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Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consumed for sensing and communications. The proposed approach uses a long short-term memory (LSTM) model that learns spatiotemporal patterns in sequences of sensorial data for future predictions. The LSTM model and the sensors collaboratively monitor the environment. They are controlled by a reinforcement learning (RL) agent that dynamically decides about using the LSTM prediction versus physical sensing in a way that maximizes energy saving while maintaining prediction accuracy. Two approaches are used for the RL: 1) the Markov decision process (MDP) model-based for low scale applications and 2) deep Q-Network-based for larger scales. Compared to the current literature, the proposed solution is unique in predicting all sensor readings for real-time event detection and providing a model capable of learning long-term spatiotemporal correlations, enabling power conservation and detection accuracy balance. We compare the proposed solutions to the most relevant state-of-the-art approaches using a large real dataset collected in a dynamic space by measuring the accuracy, consumed energy, network lifetime, latency, and missed events' ratio. To investigate the scalability of the solutions, these parameters are calculated for different network sizes. The results show that the system achieves 50% accuracy with 32% of activation time and 75% accuracy with 60% activation time.

Extracellular vesicle (EV)-mediated signaling is a highly evolutionary conserved intercellular mechanism in which the biological information is packaged, transported, and delivered to the target cells. The potential of EVs for diagnostic and therapeutic applications has recently received significant attention. To enhance the potential of EVs for more extensive functionalities, here we motivate the concept of engineered EV-mediat-ed communication nanonetworks. Corresponding with the EV-mediated intercellular signaling network components that naturally extend the limited ability of individual cells to perform the intricate vital functions, we propose a fundamental molecular communication network framework and review the potential experimental techniques to obtain data for its validation. The proposed framework paves the way for the development of engineered EV-mediated communication networks and enables novel approaches, perspectives, tools, and metrics to reveal the principles and emergent properties of biological EV-mediat-ed signaling networks the understanding of which is still obscure.

Wireless intra-body communication is a promising approach for providing efficient and secure connectivity for medical implants. The low power consumption of the electronics and the conductivity of the biological tissues facilitate the system implementation, which makes the technique more power-efficient than the traditional radio frequency wireless systems. The galvanic intra-body communication uses the electrical current for signal transmission in the conductive medium of the biological tissues. In this paper, we propose an ultra-low-power communication approach by implementing a galvanic impulse method for communication between an implant and an on-body device. The communication system is designed, manufactured with off-the-shelf electronic components, and measured in the phantom and in-vivo animal experiment. The implant power consumption is $45~\mu \text{W}$ for the data rate of 64 kbps with a bit error percentage below 0.5% for the implant depth of 14 cm. The design supports long-lasting battery-powered implant sensory and communication system.

Conductive impulse signaling is proposed for high-efficiency low data-rate wireless communication for future development in cardiac leadless pacemaker technology. The conductivity of the heart muscles and blood is used for electrical signal transmission between two pacemaker implants in the right atrium and the right ventricle. The communication channel is characterized in the frequency range of 1–15 MHz using numerical electromagnetic computations. The wideband implant electrodes support the impulse signaling, and the frequency flat communication channel prevents impulse distortion that simplifies the receiver design using a threshold detector scheme. An ultralow-power impulse generator is designed using off-the-shelf electronics and is integrated into a leadless pacemaker mockup, and the impulse transmission between two capsules is measured in a homogeneous phantom and in vivo animal heart experiments. The feasibility of system integration with the state-of-the-art leadless cardiac pacemakers is demonstrated that can support ultralow-power, low data-rate wireless communications.

Information theory provides maximum possible information transfer over communication channels, including neural channels recently emerged as remarkable for disruptive nano-networking applications. Information theory was successfully applied to quantify the ability of biological sensory neurons to transfer the information from dynamic stimuli. However, a little of information theory has been subjected to quantify the reliability of neuro-transmission between synaptically coupled neurons. Neuro-transmission, regarded as molecular synaptic communication, relays information between neurons and significantly affects the overall brain processing performance. In this study, we use concepts from information theory to provide the framework based on closed-form expressions that quantify the information rate allowing assessment of neuro-transmission when the parameters are provided for any type of neurons. Considering Poissonian statistics and the rate coding model of neural communication, we show how the information transferred between cortical neurons depend on the molecular, physiological and morphological diversity of cells, the firing rate, and the synaptic wiring. With synaptic redundancy, we infer the ability of an isolated post-synaptic neuron to reliably convey information encoded in the spike train from a pre-synaptic neuron. Estimating information rate between neurons primarily serves in the evaluation of the overall performance of biological neural nano-networks and the development of artificial nano-networks.

Mean arterial pressure (MAP) is an important clinical parameter to evaluate the health of critically ill patients in intensive care units. Thus, the real time clinical decision support systems detecting anomalies and deviations in MAP enable early interventions and prevent serious complications. The state-of-the-art decision support systems are based on a three-phase method that applies offline training, transfer learning, and retraining at the bedside. Their applicability in critical care units is challenging with delay and inaccuracy. In this article, we propose a real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure. The proposed system works in real time at the bedside without requiring the offline phase for training using large datasets. It thereby enables timely interventions and improved healthcare services. The proposed machine learning structure includes two stages. Stage I applies online learning using hierarchical temporal memory (HTM) to enable real time stream processing and provides unsupervised predictions. To the best of our knowledge, this is the first time it is applied to medical signals. Stage II is a long short-term memory (LSTM) classifier that forecasts the status of the patient's MAP ahead of time based on Stage I stream predictions. We perform a thorough performance evaluation of the proposed system and compare it with the state-of-the-art systems employing logistic regression (LR). The comparison shows the proposed system outperforms LR in terms of the classification accuracy, recall, precision, and area under the receiver operation curve (AUROC).

