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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.

Communication of a multivariate Gaussian source transmitted over orthogonal additive white Gaussian noise channels using delay-free joint source channel codes (JSCC) is studied in this paper. Two scenarios are considered: (1) all components of the multivariate Gaussian are transmitted by one encoder as a vector or several ideally collaborating nodes in a network; (2) the multivariate Gaussian is transmitted through distributed nodes in a sensor network. In both scenarios, the goal is to recover all components of the multivariate Gaussian at the receiver. The paper investigates a subset of JSCC consisting of direct source-to-channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. A theoretical analysis that helps explain and quantify distortion behavior for such JSCC is given. Relevant performance bounds for the network are also derived with no constraints on complexity and delay. Optimal linear schemes for both scenarios are presented. Results for Scenario 1 show that linear mappings perform well, except when correlation is high. In Scenario 2, linear mappings provide no gain from correlation when the channel signal-to-noise ratio (SNR) gets large. The gap to the performance upper bound is large for both scenarios, regardless of SNR, when the correlation is high. The main contribution of this paper is the investigation of nonlinear mappings for both scenarios. It is shown that nonlinear mappings can provide substantial gain compared to optimal linear schemes when correlation is high. Contrary to linear mappings for Scenario 2, carefully chosen nonlinear mappings provide a gain for all SNR, as long as the correlation is close to one. Both linear and nonlinear mappings are robust against variations in SNR.

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.

Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain a successful separation. To capture sample dependence, a latent source is sometimes modeled by autoregressive or moving average models with an independent and identically distributed error or residual. However, these models are limited by assuming only linear dependence among a source's samples. This paper proposes a new blind source separation algorithm based on an autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model, which captures linear correlations, non-Gaussianity, and squared residuals' dependence. The AR part of the AR-ARCH model captures the correlation among samples. The ARCH part of the model captures the non-Gaussianity and nonlinear dependence among samples. The ARCH model also assumes the time-varying conditional variances for sources. We derive the Cramér Rao lower bound (CRLB) for the mixing matrix based on the AR-ARCH model. We perform simulations on both synthetic and real data. The results show that the proposed method outperforms the baseline algorithms especially for a small number of samples and approaches the CRLB.