Molecular communication (MC) is envisioned to realize nanotheranostics as an emerging diagnostic tool to improve existing treatment modalities. Phase separation (PS) is a complex time-dependent process responsible for discrimination of two independent phases from a single homogeneous mixture. Recently, PS was revealed to be the fundamental mechanism behind formation and organization of the living cells. Inspired by PS mechanisms in nature, we establish a novel modulation scheme for MC which encodes the information in the dispersion of molecules diffusing in the environment. Hence, the diffusion distribution can be considered as carriers of molecules. To evaluate the performance of this communication scheme, a dual-carrier diffusion-division modulation (DDM) is adopted where each carrier can be effectively modeled by superposition of multiple independent phases. We derive the theoretical bit error rate (BER) of the proposed DDM scheme which is validated by particle-based simulation (PBS). Furthermore, performance of the multi-carrier DDM scheme is compared to the well-known on-off keying (OOK) modulation scheme (as the most relevant benchmark) and pulse-position modulation (PPM) scheme. It is shown that performance of the DDM-based MC system can be boosted by increasing the transmission power and/or using multiple carriers. Interestingly, the proposed DDM is a covert modulation scheme since any other receiver cannot decode the transmitted signal by just counting the number of received molecules unless having the shared key. Moreover, the DDM scheme requires a receiver that exploits the displacement distribution of the molecules inside the receiver to infer about the tranmitted bit. We strongly believe that this concept will introduce novel types of communication schemes more compatible with biological microenvironments. Also, this work establishes the foundation for more complex orthogonal multi-carrier DDM schemes which can potentially unlock novel cellular sensing mechanisms in biology.

Intercellular communication is crucial for organ function, with extracellular vesicles (EVs) acting as common messengers for almost all cells. This study proposes a novel EV-mediated intercellular communication system that uses a modulation technique regulated via altered intracellular-cytosolic calcium dynamics regulation. As a case study, intercellular communication within a cardiac muscle is considered with cardiomyocyte cells serving as transceivers. Through molecular communication theory, we propose a comprehensive model addressing EV release kinetics, propagation, degradation, and uptake. A linear time-invariant Poisson channel model is developed and closed-form expressions, verified through particle-based simulations, are derived for EV detection probabilities at the receiver. A closed-form bit error probability is derived and validated through Monte Carlo simulations. By selecting an optimal receiver threshold: 1) bit error rate (BER) in EV-mediated intercellular communication is robust against adverse effects from cardiac disorders, such as myocardial infarction; 2) transmitter design can be optimized by minimizing actuation signal amplitude and pulse width; 3) BER stays stable across heart rates for different distances, demonstrating the robustness of EV-mediated communication. This study enhances our ability to engineer precise and reliable intra-body cardiac communications, which may offer valuable applications for cardiovascular disease treatment, for example, the realization of biological lead-less multi-nodal pacemakers.

Treating brain diseases with therapeutic particles imposes significant challenges as particles are usually too large to traverse the gaps between endothelial cells in the blood-brain barrier (BBB). Focused ultrasound (FUS) for disruption of the BBB has been proposed as a remedy. However, the extent of disruption and the efficiency of the particle delivery to the regions of interest are highly dependent on FUS sonication parameters. This study investigates the effects of not only FUS sonication parameters but also the therapeutic particle admin-istration scheme by exploiting communication-theoretic channel modeling. Specifically, the particle pathways from blood vessels to hallmarked spots in the brain interstitial space are abstracted as a single-input-multiple-output (SIMO) channel. The channel outputs are then examined through the lenses of communication-theoretic measures such as channel gain, transmission efficiency, signal-to-noise ratio, and bit error ratio. The numerical results are displayed utilizing the available clinical data on six patients with brain cancer. The results show that the proposed approach could be exploited in future studies to maximize the efficacy of the treatment and minimize adverse effects.

An efficient deep learning model that can be implemented in real-time for polyp detection is crucial to reducing polyp miss-rate during screening procedures. Convolutional neural networks (CNNs) are vulnerable to small changes in the input image. A CNN-based model may miss the same polyp appearing in a series of consecutive frames and produce unsubtle detection output due to changes in camera pose, lighting condition, light reflection, etc. In this study, we attempt to tackle this problem by integrating temporal information among neighboring frames. We propose an efficient feature concatenation method for a CNN-based encoder-decoder model without adding complexity to the model. The proposed method incorporates extracted feature maps of previous frames to detect polyps in the current frame. The experimental results demonstrate that the proposed method of feature concatenation improves the overall performance of automatic polyp detection in videos. The following results are obtained on a public video dataset: sensitivity 90.94%, precision 90.53%, and specificity 92.46%.