Automatic polyp detection has been shown to be difficult due to various polyp-like structures in the colon and high interclass variations in polyp size, color, shape, and texture. An efficient method should not only have a high correct detection rate (high sensitivity) but also a low false detection rate (high precision and specificity). The state-of-the-art detection methods include convolutional neural networks (CNN). However, CNNs have shown to be vulnerable to small perturbations and noise; they sometimes miss the same polyp appearing in neighboring frames and produce a high number of false positives. We aim to tackle this problem and improve the overall performance of the CNN-based object detectors for polyp detection in colonoscopy videos. Our method consists of two stages: a region of interest (RoI) proposal by CNN-based object detector networks and a false positive (FP) reduction unit. The FP reduction unit exploits the temporal dependencies among image frames in video by integrating the bidirectional temporal information obtained by RoIs in a set of consecutive frames. This information is used to make the final decision. The experimental results show that the bidirectional temporal information has been helpful in estimating polyp positions and accurately predict the FPs. This provides an overall performance improvement in terms of sensitivity, precision, and specificity compared to conventional false positive learning method, and thus achieves the state-of-the-art results on the CVC-ClinicVideoDB video data set. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Dual chamber leadless pacemakers are multi-unit, battery-driven implants utilized for treating patients with bradyarrhythmias and sino-atrial dysfunctions. Establishing synchronization between the units provides coordination between the atrium and ventricular contraction, and this mechanism depletes battery energy. Due to implant size constraints, reducing the synchronization energy consumed to enhance the lifetime of the implant is crucial. In this paper, a set of strategies are proposed and evaluated to indicate the best strategy to enhance the lifetime of atrial unit based on the patient's heart condition. Beat selective pulse transmission is employed instead of pulse tr 4?0ansmission on every beat to reduce energy consumption. The characteristics of interbeat contraction timing of the atrium and ventricle from the patient data is modeled as time series. The designed model is extended to model synchronization strategies with sufficient synchronization accuracy and reduction in energy consumption. It is found that the implant lifetime is dependent on the natural atrial contraction probability, which is patient specific. A relation between the transmission duty-cycle and natural atrial contraction probability is derived for all the strategies, and this analysis is used in a case study to quantify the longevity. The proposed strategies show improved lifetime in comparison to the reference strategy. In the case study, for natural atrial contraction probability of 0.1, longevity is increased by two orders in relation to the reference strategy with the longevity of 4 years. However, there is no one best strategy; instead, the most energy-efficient strategy is determined from patient's natural atrial contraction probability and tolerance to suboptimal coordination.

Novel non-invasive procedures, such as targeted drug delivery may enhance the efficacy of treatments of cardiac disorders. A possible solution is to utilize a molecular communication paradigm based on cell-derived nano-sized vesicles --- extracellular vesicles (EVs) --- that can be used as vehicles for therapeutic biological cargo. EVs can be engineered with specific cell-targeting transmembrane proteins to improve their pharmacokinetic properties. Here, we study the transport of EVs in a non-cellular component of cardiac tissue referred to as the cardiac extracellular matrix (ECM). We inspect EV diffusion and advection in cardiac ECM by considering 1) tortuosity, which describes the convoluted pathway of EV transportation, 2) volume fraction, which characterizes the porosity of the cardiac ECM; and 3) EV degradation according to their half-life in the body. We analytically describe the EV transportation dynamics via a partial differential equation and solve it numerically using finite element methods. The presented findings indicate that EV propagation is dependent on the cardiac ECM hindrance sources. Bulk flow in the cardiac ECM, however, can mediate the EVs to reach their distant target cells.

The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field. © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, https://doi.org/10.1145/3311950

The multiple measurement vector (MMV) problem is applicable in a wide range of applications such as photoplethysmography (PPG), remote PPG measurement, heart rate estimation, and directional arrival estimation of multiple sources. Measurements in the aforementioned applications exhibit a dependency structure, which is not considered in the general MMV algorithms. Modeling the dependency or the correlation structure of the solution matrix to MMV problems can increase the recovery performance. The solution matrix $X$ can be decomposed into a mixing matrix $A$ and a sparse matrix with independent columns $S$ . The key idea of this model is that the matrix S can be sparser than the mixing matrix $A$ . Previous MMV algorithms did not consider such a structure for $X$ . This paper proposes two algorithms, which are based on orthogonal matching pursuit and basis pursuit, and derives the exact recovery guarantee conditions for both approaches. We compare the simulation results of the proposed algorithms with the conventional algorithms and show that the proposed algorithms outperform previous algorithms especially in the case of the low number of measurements.

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.

