireless biomedical sensor networks in complex clinical diagnostics and treatments may provide greater flexibility for both patient and medical staff as some of the same sensors could follow the patient throughout the cycle of treatment. A typical scenario from an intensive care unit, with data from three vital sign sensors, is used as an example for both hardware and software simulations. Results show that data throughput in such biomedical networks is greatly depended on packet size while competing for channel access. A new scheduling algorithm has been proposed for multi-hop networks. Some of the hardware limitations are identified, where the trade off between hardware and software systems is demonstrated

For a wireless system we assume that we are free to design the transmitter and receiver from the ground up, enabling the use of continuous-amplitude systems like Shannon mappings. Transport networks on the other hand are standardised and packet based, requiring a digital representation. In this paper a simple and effective scheme for transcoding, or digitising, the 2:1 bandwidth reducing Shannon mapping is proposed. This enables communication over heterogeneous networks without having to decode the Shannon mapping and re-encode with a digital source coder. It is shown that a simple uniform scalar quantizer operating on the one-dimensional channel symbols can outperform a 2-D entropy-constrained vector quantizer operating on the decoded signals from the Shannon mapping. The quantization can be performed either at the transmitter side, or at the receiver side. Both approaches are only 2.8 dB away from the theoretical optimum when operating at the channel capacity

The combination of a source coder and a digital modulator is in this article viewed as an analog-to-analog converter with signal compression ability. A reference-channel with defined available bandwidth, total received power, and noise statistics, can transmit one analog source signal. Compression is here defined as the number of such source signals that the digital system can transmit over the reference channel, and reconstruct with the same fidelity as the transmitted analog source signal. An image transmission example is provided where, for a given fidelity on a reconstructed image, the obtainable compression ratio is found for a digital compression system based on subband coding and pulse amplitude modulation. >

A new method for lossless coding of the classification table in low bit rate subband image coders with block-wise classification is proposed. The method is referred to as hierarchical enumeration and uses an algorithmic enumeration technique to associate a fixed-length unique binary codeword with each possible combination of integers defining the classification table. The resulting bit rate is less than the first order entropy of the classification table. When using hierarchical enumeration in combination with pyramid vector quantization of the subband samples, a high performance image coder using only fixed-length codewords is obtained. The proposed coder compares favorably with state-of-the art image coders using variable-length coding.

In this work, localisation of a source within an absorbing medium is considered. By an absorbing medium, the authors mean an environment where the signal power decays exponentially with distance. The authors assume that the source is heard by nearby sensors when transmitting and its position shall be estimated based on the received signal strength (RSS) by these sensors. Under these assumptions, the focus is to determine the Cramer–Rao lower bound (CRLB). Thus, the goal is to derive the theoretical performance limit for an optimal estimator, and to study the feasibility of RSS-based localisation in an absorbing environment and specifically in human abdominal region. The authors demonstrate that the CRLB greatly depends on the shadowing conditions, and also on the relative positions of the sensors and the source. Although the obtained results are quite general, the motivating application is localisation of capsule endoscope in human abdominal region. The authors find that the RSS-based method can reach the needed accuracy for localising a capsule endoscope.

This paper deals with still image subband coding with adaptive filter banks. Based on an analysis of distortions in coders with fixed filter banks with different characteristics, the scheme with spatially adaptive filter banks is developed. The main objective is to construct a subband coder in which both ringing noise and blocking artifacts are reduced. With respect to local image characteristics, suitable filter responses are selected to achieve this aim. The selection is based on measuring of edge business and edge orientation. A second objective is to reduce border distortions caused by the circular extension method by employing filters with short unit pulse responses in the border regions. It turns out that subband coding with adaptive filter banks in combination with an optimum filter selection strategy achieves a good trade-off between ringing noise and blocking effects. We conclude that the presented scheme for automatic filter selection works well for a large class of images with reasonably high contrast. For 'smooth-looking' images further work to improve the robustness of the algorithm is needed. The use of adaptive filtering in the image border regions solves--to some extent--the distortion problems encountered when using the circular extension method.

Filter banks based on recursive filters have recently been applied with success in subband coding of still images [1 2]. Their main feature low computational complexity make these filter banks extremely attractive for video coding applications. In. this paper we demonstrate the suitability of these filter banks in low bit rate image sequence coders operating in the 64 200 Kbps range. In particular we compare the performance of two types of recursive filter banks with that of a more traditional one based on quadrature mirror FIR filters (QMF). It is shown that the performance of our hR based systems is comparable to that of the more complex FIR based systems. Also we show that the encoding strategy of CCITT''s reference model no. 7 can be adapted successfully to fit into our subband coding scheme. Finally we discuss alternative approaches to motion estimation in hybrid image sequence coders and show preliminary results in the context our video subband coder.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents efficient filtering algorithms for the interpolation of sequences using IIR filters. Of the suggested filters, the most efficient in terms of computational complexity consists of a very simple allpass IIR filter in parallel with a pure delay. The nonlinear phase distortions of IIR filters, particularly severe in image processing, are avoided by a zero-phase implementation obtained by a combination of causal and anticausal filters. The proposed approach is more efficient for image zooming applications than other methods and yields visually satisfactory results for most gray-scale images.