Electrical stimulation of biological cells and tissues is an established technique to stimulate cells such as neurons and cardiomyocytes to enable the treatment of some disorders like Parkinson’s disease, cardiac arrhythmias, obstructive sleep apnea epilepsy, and depression. These devices use electronic circuits, batteries, and wires to transfer the stimulation signal to the target region. On the contrary, macro-scale devices such as scalp based bioelectrodes, surgical implants etc., require invasive surgery and constant fault monitoring. The use of standalone bio-compatible wireless micro-devices that can enable remote control and monitoring, powering and stimulation of cells and tissues and, deliver the stimulation therapy without additional circuits and battery, can be a significant advantage. In this paper, we introduce the concept of using magnetoelectric (ME) material composition to generate controllable electrical stimulation patterns for the Central Nervous System (CNS) stimulation therapy. We propose the potential use of ME structures in multi-modal resonant frequencies, for active stimulation. A spherical ME coreshell microdevice is designed and the Multiphysics numerical computations are used to evaluate the strain induced voltage on the device by using a remote magnetic bias and alternating magnetic field. It is shown that using the ME device in the resultant strain mode can create a sufficient voltage gradient that can potentially be used for wireless stimulation.

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.

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.

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%.

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.

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.

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.

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.

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 the 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 paper, 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 (BMIs). 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. Then, we discuss the technologies for brain implants and propose future directions for the design and fabrication of cognitive BMIs. The overarching goal of this paper is to summarize the status of engineering research at the interface between the technology and the nervous system and direct the ongoing research toward the point where synaptically interactive BMIs can be embedded in the nervous system.

In this paper, we use dual-hop relaying to overcome the signal blockage problem that occurs for millimeter waves (mmWaves) due to obstacles located in the propagation environment. Using device-to-device communication, a device in the neighborhood of the transmitter and the receiver can play the role of a relay by amplifying the signal from the source device and forwarding it to the destination device. We consider that both the relay and the destination devices are subject to interference. We study the performance of this mmWave network and derive an exact and asymptotic expressions for the bit error probability (BEP). The exact BEP expression is validated by Monte Carlo simulations. The asymptotic BEP allows determining the diversity order and the coding gain of the communication system. Additionally, we investigate the power allocation optimization subject to a power constraint and derive an analytical expression for the optimal power. Numerical results illustrate the gain achieved in terms of BEP thanks to optimal power allocation.

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

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.

In this paper we address the problem of Software Defined Networking (SDN) controller placement in large networks. Indeed, to solve the scalability issue raised by the centralized architecture of SDN, multi-controllers deployment (or distributed controllers system) is envisioned. However, the number and the location of controllers in large networks remain an issue. In this context, several works have been proposed to find the optimal placement of SDN controllers. Most of them consider latency among SDN controllers and switches as the main metric. In this work, we go beyond the state of art by proposing a solution that considers at the same time three critical objectives for the optimal placement of controllers: (i) the latency and communication overhead between switches and controllers; (ii)the latency and communication overhead between controllers; (iii) the guarantee of load balancing between controllers. We then solve the system by using Bargaining Game in order to find a fair trade off between these objectives. Simulation results clearly demonstrate the effectiveness of the proposed solution in finding the optimal placement of controllers that enforces this trade-off.

Ultra wideband (UWB) signals possess characteristics that may enable high data rate communications with deeply implanted medical sensors and actuators. Nevertheless, this application could be hindered in part by international spectrum regulations, which restrict UWB communications to 3.1-10.6 GHz where propagation conditions through the human body are rather unfavorable. Therefore, for the proper feasibility assessment and design of implant communications using UWB signals, accurate models of the radio channel are of utmost importance. Hence, we present UWB path loss models for the two most commonly used implant communication scenarios, i.e., in-body to on-body (IB2OB) and in-body to off-body (IB2OFF). These models were extracted from in vivo measurements in the abdominal cavity within 3.1-8.5 GHz using a living porcine subject. A thorough comparison between this modeling approach and channel measurements using a homogeneous phantom, which mimics the electromagnetic behaviour of muscle tissue, is presented too. Measurements in a homogeneous propagation medium are simpler to perform, but they fail to capture several physiological effects observed in a living subject. Thus, we measured the deviation between the phantom-based and in-vivo-based path loss models. In general, phantom measurements yielded a more pessimistic estimation of the path loss. We provide the correction factors to adjust easy-to-perform phantom-based measurements to more realistic path loss values, which can assist the biomedical engineer in the early stages of design and testing of wireless implantable devices.

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

Recent studies foresee that there would be roughly 50 billion of machine type communication (MTC) devices by 2020. Coping with the massive signaling overhead expected from these devices in 5G network is an important hurdle to tackle. In this paper, we have proposed two optimal solutions that use Device-to-Device (D2D) communications to lightweight the overhead of MTC devices on 5G network. Each scheme has a specific objective, and aims to manage the communications between devices and eNodeBs to achieve its objective. The proposed solutions nominate the devices that should communicate through D2D communication fashion and those that should directly communicate with eNodeBs. The first solution aims to reduce the energy consumption, whereas the second one aims to reduce the data transfer delay at the eNodeBs. The performance of the proposed schemes is evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals.