Deep learning has delivered promising results for automatic polyp detection and segmentation. However, deep learning is known for being data-hungry, and its performance is correlated with the amount of available training data. The lack of large labeled polyp training images is one of the major obstacles in performance improvement of automatic polyp detection and segmentation. Labeling is typically performed by an endoscopist, who performs pixel-level annotation of polyps. Manual polyp labeling of a video sequence is difficult and time-consuming. We propose a semi-automatic annotation framework powered by a convolutional neural network (CNN) to speed up polyp annotation in video-based datasets. Our CNN network requires only ground-truth (manually annotated masks) of a few frames in a video for training and annotating the rest of the frames in a semi-supervised manner. To generate masks similar to the ground-truth masks, we use some pre and post-processing steps such as different data augmentation strategies, morphological operations, Fourier descriptors, and a second stage fine-tuning. We use Fourier coefficients of the ground-truth masks to select similar generated output masks. The results show that it is possible to 1) produce ~ 96% of Dice similarity score between the polyp masks provided by clinicians and the masks generated by our framework, and 2) save clinicians time as they need to manually annotate only a few frames instead of annotating the entire video, frame-by-frame.

Channel modeling is a challenging vital step towards the development of diffusion-based molecular communication networks (DMCNs). Analytical approaches for diffusion channel modeling are limited to simple and specific geometries and boundary conditions. Also, simulation- and experiment-driven methods are very time-consuming and computationally complex. In this paper, the channel model for DMCN employing the fundamental concentration Green’s function (CGF) is characterized. A general homogeneous boundary condition framework is considered that includes any linear reaction systems at the boundaries in the environment. To obtain the CGF for a general DMCN including multiple transmitters, receivers, and other objects with arbitrary geometries and boundary conditions, a semi-analytical method (SAM) is proposed. The CGF linear integral equation (CLIE) is analytically derived. By employing the numerical method of moments, the problem of CGF derivation from CLIE is transformed into an inverse matrix problem. Moreover, a sequential SAM is proposed that converts the inversion problem of a large matrix into multiple smaller matrices reducing the computational complexity. Particle-based simulator confirms the results obtained from the proposed SAM. The convergence and run time for the proposed method are examined. Further, the error probability of a simple diffusion-based molecular communication system is analyzed and examined using the proposed method.

For polyp detection, we adapt a Faster R-CNN (Ren et al. 2015) architecture shown in Fig. 11.1. Faster R-CNN has two stages: region proposal network (RPN), and a box classifier network. Both stages share a common set of convolutional layers as a feature extractor to reduce the marginal cost for detection. The RPN utilizes feature maps of the last convolutional layer to generate class-agnostic RoI proposals called anchors, each with an objectness confidence value. The anchors have different aspect ratios and scales. The classifier network crops these anchors from the feature maps of the last convolutional layer and feeds the cropped features to the remainder of the network in order to predict location and confidence values of the object class (polyps).

Miniaturization of implant antennas without significant performance degradation is of great interest for future medical devices. Systems operating at low frequencies are preferred in wireless implant technology because the tissues’ losses increase with frequency and the device’s power consumption is lesser in the low-frequency range. The extremely small antennas face significant performance degradation in terms of efficiency, impedance matching and have a very high-quality factor that makes the antenna susceptible to the surrounded medium and electronics. Considering the impedance of a micro-scale antenna in sub-GHz frequency, the antenna performs like a short circuit or open circuit, and it is almost impossible to match the antenna to a standard source impedance for communication or wireless power transfer. To achieve a realistic micro-scale antenna, the magnetoelectric (ME) antenna is a promising option. ME antenna is made of a piezoelectric material bonded with the magnetostrictive layer. This new technology can solve the problem of matching in micro size antennas and it performs better than an unmatched micro-scale loop antenna.

For realizing high-speed implant communications, ultra-wideband (UWB) transmission is one of the promising candidates. In this paper, an UWB transmitter diversity antenna was presented, and then the fundamental performances were numerically and experimentally evaluated. In addition, this paper discussed the path loss performance in an implant communication link a living animal. Consequently, the communication performance improvement by the implant side polarization diversity with the antenna developed in this study was evaluated and discussed. Our evaluation results demonstrated that the developed antenna can improve the communication performance by 7 dB to establish an outage rate of 0.01.

The heart consists of billions of cardiac muscle cells&mdash, cardiomyocytes&mdash, that work in a coordinated fashion to supply oxygen and nutrients to the body. Inter-connected specialized cardiomyocytes form signaling channels through which the electrical signals are propagated throughout the heart, controlling the heart&rsquo, s beat to beat function of the other cardiac cells. In this paper, we study to what extent it is possible to use ordinary cardiomyocytes as communication channels between components of a recently proposed multi-nodal leadless pacemaker, to transmit data encoded in subthreshold membrane potentials. We analyze signal propagation in the cardiac infrastructure considering noise in the communication channel by performing numerical simulations based on the Luo-Rudy computational model. The Luo-Rudy model is an action potential model but describes the potential changes with time including membrane potential and action potential stages, separated by the thresholding mechanism. Demonstrating system performance, we show that cardiomyocytes can be used to establish an artificial communication system where data are reliably transmitted between 10 s of cells. The proposed subthreshold cardiac communication lays the foundation for a new intra-cardiac communication technique.

