Limited access to high-quality medical data poses a significant obstacle to automated diagnoses in medical modalities like Wireless Capsule Endoscopy (WCE), hindering potential advancements in automated medical diagnoses. This study presents a meticulously curated WCE dataset CAPTIV8, focused on the large colon and its pathologies, including Ulcerative Colitis (UC). Comprising a total of 1352 short video segments, totaling more than 200,000 frames with high mucosal visibility, the dataset features eight distinct types of pathology, along with signs of UC, accompanied by clinician-assigned text descriptions. To enhance its medical utility, the dataset integrates overlapping diagnoses from three diagnostic modalities: traditional and capsule endoscopy, and histology. Key attributes such as cleansing scores, text reports, capsule camera calibration and localization data have been incorporated to broaden its applicability in medical and artificial intelligence research. Designed for a wide spectrum of research challenges, from basic classification tasks to 3D reconstruction, CAPTIV8 aims to advance the incorporation of automated solutions in WCE diagnosis. The dataset can be accessed here:https:// dataverse.no/dataset.xhtml?persistentId=doi:10.18710/BSXNA1.

In this paper an approach to joint source-channel coding (JSCC) named Shannon-Kotel’nikov (S-K) mappings is discussed. S-K mappings are continuous, or piecewise smooth direct source-to-channel mappings operating on amplitude-continuous and discrete-time signals, and they encompass several existing JSCC schemes as special cases. Many existing approaches to analog- or hybrid discrete-analog JSCC provide both excellent performance as well as robustness to variable noise level at both low and arbitrary complexity and delay. However, a general theory explaining their performance and behaviour, as well as guidelines on how to construct well-performing mappings, do not exist. Therefore, such mappings are often based on educated guesses inspired by configurations that are known in advance to produce good solutions through numerical optimization methods. The objective of this paper is to develop a theoretical framework for analysis of analog- or hybrid discrete-analog S-K mappings which enables calculation of distortion when applying them on point-to-point links, reveal more about their fundamental nature, and provide guidelines for their construction at low as well as arbitrary complexity and delay. Such guidelines will likely help constrain solutions to numerical approaches and help explain why deep learning approaches obtain the solutions they do. The overall task is difficult and we do not provide a complete framework at this stage: We focus on high SNR and memoryless sources with an arbitrary continuous unimodal density function and memoryless Gaussian channels. We also provide example mappings based on surfaces which are chosen, or constructed, based on the provided theory.

3D shape reconstruction from images is an active topic in computer vision. Shape-from-Shading is an important approach which requires the surface properties and light source position to infer the 3D shape. A L2 regularizer is typically used to penalize the irradiance equation. In this article, anisotropic diffusion (AD) is introduced as a regularizer to solve the image irradiance equation. The method is then compared with L1 and L2 regularization methods, where all of the three techniques are formulated using gradient descent. Results shows that with AD, edges can be better preserved. AD shows lower depth error and higher correlation when compared with L1 and L2 regularization methods.

3D shape reconstruction from images is an active topic in computer vision. Shape-from-Focus (SFF) is an important approach which requires image stack in a focus controlled manner to infer the 3D shape. In this article, 3D reconstruction of synthetic gastrointestinal regions is done using SFF. Image stack is generated in Blender software with focus controlled camera. A color focus measure is applied for shape recovery followed by a weighted L2 regularizer to estimate for inaccurate depth values. A precise comparison is done between recovered shape and ground truth data by measuring the depth error and correlation between them. Results shows that SFF technique will be practical for 3D reconstruction of GI regions with focus and motion controlled pillcams which is technologically feasible to implement.

Serving as peers in the central nervous system, neurons make use of two communication paradigms, electrochemical, and molecular. Owing to their effective coordination of all the voluntary and involuntary actions of the body, an intriguing neuronal communication nominates as a potential paradigm for nano-networking. In this paper, we propose an alternative representation of the neuron-to-neuron communication process, which should offer a complementary insight into the electrochemical signals propagation. To this end, we apply communication-engineering tools and abstractions, represent information about chemical and ionic behavior with signals, and observe biological systems as input-output systems characterized by a frequency response. In particular, we inspect the neuron-to-neuron communication through the concepts of electrochemical communication, which we refer to as the intra-neuronal communication due to the pulse transmission within the cell, and molecular synaptic transmission, which we refer to as the inter-neuronal communication due to particle transmission between the cells. The inter-neuronal communication is explored by means of the transmitter, the channel, and the receiver, aiming to characterize the spiking propagation between neurons. Reported numerical results illustrate the contribution of each stage along the neuronal communication pathway, and should be useful for the design of a new communication technique for nano-networks and intrabody communications.

