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Objective: Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data. Methods: We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names. Results: Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research. Conclusion: This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.

This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed approach consists of two networks: a classifier- and a survival- network. The classifier attempts to extract features from 3D CT scans to predict the ISUP grade of Renal cell carcinoma (RCC) tumors, as defined by the International Society of Urological Pathology (ISUP). Our classifier is a 3D convolutional neural network to avoid losing crucial information on the interconnection of slides in 3D images. We employ multiple procedures, including image augmentation, preprocessing, and concatenation, to improve the performance of the classifier. Given the strong correlation between ISUP grading and renal cancer prognosis in the clinical context, we use the ISUP grading features extracted by the classifier as the input to the survival network. By leveraging this clinical association and the classifier network, we are able to model our survival analysis using a simple DL-based network. We adopt a discrete LogisticHazard-based loss to extract intrinsic survival characteristics of RCC tumors from CT images. This allows us to build a completely parametric survival model that varies with patients' tumor characteristics and predicts non-proportional survival probability curves for different patients. Our results demonstrated that the proposed method could predict the future course of renal cancer with reasonable accuracy from the CT scans. The proposed method obtained an average concordance index of 0.72, an integrated Brier score of 0.15, and an area under the curve value of 0.71 on the test cohorts.

Wireless capsule endoscopy is a fast-growing technology in healthcare systems. Due to using battery for powering the camera, light source, wireless communication, and other electronics, it has substantial limitations with the image quality, frame rate, and operating time. In this work, we propose a wireless passive video transmission system for capsule endoscopy, in which the power consumption is reduced by using analog camera sensor, and implementing an innovative radar technique for remote reading of the analog video signal using radio frequency backscattering. The power consumption of the capsule communication system tends to zero. The communication electronics system is minimized to a single Varactor diode with appropriate matching circuits and the image sensor power consumption is reduced by eliminating the camera sensor’s analog to digital converter. With these improvements the capsule system can operate for a longer period of time which enables the feasibility of continuous video streaming during the gastric tract screening. The design feasibility is demonstrated in a phantom experiment, and validated in an animal experiment for depths 6–11 cm using a bi-static radar system at 400 MHz, implemented using software defined radio platform.

The myelin sheath, as an insulation layer in nerve cells, plays an essential role in neural communication and signal conduction. Loss of myelin, referred to as demyelination, is associated with many mental disorders. Detecting the tiny demyelinated parts in nerve fibers can provide early diagnosis of some mental disorders and create effective treatment plans. This paper establishes a new engineering approach for differentiation between demyelinated and myelinated axons by analyzing spectral responses resulting from the optical simulation framework. We propose computational modeling on the photonic communication of nerve fibers and develop a graphene-based neurophotonic device that can be used to detect the regions demyelinated on the nerve fiber. We first model a nanoscale thin-film configuration of the multilayered myelinated axon to evaluate photons transmission in the nerve fibers under geometric defects as demyelination. Then, the nerve’s optical characteristics are achieved by focusing on the reflectance of light incidence on the nerve model with the change of the demyelination size to distinguish demyelinated—from myelinated nerves by the spectral contrast. Undertaking the different levels of demyelination progression, we theoretically explore the variations of effective refractive index using an analytical solution technique. Ultimately, we design a nanostructure configured with silicon dioxide, graphene, and gold nanoparticles to function as a biochip recognizing myelinated axon damage under the surface plasmon effect. This device can promote a practical procedure to distinguish nanoscale demyelinated and myelinated axons, which can be utilized for neural sensing of tiny brain tissues as a neurophotonic needle.