Cardiovascular diseases continue to be a leading cause of morbidity and mortality worldwide. Cardiomyocytes, as the elementary heart components, play a crucial role in maintaining a healthy heart by coordinating contractions throughout the heart muscle that lead to a heartbeat. This study aims to characterize fine-grained ionic-level manipulation of cardiomyocytes for the controlled electrical activity that will offer new insights within the medical field. We explore the concept of Molecular Communications (MC) to analyze the propagation of potassium ions in the cardiomyocyte cytosol. By associating the number of the potassium ions in the cytosol with the membrane- and action potentials, we use metrics from the well-known Shannon's information theory to optimize the ionic injection process and manipulate cardiomyocytes electrical activity. In case ON/OFF keying modulation is adopted as the potassium ion injection method, the optimal input distribution in terms of information capacity follows the derived Bernoulli distribution. This study offers underlying concepts that can be exploited in the creation of cardiomyocyte signals either for data communication via cellular infrastructure or heart pacing. The framework presented here needs to be upgraded in the following phases and made more physiologically plausible. Open Access CC-BY

Secure communication can be considered as an integral part of the next generation implantable medical devices. With the advent of physical layer security (PLS) methods, confidential messages can be transmitted without the use of encryption keys. For analyzing the effectiveness of PLS for next-generation leadless cardiac pacemakers, we provide secrecy analysis using a performance metric of secrecy capacity. Secrecy capacity defines the secure transmission rate between legitimate nodes without leakage of information to an eavesdropper and depends on respective channel attenuations. The legitimate and eavesdropper channel attenuations are evaluated by 3D numerical electromagnetic simulations using a detailed human model. We do not assume eavesdropper to be located in specific directions or positions and considers it to be located anywhere around the body. We evaluate the secrecy capacity by defining a spherical grid for eavesdropper positions around the body with a radius of 1 m. The secrecy capacity of the entire space is evaluated by extrapolating the grid to different radial distances using free space path loss model. Moreover, by fixing application based secure communication rate, the entire space is divided into secure and in-secure volumes. The in-secure volume consists of all the eavesdropper positions from which the pacemaker can be eavesdropped. We also evaluated the angle from which the maximum leakage of information takes place and referred it as “Eve’s sweet spot angle”. Data for channel attenuations from phantom and in-vivo experiments is also utilized to validate and observe the differences between simulations and experiments. This work will help in design of the communication module of implanted leadless cardiac pacemakers with enhanced security on the physical layer. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods such as, automatic false positive learning and off-line learning, both of which can be incorporated with the region based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance compared to other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos., Comment: 9 pages

5G networks are expected to provide gigabit data rate to users via the millimeter-wave (mmWave) communication technology. One of the major problems faced by mmWaves is that they cannot penetrate buildings. In this paper, we utilize multihop relaying to overcome the signal blockage problem in an mmWave band. The multihop relay network comprises a source device, several relay devices, and a destination device and uses device-to-device communication. Relay devices redirect the source signal to avoid the obstacles existing in the propagation environment. Each device amplifies and forwards the signal to the next device, such that a multihop link ensures the connectivity between the source device and the destination device. We consider that the relay devices and the destination device are affected by external interference and investigate the bit error probability (BEP) of this multihop mmWave system. Note that the study of the BEP allows quantifying the quality of communication and identifying the impact of different parameters on the system reliability. In this way, the system parameters, such as the powers allocated to different devices, can be tuned to maximize the link reliability. We derive exact expressions for the BEP of M-ary quadrature amplitude modulation and M-ary phase-shift keying in terms of multivariate Meijer's G-function. Due to the complicated expression of the exact BEP, a tight lower bound expression for the BEP is derived using a novel Mellin-approach. Moreover, an asymptotic expression for the BEP at high SIR regime is derived and used to determine the diversity and the coding gain of the system. In addition, we optimize the power allocation at different devices subject to a sum power constraint such that the BEP is minimized. Our analysis reveals that optimal power allocation allows achieving more than 3-dB gain compared with the equal power allocation. This paper can serve as a framework for designing and optimizing mmWave multihop relaying systems... 2018 IEEE. Translations and content mining are permitted for academic research only.

This paper tackles the challenge of optimally placing relay nodes (RNs) in wireless sensor networks given a limited set of positions. The proposed solution consists of: 1) the usage of a realistic physical layer model based on a Rayleigh block-fading channel; 2) the calculation of the signal-to-interference-plus-noise ratio (SINR) considering the path loss, fast fading, and interference; and 3) the usage of a weighted communication graph drawn based on outage probabilities determined from the calculated SINR for every communication link. Overall, the proposed solution aims for minimizing the outage probabilities when constructing the routing tree, by adding a minimum number of RNs that guarantee connectivity. In comparison to the state-of-the art solutions, the conducted simulations reveal that the proposed solution exhibits highly encouraging results at a reasonable cost in terms of the number of added RNs. The gain is proved high in terms of extending the network lifetime, reducing the end-to-end- delay, and increasing the goodput.