Neuronal communication is a biological phenomenon of the central nervous system that influences the activity of all intra-body nano-networks. The implicit biocompatibility and dimensional similarity of neurons with miniature devices make their interaction a promising communication paradigm for nano-networks. To understand the information transfer in neuronal networks, there is a need to characterize the noise sources and unreliability associated with different components of the functional apposition between two cells-the synapse. In this paper, we introduce analogies between the optical communication system and the neuronal communication system to apply results from optical Poisson channels in deriving theoretical upper bounds on the information capacity of both the bipartite and tripartite synapses. The latter refer to the anatomical and functional integration of two communicating neurons and surrounding glia cells. The efficacy of information transfer is analyzed under different synaptic setups with progressive complexity, and is shown to depend on the peak rate of the communicated spiking sequence and neurotransmitter (spontaneous) release, neurotransmitter propagation, and neurotransmitter binding. The results provided serve as a progressive step in the evaluation of the performance of neuronal nano-networks and the development of new artificial nano-networks.

Biological structures are typically based on molecular communication systems which use a myriad of molecule types to encode messages. Among the cells found in living organisms, interconnected neurons communicate by means of neurotransmitters, particles that serve as physical carriers of information. Owing to information propagation among the nano-scale components, neuronal communication is recently identified as a potential candidate for nano-networking. This paper elaborates on the concept of molecular synaptic transmission between neurons, aiming to give an insight into the performance of physical end-to-end model according to the cell physiology. The synaptic transmission is investigated from several aspects: the transmitter (pre-synaptic terminal), the channel (synaptic cleft), and the receiver (post-synaptic terminal), with a goal to characterize the propagation of the spiking rate function between neurons. Moreover, some ideas on how to incorporate the impact of astrocytic processes to the neuronal communication are presented.

In this paper, nonlinear distributed joint source-channel coding (JSCC) schemes for transmission of multivariate Gaussian sources over a Gaussian multiple access channel are proposed and analyzed. The main contribution is a zero-delay JSCC named Distributed Quantizer Linear Coder (DQLC), which performs relatively close the information theoretical bounds, improves when the correlation among the sources increases, and does not level off as the signal-to-noise ratio (SNR) becomes large. Therefore it outperforms any linear solution for sufficiently large SNR. Further an extension of DQLC to an arbitrary code length named Vector Quantizer Linear Coder (VQLC) is analyzed. The VQLC closes in on the performance upper bound as the code length increases and can potentially achieve the bound for any number of independent sources. The VQLC leaves a gap to the bound whenever the sources are correlated, however. JSCC achieving the bound for arbitrary correlation has been found for the bivariate case, but that solution is significantly outperformed by the DQLC/VQLC when there is a low delay constraint. This indicates that different approaches are needed to perform close to the bounds when the code length is high and low. The VQLC/DQLC also apply for bandwidth compression of a multivariate Gaussian transmitted on point-to-point links.

Ultrawideband (UWB) radio technology for wireless implants has gained significant attention. UWB enables the fabrication of faster and smaller transceivers with ultralow 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 in vivo 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.

In this paper we generalize an existing distributed zero-delay joint source-channel coding scheme for communication of a multivariate Gaussian on a Gaussian Multiple Access Channel named Distributed Quantization Linear Coder (DQLC) to arbitrary code length. Although the DQLC is well performing, it leaves a certain gap to the performance upper bound (or distortion lower bound) based on arbitrary code length. The purpose of this paper is to determine if the generalization of the DQLC to arbitrary code length, named Vector Quantization Linear Coder (VQLC), can close the gap to the bound when the code length is large. Our results show that the VQLC mapping has the potential to reach the upper bound for any number of Gaussian sources at high SNR when the sources are uncorrelated. We also approximately determine the VQLC performance as a function of code length for the special case of two sources.

In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improves with increasing correlation and are robust against variations in noise level. Both schemes also exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large.

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 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.

This paper deals with lossy joint source-channel coding for transmitting memoryless sources over AWGN channels. The scheme is based on the geometrical interpretation of communication by Kotel'nikov and Shannon where amplitudecontinuous, time-discrete source samples are mapped directly onto the channel using curves or planes. The source and channel spaces can have different dimensions and thereby achieving either compression or error control, depending on whether the source bandwidth is smaller or larger than the channel bandwidth. We present a general theory for 1:N and M:1 dimension changing mappings, and provide two examples for a Gaussian source and channel where we optimize both a 2:1 bandwidth-reducing and a 1:2 bandwidth-expanding mapping. Both examples show high spectral efficiency and provide both graceful degradation and improvement for imperfect channel state information at the transmitter.

Summary form only given. We compute the noise components in 1:N dimension expanding (error protection) and M:1 dimension reducing (lossy compression) systems for memoryless Gaussian sources transmitted over AWGN channels. A theory for dimension reducing systems was developed wherein the source- and channel coders are combined into one nonlinear operation and the parametric curves are used to map the source onto the channel. For both systems we have two types of distortion. Results show that the 2:1 system works very well and that the 1:2 system is not as close to optimum but the gain compared to a linear system is approximately the same as in the 2:1 case. The solid graphs illustrate the characteristics of the two distortion contributions. The small noise contribution and approximation noise above the optimum point (increasing CSNR), and the threshold- and channel noise below the optimum point