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.

Energy scavenging from ambient sources represents a promising solution for sustaining continual operation for wireless sensor networks. An energy harvesting wireless sensor network (EH-WSN) can have two node types, harvesting enabled nodes (HNs) and non-harvesting enabled nodes (NHNs). In this paper, we consider the problem of achieving network longevity in EH-WSN when in-network data aggregation is used. The aim is to construct an aggregation tree that extends the network lifetime through reducing the overhead on NHN as much as possible. Two solutions are proposed; the first model the problem using a integer linear program, whereas the second solution uses minimum directed spanning tree. The objective of these protocols is to extend the network lifetime while reducing the runtime complexity. Both solutions are evaluated through extensive simulation experiments. The obtained results demonstrate their feasibility and ability in achieving the design goals.

In-network data aggregation is one of the popular optimization methodologies in the realm of wireless sensor networks (WSNs). To enable effective implementation, a routing tree is formed and the node transmissions are carefully scheduled to meet flow constraints. Minimizing the data delivery latency has been the most common objective of the data aggregation scheduling optimization. Prior work on this optimization problem pursued heuristics to overcome the complexity of the problem and used an upper bound on latency as a metric to assess the quality of the solution. In this paper we argue that for small and medium sized networks it is computationally feasible to obtain the optimal solution. We formulate the data aggregation scheduling problem as a linear integer program. Two variants of the problem are considered. The first assumes that the routing tree is predefined, e.g., through a network layer protocol, and node transmissions are to be scheduled to minimize delay. For the second variant, the routing topology formation and node schedule are to be optimized in an integrated manner. The proposed solutions are compared to the existing heuristics via extensive simulation experiments.

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.

Colon cancer and precancerous colon lesions are major health problems. The most frequent precancerous cancer lesions are polyps characterized by abnormal tissue growth in the human bowel. There are also anomalies such as inflammation and bleeding area. Colon capsule endoscopy (CCE) is a recent promising technology which enables to obtain videos of the inside of the intestine via an on board digital camera. The small capsule ingested by the patient is recuperated after it passes through the whole gastrointestinal track. Expert gastroenterologists analyze the video sequence visually in order to find out frames containing abnormalities related to cancer. This process is labor intensive and time consuming. In this paper we propose an algorithm that provides automatic video analysis in order to classify tissue regions in images into two categories: normal and abnormal. In order to achieve high correct classification rates, we first pre-process the image data by removing the noise in it and by normalizing the intensity values of the image pixels. Next, we use the well-known SIFT descriptor algorithm with Bag of Feature (BoF) approach for feature extraction. For training, Support Vector Machine (SVM) is trained on the extracted features using the training dataset. Finally a testing data set is used to assess the performance of the proposed algorithm. The early experimental results are very encouraging and show high correct classification rates, reaching up to 98.25% for images with polyps. The novelty of the proposed algorithm is the combination of using specific SIFT features with Bof and the vignetting correction to capture the local characteristics of the abnormalities in capsule video endoscopy images.

In this paper, we present a novel application for elucidating all kind of videos that require expert knowledge, e.g., sport videos, medical videos etc., focusing on endoscopic surgery and video capsule endoscopy. In the medical domain, the knowledge of experts for tagging and interpretation of videos is of high value. As a result of the stressful working environment of medical doctors, they often simply do not have time for extensive annotations. We therefore present a semi-supervised method to gather the annotations in a very easy and time saving way for the experts and we show how this information can be used later on.

The unprecedented cellular traffic growth caused by the ubiquitous mobile Internet access has motivated the search for solutions to cope with this explosive demand of connectivity. Mobile traffic offloading through Wi-Fi networks has emerged as an effective way of supplementing cellular services. This approach has been proven very effective to reduce the load of delay-tolerant traffic in cellular networks. Recent developments suggest that real-time traffic like VoIP could be also offloaded if Wi-Fi networks supported a reasonable number of concurrent calls. In this way, ubiquitous IEEE 802.11 networks operating in the 2.4 GHz ISM band could complement the cellular systems in indoor and rural environments. Wi-Fi networks are allocated 14 channels in this frequency band, thereof only 3 non-overlapped channels are allowed for Wi-Fi extended service sets in order to avoid interference. In this paper we investigate the use of assignment configurations with 4 partially-overlapped channels for VoIP transmissions, which may increase the network capacity in terms of concurrent users supported by Wi-Fi networks. Through computer simulations we demonstrate that 4-channel configurations are feasible at the expense of a tolerable degradation in quality of service for voice traffic with pedestrian mobility.