Molecular motor networks (MMNs) are networks constructed from molecular motors to enable nanomachines to perform coordinated tasks of sensing, computing, and actuation at the nano- and micro- scales. Living cells are naturally enabled with this same mechanism to establish point-to-point communication between different locations inside the cell. Similar to a railway system, the cytoplasm contains an intricate infrastructure of tracks, named microtubules, interconnecting different internal components of the cell. Motor proteins, such as kinesin and dynein, are able to travel along these tracks directionally, carrying with them large molecules that would otherwise be unreliably transported across the cytoplasm using free diffusion. Molecular communication has been previously proposed for the design and study of MMNs. However, the topological aspects of MMNs, including the effects of branches, have been ignored in the existing studies. In this paper, a physical end-to-end model for MMNs is developed, considering the location of the transmitter node, the network topology, and the receiver nodes. The end-to-end gain and group delay are considered as the performance measures, and analytical expressions for them are derived. The analytical model is validated by Monte-Carlo simulations and the performance of MMNs is analyzed numerically. It is shown that, depending on their nature and position, MMN nodes create impedance effects that are critical for the overall performance. This model could be applied to assist the design of artificial MMNs and to study cargo transport in neurofilaments to elucidate brain diseases related to microtubule jamming.

Diffusive molecular communication (DMC) is envisioned as a promising approach to realize nanonetworks for various applications. Characterization of the diffusion channel is vital for the development of the DMC systems. In this paper, we assume that concentration Green's function (CGF) for a primary diffusion environment (without flow) is given. A semi-analytical approach is proposed based on the Green's second identity to obtain the CGF for diffusion in a secondary environment (the primary environment including a set of new boundaries). Employing the Green's second identity, a linear integral equation for the CGF of diffusion in the secondary environment is obtained which can be transformed to a linear system of equations using the numerical method of moments. This approach leads to a very high flexibility for using in a wide range of geometries and conditions compared to the pure analytical approaches and has much less complexity compared to the pure simulation approaches.

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extractor by adding extra polyp images to the training dataset to answer whether we need deeper and more complex CNNs or better dataset for training in automatic polyp detection and segmentation. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance on the 2015 MICCAI polyp detection dataset. The best results achieved are 72.59% recall, 80% precision, 70.42% dice, and 61.24% Jaccard. The model achieved state-of-the-art segmentation performance., 6

Wearable wireless medical sensors beneficially impact the healthcare sector, and this market is experiencing rapid growth. Globally, the digital health market is forecast to increase from $80 billion in 2015 to $200 billion in 2020. Medical body area networks (MBANs) improve the mobility of patients and medical personnel during surgery, accelerate the patients' recovery, while facilitating the remote monitoring of patients suffering from chronic diseases. Currently, MBANs are being introduced in unlicensed frequency bands, where the risk of mutual interference with other electronic devices can be high. Techniques developed during the evolution of cognitive radio (CR) can potentially alleviate these problems in medical communication environments. In addition, these techniques can help increase the efficiency of spectrum usage to accommodate the rapidly growing demand for wireless MBAN solutions and enhance coexistence with other collocated wireless systems. A viable architecture of an MBAN with practical CR features is proposed in this work. Additionally, ultra wideband (UWB) radio for the implementation of CR offers many advantages to MBANs, and some features of this technology can be exploited for effective implementation of CR. Conceptual aspects associated with energy harvesting and practical identification of spectrum opportunities for radio frequency (RF) energy scavenging motivates the options taken in the development of the protocols. The physical (PHY) and medium access control (MAC) layer aspects of the proposal are proposed in addition to their implementation challenges in the context of CR.

One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in a generative adversarial networks approach. We propose an edge filtering-based combined input conditioned image to train our proposed networks. This enables realistic polyp image generations while maintaining the original structures of the colonoscopy image frames. More importantly, our proposed framework generates synthetic polyp images from normal colonoscopy images which have the advantage of being relatively easy to obtain. The network architecture is based on the use of multiple dilated convolutions in each encoding part of our generator network to consider large receptive fields and avoid many contractions of a feature map size. An image resizing with convolution for upsampling in the decoding layers is considered to prevent artifacts on generated images. We show that the generated polyp images are not only qualitatively realistic but also help to improve polyp detection performance., 10 pages

Machine learning is widely used on stored data, recently it is developed to model real time streams. Applying machine learning on medical streams might lead to a breakthrough on emergency and critical care through online predictions. Modeling real time streams implies limitations to the current state-of-the-art of machine learning and requires different learning paradigm. In this paper, we investigate and evaluate two different machine learning paradigms for real time predictions of medical streams. Both the hierarchical temporal memory (HTM) and long short-term memory (LSTM) are employed. The performance assessment using both algorithms is provided in terms of the root mean square error (RMS) and mean absolute percentage error (MAPE). HTM is found advantageous as it provides efficient unsupervised predictions compared to the semi-supervised learning supported by LSTM in terms of the error measures. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Wireless capsule endoscopy is an advanced application of using ICT in healthcare and medicine. It is a non-invasive method to examine the gastrointestinal (GI) for early diagnosis and treatment. The capsule is equipped with a camera, light source, processing, control unit, and a transmitter to send the captured pictures or videos wirelessly to an external receiver system. The capsule is used to monitor small bowel diseases, esophageal diseases, and colonic diseases. The current data rate of the capsule endoscopes is limited to 1–2 Mbps with 2–3 frames per second and significant compression rates are applied to provide an operation time at least 8 hours inside the body. The data and frame rates might be sufficient for a capsule without locomotion, in which the capsule travels slowly, and the gastric wall movements mainly manage the speed. However, the next generation of the capsules will need remote locomotion and orientation control for fast and precise diagnosis. This application requests high data rate and low image or video compression rates with high frame rates for real-time navigation and control. Such a communication system becomes power hungry and limits the longevity of the device. Design of an ultra-low power wireless transmission scheme can assist the capsule device to save energy to handle the technology requirements. In this paper, we assess the communication systems of the capsule endoscopes and propose a battery-free high data rate backscatter wireless transmission scheme. The proposed system has been tested inside biological phantom in order to measure the path loss over different capsule depths. The average attenuation in a backscatter link inside biological phantom is 4.1 dB/cm in which is a linear function of capsule depth. According to link budget calculations, by using a 20 dBm transmitter at the outside of the body we can receive 8 Mbps data from the capsule at 10 cm depth. Our proposed system demonstrates superior performance compared to state-of-the-art systems in the literature.