Electromagnetic radiation can be produced using functional materials such as magnetoelectric (ME) composites, in which the magnetoelasticity and piezoelasticity of material are involved. The mechanical nature of the vibrations is used to miniaturize the ME antenna to micro-scale size. The antenna performance evaluation requires a multiphysics analysis of the structure. An ME antenna design and simulation is detailed using the finite element method (FEM) in COMSOL Multiphysics® in which the structural mechanics, electrostatics, and magnetic field physics are coupled together to address the simulation needs. An antenna size of 250 $\mu \text{m}\,\,\times $ 50 $\mu \text{m}\,\,\times \,\,1\,\,\mu \text{m}$ is simulated within a static magnetic bias field of 20 mT. The nonlinear isotropic model is used for magnetostrictive material definition in which the prestress is defined by the magnetic bias. The model is linearized for radio frequency (RF) simulations to account for the AC simulation. The antenna farfield radiation pattern and the gain are computed using finite difference time domain (FDTD) by incorporating the extracted nearfield of the ME antenna in COMSOL. The simulated antenna impedance, radiation pattern and antenna gain are compared to an equivalent micro-loop magnetic antenna. In addition, electromagnetic computations are used to evaluate the coupling performance between the ME antenna and a larger loop antenna over a distance up to 20 mm in free space and biomedical tissues to address the potential of using ME antenna in medical implants for wireless communication and wireless power transfer.

The propagation of molecules, ions and electrochemical signals in various cell types occurs through hundreds of gap junction channels. A gap junction channel is composed of hundreds of units which are further decomposed to hemichannels. From the computational modeling perspective, each gap junction channel acts as a state machine, switching between different permeability states at known rates. Both the permeabilities and the rates jointly depend on transmembrane and transjunctional voltages. To the best of our knowledge, the effects of the permeability states on the communication performance between cells remain unknown to-date. Here, we use the example of gap junctions between cardiac myocytes (cardiomyocytes) to model a gap junction channel as a finite-state channel. We then apply the Shannon’s information theory to compute the achievable information rates. Our results demonstrate the information theoretical limits of communication over cardiac gap junction channels. The proposed analysis is anticipated to address open-research problems in cardiac modeling, as well as in nano-networking where man-made miniature devices inter-connect with cardiomyocytes to enable novel groundbreaking applications in healthcare.

Electrical signaling is known as the means for inter-cellular connectivity among neural cells. However, there are some indications that optical phenomena can occur in the neuronal cells based on biochemical processes in intra-or extracellular reactions. Also, external optical signals can be used to manipulate engineered neural cells for performing key functions. Motivated by these, this paper establishes a framework on photon transmission inside nerve fiber using optical properties of the nerve tissues to study photonic signaling. We use the same framework to analyze and propose mitigation using nanoparticles for demyelinated nerve channels- a potential cause for certain brain disorders like Alzheimer’s disease. Our study assumes photons are available. The proposed framework for photonic signaling across nerve fibers uses an analytical model of a nerve fiber’s segment that consists of physical factors, including size, Ranvier node presence, and interconnected segments. The demyelination effect on the nerve channel is evaluated by numerical electromagnetic computations. On this basis, photonic transmittance of a defected nerve segment is calculated, while demyelination takes place in different locations, lengths, and depths across the nerve segment. To repair the demyelination and enhance the transmittance of the demyelinated axon, we have performed analysis on nanoparticles such as silicon and silica quantum dots, gold nanoparticles, and gold/silica core-shell nanoparticles. Our computational models show that nanoparticles’ presence improves the optical properties of nerve configuration that indicate photons can be transmitted in the axonal routes of demyelinated channels.

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 μW 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.

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.

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).

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.

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.

Multi-node leadless pacemaker system overcomes the main limitations related to lead complications of the conventional cardiac pacemaker and will thus replace them in the near future. The multiple nodes of the technology require the development of low-power, low data-rate and energy-efficient communication framework for device synchronization and bi-directional communication between them. Moreover, the nodes need to communicate with the outer peripheral devices for data telemetry, control and remote monitoring. This paper focuses on evaluation of different energy-efficient modulation schemes at 433 MHz for bi-directional communication between the nodes using homogeneous liquid phantom model of human heart and living animal experiments. In this paper, we have analyzed three simple, low-budget modulation schemes - On Off-Keying (OOK), Frequency Shift-Keying (FSK), and Gaussian Frequency Shift-Keying (GFSK). The analysis is done based on the total transmitter power required to achieve a reliable communication indicated by the minimum threshold values of bit-error rate and packet-error rate. The experiments have been conducted for three common implant communication scenarios - in-body to in-body, in-body to on-body and in-body to off-body links. For conducting the experiments, we have designed the experimental setup with electronic components and fabricated antennas. The results have shown that GFSK has the best performance among the other modulation techniques based on the total transmitter power. We also investigated higher order of the same modulation schemes - 4-FSK and 4-GFSK. The results showed that GFSK performed much better than 4-FSK and 4-GFSK. This research will be carried forward to build the entire radio frequency communication framework for the multi-node pacemaker technology.