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.

Biomedical implantable sensors transmitting a variety of physiological signals have been proven very useful in the management of chronic diseases. Currently, the vast majority of these in-body wireless sensors communicate in frequencies below 1 GHz. Although the radio propagation losses through biological tissues may be lower in such frequencies, e.g., the medical implant communication services band of 402 to 405 MHz, the maximal channel bandwidths allowed therein constrain the implantable devices to low data rate transmissions. Novel and more sophisticated wireless in-body sensors and actuators may require higher data rate communication interfaces. Therefore, the radio spectrum above 1 GHz for the use of wearable medical sensing applications should be considered for in-body applications too. Wider channel bandwidths and smaller antenna sizes may be obtained in frequency bands above 1 GHz at the expense of larger propagation losses. Therefore, in this paper, we present a phantom-based radio propagation study for the frequency bands of 2360 to 2400 MHz, which has been set aside for wearable body area network nodes, and the industrial, scientific, medical band of 2400 to 2483.5 MHz. Three different channel scenarios were considered for the propagation measurements: in-body to in-body, in-body to on-body, and in-body to off-body. We provide for the first time path loss formulas for all these cases.

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.

THIS special issue includes seven interesting articles that tackle some of the existing challenges and design issues related to the use of RF and Communication Technologies for wireless implants. An in-body medical device is a miniature device that is inserted in the human body to collect physiological signals and images or to act like a prosthetic device restoring certain body functions. In order for these devices to communicate with the external world, radio frequency (RF) wireless technologies are often used to maintain their operation for a longer period of time. This is achieved by integrating wireless communication technologies within these devices. In some situations, a body worn device is used to receive the medical information from the devices in the human body and then transmit to remote stations for remote medical monitoring.

Body area network (BAN) technology can enable the real-time collection and monitoring of physiological signals for personalized healthcare. Implantable biomedical sensors transmitting continuously clinical information to an external unit can facilitate the involvement of the patients in the management of chronic diseases. In addition, ingestible sensors like the wireless capsule endoscope (WCE) have been proven extremely useful as clinical diagnostic tools. It is envisaged that such devices will evolve to also perform in-body therapeutic procedures. Future medical applications may require the interconnection of two or more of these in-body devices to interchange information for better diagnostics or to relay data from deeply implanted sensors. In this context, ultra wideband (UWB) radio links can be used for the communication interfaces of in-body sensors due to their large bandwidth and low power consumption. Nevertheless, little is known about the behavior of the in-body to in-body (IB2IB) radio channel in the UWB spectrum. This paper aims to fill this gap by providing insight into the behavior of the IB2IB channel based on propagation measurements in 3.1-8.5 GHz. Because of the impossibility to conduct in-body measurements with human subjects, we used a phantom that emulated the dielectric characteristics of the human muscle tissue. The path loss as a function of the distance between antennas and the frequency are thoroughly discussed.

Field trials have been performed in Covilhã to identify the spectrum opportunities for radio frequency (RF) energy harvesting through power density measurements from 350 MHz to 3 GHz. Based on the identification of the most promising opportunities, a dual-band printed antenna was conceived, operating at GSM bands (900/1800), with gains of 1.8 and 2.06 dBi, and efficiency varying from 77.6 to 82%, for the highest and lowest operating frequency bands, respectively. In this paper, guidelines for the design of RF energy harvesting circuits and choice of textile materials for a wearable antenna are briefly discussed. Besides, we address the development and experimental characterization of three different prototypes of a five-stage Dickson voltage multiplier (with and without impedance matching circuit) responsible for RF energy harvesting. All the three prototypes (1, 2 and 3) can power supply the sensor node for RF received powers of 2 dBm, -3 dBm and -4 dBm, and conversion efficiencies of 6, 18 and 20%, respectively.

In this paper, we investigate a market equilibrium in multichannel sharing cognitive radio networks (CRNs): it is assumed that every subchannel is orthogonally licensed to a single primary user (PU), and can be shared with multiple secondary users (SUs). We model this sharing as a spectrum market where PUs offer SUs their subchannels with limiting the interference from SUs; the SUs purchase the right to transmit over the subchannels while observing the inference limits set by the PUs and their budget constraints. Moreover, we consider each SU limits the total interference that can be invoked from all other SUs, and assume that every transmitting SU marks the interference charges to other transmitting SUs. The utility function of SU is defined as least achievable transmission rate, and that of PU is given by the net profit. We define a market equilibrium in the context of extended Fisher model, and show that the equilibrium is yielded by solving an optimization problem, Eisenberg-Gale convex program. To make the solutions of the convex program meet the market equilibrium, we apply monotone-transformation to the utility function of each SU. Furthermore, we develop a distributed algorithm that yields the stationary solutions asymptotically equivalent to the solutions given by the convex program.