Neurological disorders such as Alzheimers and Parkinsons diseases are associated with malfunctioning neurons, and neuronal signaling and communication pathways. Restoring the neuronal function is considered one of the important areas of research to understand the brain and to develop treatment methods. Neurons that fire above their baseline levels when the head points in a specific direction are called head direction (HD) cells. Knowledge about the connection between the motor function and the HD cells can lead to a potentially efficient method of controlling neurons with brain-machine interfaces. In this study, we explore the possibility of using an existing neuronal model to describe the stimuli and responses of HD cells by comparing outputs of the computational model with recordings available through a dataset. For this, we use the computationally simple Izhikevich neuron model. The method used is flexible and can easily be adapted to other models as well as other types of spike metrics. The obtained results yield inconclusive inferences but do not exclude the possibility of other computational neuronal models being able to describe the behavior of the neurons with the proposed method. Further research is needed if the proposed method with some modifications can be applied using a complete waveform for the Izhikevich model. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Disease-affected nervous systems exhibit anatomical or physiological impairments that degrade processing, transfer, storage, and retrieval of neural information leading to physical or intellectual disabilities. Brain implants may potentially promote clinical means for detecting and treating neurological symptoms by establishing direct communication between the nervous and artificial systems. Current technology can modify neural function at the supracellular level as in Parkinson’s disease, epilepsy, and depression. However, recent advances in nanotechnology, nanomaterials, and molecular communications have the potential to enable brain implants to preserve the neural function at the subcellular level which could increase effectiveness, decrease energy consumption, and make the leadless devices chargeable from outside the body or by utilizing the body’s own energy sources. In this study, we focus on understanding the principles of elemental processes in synapses to enable diagnosis and treatment of brain diseases with pathological conditions using biomimetic synaptically interactive brain-machine interfaces. First, we provide an overview of the synaptic communication system, followed by an outline of brain diseases that promote dysfunction in the synaptic communication system. We then discuss technologies for brain implants and propose future directions for the design and fabrication of cognitive brain-machine interfaces. The overarching goal of this paper is to summarize the status of engineering research at the interface between technology and the nervous system and direct the ongoing research towards the point where synaptically interactive brain-machine interfaces can be embedded in the nervous system. Synaptic Communication Engineering for Future Cognitive Brain-machine Interfaces © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Wireless capsule endoscopy (WCE) has revolutionised the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large number of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reduce the diagnosis time and improve the detection accuracy. To address this problem, the authors propose a CAD system for automatic detection of ulcer in WCE images. Firstly, they enhance the input images to be better exploited in the main steps of the proposed method. Afterward, segmentation using saliency map-based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden Markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results. This paper is a postprint of a paper submitted to and accepted for publication in [IET Image Processing] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

Novel implantable and externally controllable bio-nanomachines-based treatment strategies for Glioblastoma brain cancer have been proposed recently to bring hope to patients who suffer from this devastating cancer type. The main challenges in developing such strategies lie in both crossing the stringent Blood-Brain Barrier and maximizing the drug concentration at particular sites rich in Glioblastoma cells within safety guidelines. Aiming to provide a first step towards the realization of such a novel treatment method, here we propose analytical models to characterize and analyze an exosome-mediated brain drug delivery molecular communication system. We consider biophysical models and derive the closed-form transfer functions for a communication system that comprises of the engineered neural stem cells that release exosomes into the extracellular space in the brain and Glioblastoma-like cells that collect exosomes from the extracellular space in the brain. The presented numerical results show a dependency of the exosome propagation on various hindrance sources in the extracellular space and a limited operation performance at high frequencies that refer to the exosome concentration dynamics. The collection of exosomes by Glioblastoma-like cells show a dependency on high and stable exosome concentration in the extracellular space and low-frequency operation for a reasonable performance output.

In this paper, the effects of ventricular blood volume change on the cardiac wireless channel modeling at 2.4 GHz are presented for future multi-node leadless capsule pacemakers. Numerical electromagnetic simulations are implemented in the anatomical model of human body and experiments are done in a homogeneous liquid phantom. The results show that the coupling between the capsule antennas decreases with increasing blood volume, as the blood is a highly attenuating medium. The received power varies by 3.1 dB for the implant-to-implant link inside the heart and varies by 1.3 dB for the heart implant to sub-cutaneous implant, during the diastole and systole cycle. The findings are useful to develop an intra-body communication between leadless pacemakers providing timing information for synchronized pacing in a multi-chamber leadless pacemaker system.