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.

Abstract 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.

Automatic image 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 paper, we apply a recent region-based convolutional neural network (CNN) approach for the automatic detection of polyps in the 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 offline 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 than other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.

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 much 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.

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 to ensure link reliability.

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

Graphene material, due to its unique conductivity and transparency properties, is utilized extensively in designing tunable terahertz perfect absorbers. This paper proposes a framework to design a tunable terahertz perfect absorber based on fractal triangle-shaped graphene layers embedded into dielectric substrates with the potential for spectral narrowing and widening of the absorption response without the need for geometric manipulation. In this way, the absorption cross-section spectra of the suggested configurations are achieved over the absorption band. First, the defection impact on the single-layer fractal triangle-shaped graphene structure inserted in insulators of the absorber is evaluated. Then, a flexible tunability of the absorbance’s peak is indicated by controlling the Fermi energy. By stacking fractal graphene sheets as a double graphene layer configuration in both the same and cross-states positioning, it is demonstrated that the absorption characteristics can be switched at 6–8 THz with a stronger amplitude, and 16–18 THz with a lower intensity. The impact of changing the Fermi potentials of embedded graphene layers is yielded, resulting in a plasmonic resonance shift and a significant broadening of the absorption bandwidth of up to five folds. Following, the absorption spectra related to the fractal triangle-shaped structures consist of a multi-stage architecture characterized by a spectral response experiencing a multiband absorbance rate and an absorption intensity of over 8 × 106 nm2 in a five-stage perfect absorber. Ultimately, the variations of the absorbance parameter and plasmonic mode under rotating the graphene sheet are explored for single and double fractal triangle-shaped perfect configurations on the absorption band. The presented mechanism demonstrates the tunability of the absorption spectrum in terms of narrowing or broadening and switching the plasmonic resonance by configuring multi-stage structures that can employ a broad range of applications for sensory devices.

The heart consists of billions of cardiac muscle cells—cardiomyocytes—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’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.

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

This paper considers the problem of centralized spectrum allocations in wireless sensor networks towards the following goals: (1) maximizing fairness, (2) reflecting the priority among sensor data, and (3) avoiding unnecessary spectrum handoff. We cast this problem into a multiobjective mixed integer nonconvex nonlinear programming that is definitely difficult to solve at least globally without any aid of conversion or approximation. To tackle this intractability, we first convexify the original problem using arithmetic-geometric mean approximation and logarithmic change of the decision variables and then deploy weighted Chebyshev norm-based scalarization method in order to collapse the multiobjective problem into a single objective one. Finally, we apply simple rounding method in order to obtain approximate integer solutions. The results obtained from the numerical experiments show that, by adjusting the weight on each objective function, the proposed algorithm allocates spectrum bands fairly with well observing each sensor’s priority and reduced spectrum handoffs.

This paper studies the optimum signal choice for the estimation of the aortic blood pressure via aorta radius, using a monostatic radar configuration. The method involves developing the Cramér-Rao lower bound (CRLB) for a simplified model. The CRLB for model parameters are compared with simulation results using a grid-based approach for estimation. The CRLBs are within the 99% confidence intervals for all chosen parameter values. The CRLBs show an optimal region within an ellipsoid centered at 1 GHz center frequency and 1.25 GHz bandwidth with axes of 0.5 GHz and 1 GHz, respectively. Calculations show that emitted signal energy to received noise spectral density should exceed 1012 for a precision of approximately 0.1 mm for a large range of model parameters. This implies a minimum average power of 0.4 μW. These values are based on optimistic assumptions. Reflections, improved propagation model, true receiver noise, and parameter ranges should be considered in a practical implementation.

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