In this paper, we identify the spectrum opportunities for radio frequency (RF) energy harvesting through power density measurements from 350 MHz to 3 GHz. The field trials have been performed in Covilhâ by using the NAKDA-SMR spectrum analyser with a measuring antenna. Based on the identification of the most promising opportunities, a dual-band band printed antenna operating at GSM bands (900/1800) is proposed, with gains of the order 1.8-2.06 dBi and efficiency 77.6-84%. Guidelines for the design of RF energy harvesting circuits and choice of textile materials for a wearable antenna are also discussed. Besides, we address the guidelines for designing circuits to harvest energy in a scenario where a wireless body area network (WBAN) is being sustained by a TX91501 Powercasf®RF dedicated transmitter and a five-stage Dickson voltage multiplier responsible for harvesting the RF energy. The IRIS motes, considered for our WBAN scenario, can perpetually operate if the RF received power attains at least -10 dBm.

The recent advancements in nanotechnology have been instrumental in initiating research and development of intelligent nanomachines, in a variety of different application domains including healthcare. The stimulation of the cerebral cortex to assist the treatment of brain diseases have been investigated with growing interest in the past, where nanotechnology offers a dramatic breakthrough. In this paper, we discuss the feasibility of a nanomachine-to-neuron interface to design a nanoscale stimulator device called synaptic nanomachine (SnM), compatible with the neuronal communication paradigm. An equivalent neuron-nanomachine model (EqNN) is proposed to describe the behavior of neurons excited by a network of SnMs. Sample populations of neurons are simulated under different stimulation scenarios. The assessment of the existing correlation between SnM stimulus and response, as well as between neurons and clusters of neurons, has been performed using statistical methods. The obtained results reveal that a controlled nanoscale stimulation induces apparently an oscillatory behavior in the neuronal activity and localized synchronization between neurons. Both effects are expected to have the basis of important cognitive and behavioral functions such as learning and brain plasticity.

ABSTRACT Relay selection schemes for cooperative communications to achieve full cooperative diversity gains while maintaining spectral and energy efficiency have been extensively studied in a recent research. These schemes select only the best relay from multiple relaying candidates to cooperate with a communication link. In the present paper, we reviewed recently proposed cooperative communication protocols that integrate with relay selection mechanisms. The key design issues for relay selection mechanisms, for example, relaying candidate selection, optimal relay assignment, and cooperative transmission, were identified. We further discussed the challenges of optimal relay assignment in multi‐hop wireless sensor networks and presented the potential applications of cooperative communications with a relay selection in such networks. Future research directions were outlined, for example, the issues of service differentiation and system fairness in cooperative communication systems and the joint use of game theory and adaptive learning techniques in relaying candidate selection and optimal‐relay assignment mechanisms for efficient allocation of network resources. Copyright © 2011 John Wiley & Sons, Ltd.

The human neural system is comprised of millions of neural cells which are fully networked through synaptic connections. Each neuron receives the input through dendrites from neighboring nodes, in order to fire an action potential through its axon into the next neurons. In this paper, a biological communication network is modeled and a scenario is implemented to detect the damaged neural cells. For this purpose, the membrane potential of the biological network nodes is monitored and evaluated using spiking neural network algorithm. A spiking Izhikevich neural modeling technique is implemented at nanoscale to model the biological network. A swarm of nanobots is taken into consideration to diagnose the malfunction of the biological communication network by measuring the membrane potential of each firing neuron and the neighboring nodes. After fault occurrence, some nodes will no longer be available to process and communicate in the biological communication network. Therefore, the fully-connected biological network as a small-world network would be a randomly-connected communication network. The idea of this research is to diagnose the defected nano-cells and autonomously cluster to regenerate a small-world network using the available neighboring neural cells. To reestablish the small-world biological communication network, a graph theory scheme is applied considering the membrane potentials and coordination of the neural cells in a two dimensional biological network. The depth-first search algorithm is implemented for clustering and the simulation results are illustrated and discussed.

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.

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

A communication system based on the joint source-channel coding principle is proposed where a fixed-rate source encoder using neither a codebook nor an entropy encoder is exploited to avoid the error propagation effect and thus gain in system robustness. An explicit expression of the mean square error (MSE) distortion of the system for a uniform source is derived. Based on the MSE distortion, the optimal design of the communication system under the total transmission rate constraint is formulated as a mixed integer nonlinear optimization problem. We provide an algorithm to achieve the optimal solution via a convex optimization solver. The numerical result shows that the overall performance of the proposed system is close to the performance of the entropy coding scalar quantizer (ECSQ) system established by A. Gyorgy and T. Linder [1] for almost all rate regions.