To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system. F-CNNs are usually trained on binary masks for object segmentation. We propose the use of 2D Gaussian masks instead of binary masks to enable these models to detect different types of polyps more effectively and efficiently and reduce the number of false positives. The experimental results showed that the proposed 2D Gaussian masks are efficient for detection of flat and small polyps with unclear boundaries between background and polyp parts. The masks make a better training effect to discriminate polyps from the polyp-like false positives. The proposed method achieved state-of-the-art results on two polyp datasets. On the ETIS-LARIB dataset we achieved 86.54% recall, 86.12% precision, and 86.33% F1-score, and on the CVC-ColonDB we achieved 91% recall, 88.35% precision, and F1-score 89.65%. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

We consider in this letter the problem of constrained relay node (RN) placement where sensor nodes must be connected to base stations by using a minimum number of RNs. The latter can only be deployed at a set of predefined locations, and the two-tiered topology is considered where only RNs are responsible for traffic forwarding. We propose a one-step constrained RN placement (OSRP) algorithm which yields a network tree. The performance of OSRP in terms of the number of added RNs is investigated in a simulation study by varying the network density, the number of sensor nodes, and the number of candidate RN positions. The results show that OSRP outperforms the only algorithm in the literature for two-tiered constrained RNs placement.

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.

Designing novel artificial intra-body networks and/or synthetic neurons, which interact with operating cells and compensate for malfunctioning cells, requires understanding and quantifying the information transfer in neural networks. However, the latter is not studied enough in the existing literature. Here we quantify the information rate transmitted between two neurons by analyzing Poisson Multiple-Input Multiple-Output (MIMO) synaptic channels. The results provided are intuitive and prove that multiple synapses working in cooperation improve the reliability of the neuron-to-neuron communication channel. The results serve as a progressive step in the evaluation of the performance of biological neural networks and the development of artificial cells and networks.

An un-coded multi-transmitter query scheme is introduced for wireless backscatter communication systems in which M transmitters and N receivers are used for single-tag connectivity (M × 1 × N). The main idea is to harden the wireless communication channel with a tag device for high data rate readings. The proposed method is designed for multipath fading channels in which the backscatter channel is a multiplicative Rayleigh. A coherent transmit query scheme is used to increase the tag-reflected signals and simultaneously alter the fading statistics in the forward path by implementing a receiver feedback. Full-diversity performance and array gain is achieved using the receiver diversity without requiring any tag antenna diversity. Therefore, the tag device remains simple. Mathematical expressions for the probability density function (PDF) of the backscatter channel are presented using closed-form equations. Bit error rate (BER) simulations for the binary phase shift keying (BPSK) modulation are computed numerically. Diversity gain of 10 dB is obtained by using a 2 × 1 × 1 scheme. The results show that the transmit diversity for single-tag usage performs the same as the tag antenna diversity, at the expense of a moderate transmitter complexity. The tag device remains intact as a requirement for the simplicity and size constraints. Also, the system realization becomes more feasible due to the available space on the transmitter side to accomplish the uncorrelated forward channel conditions. The feasibility study is demonstrated using software-defined radio (SDR) implementations. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Wireless capsule endoscopy (WCE) is a non-invasive technology used for inspection of the gastrointestinal tract. Localization of the capsule is a vital part of the system enabling physicians to identify the position of anomalies. Due to intestinal motility, the positions of the intestines will change significantly within the abdominal cavity over time. However, the distance from one position to another within the intestines changes much less. In this paper a method for calculating the pathlength travelled by a WCE is proposed. The method is based on Kalman-and particle filters and is simulated using a model that approximately replicates the movement through the small intestine. The travelled distance was estimated to an accuracy within a few millimeters. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Classical algorithms for the multiple measurement vector (MMV) problem assume either independent columns for the solution matrix or certain models of correlation among the columns. The correlation structure in the previous MMVformulation does not capture the signals well for some applications like photoplethysmography (PPG) signal extraction where the signals are independent and linearly mixed in a certain manner. In practice, the mixtures of these signals are observed through different channels. In order to capture this structure, we decompose the solution matrix into multiplication of a sparse matrix composed of independent components, and a linear mixing matrix. We derive a new condition that guarantees a unique solution for this linear mixing MMV problem. The condition can be much less restrictive than the conditions for the typical MMV problem in previous works. We also propose two novel sparse Bayesian learning (SBL) algorithms, independent component analysis sparse Bayesian learning (ICASBL) and fast independent component sparse Bayesian learning (FASTICASBL), which capture the linear mixture structure. Analysis of the global and local minima of the ICASBL cost function is also provided, and similar to the typical SBL cost function it is shown that the local minima are sparse and that the global minima have maximum sparsity. Experimental results show that the proposed algorithms outperform traditional approaches and can recover the signal with fewer number of measurements in the linear mixing MMV setting. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Multiple measurement vector (MMV) enables joint sparse recovery which can be applied in wide range of applications. Traditional MMV algorithms assume that the solution has independent columns or correlation among the columns. This assumption is not accurate for applications like signal estimation in photoplethysmography (PPG). In this paper, we consider a structure for the solution matrix decomposed into a sparse matrix with independent columns and a square mixing matrix. Based on this structure, we find the uniqueness condition for l 1 minimization. Moreover, an algorithm is proposed that provides a new cost function based on the new structure. It is shown that the new structure increases the recovery performance especially in low number of measurements. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