A communication system based on the joint source-channel coding principle is proposed, where a fixed-rate source encoder is used to avoid the error propagation effect as experienced when variable length coding is used and thus gain system robustness. An explicit expression of the mean square error (MSE) distortion of the system for a uniform source is derived. We provide an algorithm to achieve the optimal solution by investigating the Karush–Kuhn–Tucker (KKT) condition. The numerical results show that the overall performance of the proposed system is close to the performance of the entropy coded scalar quantiser (ECSQ) system established by A. György and T. Under for almost all rate region. A 0.176-bits-per-sample performance loss is found for the worst case by comparing our considered system and ECSQ system. An observation resembling the finding by V. Goya1 et al. is that in the low rate region, there exist certain regimes in which the proposed system outperforms the ECSQ system. Finally, the proposed system outperforms the equal rate system with a large margin.

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.

Wireless sensor networks consist of resource constrained nodes, especially with respect to power resources. In many cases, the replacement of a dead node is difficult and costly, e.g. an implanted node in the human body. Our main goal in this paper is reducing the total power consumption of the network. For this purpose, we consider the cooperation of nodes in data transmission in terms of a group, since the major consumer of power is the data transmission process. A mobile node may move to a new location, in which it is desirable for the node to join a group. In this paper, we propose an algorithm for nodes to choose the best group in their signal range, using coalitional game theory to determine what is beneficial in terms of power consumption. The protocol is formalized in rewriting logic, implemented in the Maude tool, and validated by means of Maude's model exploration facilities. Simulation-based tools are in general not able to prove the protocol. However, by using Maude, we prove the correctness of our proposed protocol, by searching for failures of the protocol, through all possible behaviors of sensors. These searches prove that grouping nodes is done correctly in all reachable states from a set of initial states of the model. In addition, we simulate our model in order to quantitatively analyze the efficiency of the proposed protocol. The results show significant improvements in power efficiency.

We model cognitive radio networks (CRNs) as a spectrum market where every primary user (PU) offer her subchannels with certain interference bound indicating the interference limit the PU can tolerate, and secondary users (SUs) purchase the right to access the subchannels while observing their budget constraints as well as the inference bound. In this spectrum market model, the utility of SU is defined as the achievable transmission rate in free space, and the utility of PU is given by the net profit the PU can make. Then we develop a market equilibrium in the context of Fisher model, and show that the equilibrium is obtained by solving an optimization problem called Eisenberg-Gale convex program. Furthermore, we develop a distributed algorithm with best response dynamics and price dynamics, and prove that its asymptotic solutions are equivalent to the solutions given by the convex program. Besides, we introduce adaptive step size to the price dynamics for faster convergence. With some numerical examples, we show that it helps to achieve faster convergence.

A new localized quality of service (QoS) routing protocol for wireless sensor networks (WSN) is proposed in this paper. The proposed protocol targets WSN's applications having different types of data traffic. It is based on differentiating QoS requirements according to the data type, which enables to provide several and customized QoS metrics for each traffic category. With each packet, the protocol attempts to fulfill the required data-related QoS metric(s) while considering power efficiency. It is modular and uses geographical information, which eliminates the need of propagating routing information. For link quality estimation, the protocol employs distributed, memory and computation efficient mechanisms. It uses a multisink single-path approach to increase reliability. To our knowledge, this protocol is the first that makes use of the diversity in data traffic while considering latency, reliability, residual energy in sensor nodes, and transmission power between nodes to cast QoS metrics as a multiobjective problem. The proposed protocol can operate with any medium access control (MAC) protocol, provided that it employs an acknowledgment (ACK) mechanism. Extensive simulation study with scenarios of 900 nodes shows the proposed protocol outperforms all comparable state-of-the-art QoS and localized routing protocols. Moreover, the protocol has been implemented on sensor motes and tested in a sensor network testbed.

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.

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

Impulse radio (IR) ultra wideband (UWB) technology is assessed herein for wireless data communications with a capsule endoscope operating inside the digestive tract. The UWB channel is characterised for the frequency range of 1–5 GHz and line-of-sight (LOS) scenarios. Owing to the lack of a standardised mathematical model for in-body UWB signals, the channel characterisation is attained by using numerical simulations. Because of the lossy material properties of the human tissues, short delay spread of the in-body channel is observed. The design of a packet based IR-UWB transmitter is presented herein, considering the restrictions on power consumption, size, cost and complexity. A novel coherent receiver using a single-branch correlation scheme is proposed. The receiver system performance is optimised by adjusting the shape and the delay of the template pulse for providing maximum output of the correlator. Its bit-error-rate (BER) performance using bi-phase pulse amplitude modulation (BPAM) is evaluated. The effects of different templates on the system performance are also investigated. Fast synchronisation is achieved by using a bank of correlators; the number of correlator branches and the preamble length for successful synchronisation are estimated. This investigation reveals the feasibility of using IR-UWB for capsule endoscope fast data transmission.

In this paper we deal with biomedical applications of wireless sensor networks, and propose a new quality of service (QoS) routing protocol. The protocol design relies on tra±c diversity of these applications and en- sures a di®erentiation routing using QoS metrics. It is based on modular and scalab

Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of adaptivity, reliability and network throughput. In this paper, we investigate the use of cooperative communications with adaptive relay selection for resource-constrained wireless sensor networks, and propose QoS-RSCC, a QoS-support multi-agent reinforcement learning based relay selection scheme for cooperative communications. In QoS-RSCC, optimal relays, in terms of outage probability and channel efficiency, are selected distributedly from multiple relaying candidates for the intermediate routers along the multi-hop route, without the needs of prior knowledge of the wireless network model and centralized control. We compare the network performance of QoS-RSCC with CRP, and investigate the impacts of network traffic load, channel bit error rate, and node's mobility on the network performance. Simulation results show that QoS-RSCC can achieve a near-optimal performance on both diversity gains and channel efficiency, and fits well in dynamic environments.

In this paper, we consider the problem of measurement allocation in a spatially correlated sensor field. Our objective is to determine the probability of each sensor's being measured for improved observability; the sensor located at less correlated area should be assigned more probability. To this end, we quantify the level of correlation of each sensor through the mutual information criterion reflecting the level of uncertainty about unattended locations. Then we deploy the Shapley value, a representative single-valued solution concept in cooperative game theory. The Shapley value expresses the average marginal contribution of each sensor to the observation of a spatially correlated sensor field, and can be used to allocate the probability of each sensor's being measured in proportion to its contribution. Against the intractability in computing the true Shapley value, we deploy a randomized methods based on sampling, which can compute the approximate Shapley value with linear time complexity. Through numerical experiments, we evaluate the approximate Shapley value achieved by the randomized method by comparing it to the exact Shapley value, and estimate how the measurement allocation based on the Shapley value contributes to the overall observability and coverage.

Nowadays, it is common that people carry several kinds of portable devices such as cellular phone, MP3 player, and PDA etc. If we assume that these devices build a WPAN (Wireless Personal Area Network) via Bluetooth Piconet, the master node of the Piconet plays a role of MR (Mobile Router) with maintaining connection to Internet via WLAN (Wireless LAN). Thus WPAN with Bluetooth is quite suitable for realizing NEMO (Network Mobility). Furthermore, if it is assumed that two or more Piconets are connected with one another through Scatter-net, the nested mobility is achieved easily. In this situation, each slave device will connect to Internet via the mobile router. Then the mobile router can use the path via WLAN or make a new path through Scatternet, i.e., via a master node in other Piconet, in order to forward the connection of each slave device. In this paper, we show that the path through Scatternet is more energy efficient, especially in terms of the lifetime of mobile routers. For this purpose, we present an energy consumption model of WLAN and Bluetooth, which is based on payload size and transmission/receiving duration. In addition, we show that making a new path through Scatternet built on two or more Pi-conets is more beneficial to prolonging the lifetime. We prove the energy efficiency of our proposal through ns-2 simulations.

This paper proposes a multi-objective quality of service (QoS) routing protocol for wireless sensor networks (WSN). The protocol takes into account the traffic diversity typical for many applications and provides a differentiation in routing using QoS metrics. It ensures several QoS metrics for different traffic categories, and attempts for each packet to fulfill the required metrics in a power-aware and localized way. It employs memory and computation efficient estimators in a distributed manner and uses a multi-sink single-path approach to increase reliability. The main contribution of this paper is data traffic based QoS with regard to all the considered QoS metrics. As far as we know, this protocol is the first that makes use of the diversity in the data traffic while considering latency, reliability, residual energy in the sensor nodes, and transmission power between nodes and casts QoS metrics as a multi-objective problem. The proposed algorithm can operate with any MAC protocol, provided that it employs an ACK mechanism. Simulation results show the proposed protocol outperforms all compared state-of-the-art QoS and localized routing protocols.

Ultra wideband (UWB) channel modeling from implanted antenna deep inside of a human body to receiving antennas on-body and outside body is considered for biomedical applications. Distance dependence of the channel path loss is modeled by simulating an implantable UWB antenna deep inside the chest of a human body. Time domain electromagnetic (EM) simulation using the anatomy model of a human body assuming frequency dependent tissue properties is conducted. It is shown that the energy coupling due to the non-radiative near-field of the implanted antenna becomes dominant for the signal transmission, where the link quality can be improved significantly by exploiting the near-field coupling. The effect of the implanted antenna polarization for UWB signal transmission is studied and shown that better link quality is obtained by utilizing the antenna polarization along the width of the body. Furthermore, we show that the distance dependent path loss for inside body can be modeled by a power function but it can only be modeled with a known logarithmic function for outside body.