This paper proposes the use of hybrid automatic repeat request (HARQ) with code combining (HARQ-CC) to offer reliable communications over double Rayleigh channels. The double Rayleigh fading channel is of particular interest to vehicleto-vehicle communication systems as well as amplify-and-forward relaying and keyhole channels. This paper studies the performance of HARQ-CC over double Rayleigh channels from an information theoretic perspective. Analytical approximations are derived for the ϵ-outage capacity, the average number of transmissions, and the throughput of HARQ-CC. Moreover, we evaluate the delay experienced by Poisson-arriving packets for HARQ-CC. We provide analytical expressions for the average waiting time, the packets sojourn time, the average consumed power, and the energy efficiency. In our investigation, we take into account the impact of imperfect feedback on different performance metrics. Additionally, we explore the tradeoff between energy efficiency and the throughput. The proposed scheme is shown to maintain the outage probability below a specified threshold ϵ, which ensures the link reliability. Meanwhile, HARQ-CC adapts implicitly the transmission rate to the channel conditions such that the throughput is maximized. Our results demonstrate that HARQ-CC allows improving the achievable communication rate compared to fixed time diversity schemes. To maximize the throughput of HARQ-CC, the rate per HARQ round should be less than the rate required to meet the outage constraint. Our investigation of the performance of HARQ-CC over Rayleigh and double Rayleigh channels shows that double Rayleigh channels have a higher severity of fading and result in a larger degradation of the throughput. Our analysis reveals that HARQ with incremental redundancy achieves a larger throughput compared to HARQ-CC, whereas HARQ-CC is simpler to implement, has a lower decoding complexity, and requires less memory resources. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned problem our study aims to improve the human detection performance by developing an automatic polyp screening framework as a decision support tool. We use a small image patch based combined feature method. Features include shape and color information and are extracted using histogram of oriented gradient and hue histogram methods. Dictionary learning based training is used to learn features and final feature vector is formed using sparse coding. For classification, we use patch image classification based on linear support vector machine and whole image thresholding. The proposed framework is evaluated using three public polyp databases. Our experimental results show that the proposed scheme successfully classified polyps and normal images with over 95% of classification accuracy, sensitivity, specificity and precision. In addition, we compare performance of the proposed scheme with conventional feature based methods and the convolutional neural network (CNN) based deep learning approach which is the state of the art technique in many image classification applications. © 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 22.8.2019 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Widely used medical imaging systems in clinics currently rely on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The aforementioned technologies provide clinical data with a variety of resolution, implementation cost, and use complexity, where some of them rely on ionizing radiation. Microwave sensing and imaging (MSI) is an alternative method based on nonionizing electromagnetic (EM) signals operating over the frequency range covering hundreds of megahertz to tens of gigahertz. The advantages of using EM signals are low health risk, low cost implementation, low operational cost, ease of use, and user friendliness. Advancements made in microelectronics, material science, and embedded systems make it possible for miniaturization and integration into portable, handheld, mobile devices with networking capability. MSI has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, cancer detection, and localization of in-body RF sources. The fundamental notion of MSI is that it exploits the tissue-dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. This paper presents a comprehensive overview of the active MSI for various medical applications, for which the motivation, challenges, possible solutions, and future directions are discussed.

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.

As the rollout of 4G mobile communication networks takes place, representatives of industry and academia have started to look into the technological developments toward the next generation (5G). Several research projects involving key international mobile network operators, infrastructure manufacturers, and academic institutions, have been launched recently to set the technological foundations of 5G. However, the architecture of future 5G systems, their performance, and mobile services to be provided have not been clearly defined. In this paper, we put forth the vision for 5G as the convergence of evolved versions of current cellular networks with other complementary radio access technologies. Therefore, 5G may not be a single radio access interface but rather a “network of networks”. Evidently, the seamless integration of a variety of air interfaces, protocols, and frequency bands, requires paradigm shifts in the way networks cooperate and complement each other to deliver data rates of several Gigabits per second with end-to-end latency of a few milliseconds. We provide an overview of the key radio technologies that will play a key role in the realization of this vision for the next generation of mobile communication networks. We also introduce some of the research challenges that need to be addressed.

Time reversal (TiR) is a pre-filtering technique that exploits the propagation channel state information in order to reduce the complexity of a user's terminal in a communication system. TiR transfers the system complexity to the base station and has been proposed for ultra wideband (UWB) communications in highly dispersive multipath environments. In TiR-UWB two complex tasks must be carried out by the base station. The first is the estimation of the channel state information of the user's terminal, and the second is the generation of the time reversed channel signal. In this paper, the use of a correlation receiver for the estimation of the multipath components (MPCs) of the propagation channel is proposed. Then, a novel binary time reversal (BTiR) method that makes use of the resolvable MPCs to simplify the transceiver tasks is introduced. When using BTiR the communication system can be regarded as a direct sequence spread spectrum (DSSS) scheme in which the spreading chips are provided by the propagation channel. The performance of the proposed BTiR scheme is assessed by using measurements of UWB spatial channels in a typical indoor environment.

In this column, we aimed to spark interest in the medical uses of UWB signals. As illustrated throughout the article, the role of the signal processing community is crucial for the improvement of existing applications and the search for novel procedures for diagnosing and treating effectively life-threatening diseases. Therefore, further research in this field is encouraged.