To prolong the lifetime of nodes in wireless sensor networks, both transmission cost and processing power should be minimized. For this purpose, we propose a new direct source channel mapping method which utilizes large bandwidth. The system combines a closed-loop Differential Pulse Code Modulation (DPCM) source coder followed by pulse position modulation (PPM) transmission. To accommodate closed-loop prediction over a noisy channel an additional feedback channel is used. Two or more sequential samples are predicted at a time and combined before transmission, thus reducing transmission cost further. Simulations performed on both an auto regressive AR(1) source and a test image over additive white Gaussian noise (AWGN) channel show that our proposed system can achieve significant energy saving for both noiseless and noisy feedback channels without delay.

This paper considers the centralized spectrum allocations in resource-constrained wireless sensor networks with the following goals: (1) allocate spectrum as fairly as possible, (2) utilize spectrum resource maximally, (3) reflect the priority among sensor data, and (4) reduce spectrum handoff. The problem is formulated into a multi-objective problem, where we propose a new approach to solve it using modified game theory (MGT). In addition, cooperative game theory is adopted to obtain approximated solutions for MGT in reasonable time. The results obtained from numerical experiments show that the proposed algorithm allocates spectrum bands fairly with well observing each sensor's priority and nearly minimal spectrum handoffs.

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

There is an increasing demand to discuss diagnostic images and reports of difficult cases with experienced staff. A possible solution besides physically transporting patients and material is to use high-speed communication networks to transfer images and reports electronically. With the web application PACSflow we have developed a solution to transfer images, reports, and messages as a single package in a one-step procedure. The PACSflow is an interoperable and standard compliant web-based application, which gives clinicians a user-friendly interface for their work on a daily basis. The solution assumes that the diagnostic images are compatible with the digital imaging and communications in medicine (DICOM) format. The Department of Cardiology at the Rikshospitalet University Hospital in Oslo, Norway, and the Department of Internal Medicine at the Sørlandet Sykehus in Arendal, Norway, are making clinical use of the system. Initial tests indicate that use of PACSflow has reduced the time required to prepare and transfer data by a factor of 3.

A novel way of constructing integer coefficient 2-channel filter banks is proposed. A set of relationships among the filter coefficients is established in order to satisfy linear phase, perfect reconstruction, and FIR properties. The remaining degrees of freedom are used to obtain integer coefficient values by maximizing a performance evaluation function, namely the subband coding gain. The number of bits required to represent the subband samples is kept low through efficient nonlinear implementation techniques. An octave-band frequency partitioning where the number of stages is determined according to the image size is employed. The subband samples are then classified into one out of a finite number of classes, and each class is coded by an arithmetic coder. The obtained compression ratios are encouraging compared to the "best" results reported so far in the literature.

The performance of several decorrelating transforms is evaluated in the JPEG-2000 baseline system. The transforms considered are based on uniform parallel 2-channel and 8-channel filter banks and discrete cosine transform (DCT) building blocks. Through the analysis of lossy compression results, a system employing a combination of 8- channel and 2-channel filter banks is found to perform best overall. Also, the performance of several reversible transforms is evaluated in the same coding framework. Based on lossy and lossless compression results, a reversible version of the Cohen-Daubechies-Feauveau (2,2) wavelet transform was found to be most effective.

The performance of subband image coders depends on proper choice of filter banks. Although odd length filters in the filter banks produce waveform type artifacts, this can be alleviated by enforcing a smooth interpolation property to the synthesis lowpass filter. Evaluation of even, odd, and combinations of even and odd length filters in tree-structured filter banks, where the filter coefficients are obtained by optimizing for coding gain at each stage, is done for image coding purposes. Favorable results are obtained when a combination of odd and even length filters are used.

The performance of subband image coders depends on the proper choice of frequency partitioning in the subband domain. It is therefore desirable to have an adaptive nonuniform filter bank to cope with the frequency characteristics of the image at hand. However, a good choice, if a nonadaptive structure is to be used, is combinations of uniform parallel and octave-band tree-structured filter banks. Uniform parallel (8-channel, 32-tap), and 3-stage and 6-stage octave-band tree-structured systems are compared using a fixed rate coder for image coding. Favorable results are obtained using the combination of a parallel filter bank and a tree-structured system. The coding results are comparable to the "best" results reported in the literature.

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

By employing nonuniform nonunitary filter bank in an image coder increased performance in terms of both theoretical and practical (PSNR) coding gains, and subjective quality can be obtained. One possible candidate is octave-band tree-structured filter banks. An optimized perfect reconstruction octave-band three-stage tree-structured filter bank is proposed. The filter coefficients are optimized at each stage for subband coding gain. The performance of the proposed filter bank outperforms both traditional octave-band three-stage tree-structured filter banks and parallel filter banks in terms of both PSNR and visual quality.