The Internet of Bio-Nano Things (IoBNT) integrates biosensors, nanorobots, and molecular communication, significantly extending the functionality of traditional IoT systems on a nano-scale. It holds promise for targeted drug delivery and real-time health monitoring applications. However, IoBNT faces critical challenges, including high delay, low network reliability, and congestion, primarily due to biological environments’ complex and dynamic nature. DNA emerges as an ideal information carrier for IoBNT due to its high information density, longevity, biocompatibility, and robustness against environmental interference. These properties make DNA uniquely suited for reliable and efficient communication within IoBNT, with additional functionalities in bio-sensing and DNA computing. This paper proposes AoI-OptiIoBNT, an innovative routing and packet forwarding strategy designed to optimize DNA-based information flow in IoBNT. AoI-OptiIoBNT combines an Age of Information (AoI)-driven approach with a Markov Decision Process (MDP)-based routing algorithm to mitigate delay and congestion. It incorporates a multi-retransmission strategy to enhance network reliability and introduces a Yin-Yang Coding (YYC) mechanism to reduce error rates and improve decoding accuracy. Simulation results demonstrate that AoI-OptiIoBNT substantially improves the efficiency, reliability, and overall performance of IoBNT networks. It offers a robust framework for addressing congestion, packet loss, and delay, making it a promising solution for advancing IoBNT applications.

The thriving domain of the Internet of Bio-Nano Things (IoBNT) promises revolutionary advances in biomedicine, enabling biosensing, health monitoring, and therapeutic capabilities at the cellular level. A pivotal challenge, however, lies in devising reliable, efficient communication mechanisms within this bio-nano realm. This article introduces an emerging DNA-based molecular communication (MC) system utilizing a novel track-hopper mechanism that significantly enhances precision and control in molecular cargo transport. By leveraging DNA strands for information encoding and cargo transport, our track-hopper-based MCs (THMCs) IoBNT system achieves a symbiosis of high reliability, low delay, and precise directional control, surpassing traditional diffusion and motor-based methods. Through extensive theoretical analysis and simulation of network topology’s link and node response functions, we demonstrate the system’s superior performance in network delay and reliability metrics, underpinning its potential to redefine communication within IoBNT for applications ranging from health monitoring to disease detection. Our findings illuminate a path forward in bio-nano information exchange, offering a robust framework for the next generation of IoBNT systems.

Molecular communication (MC), an innovative paradigm leveraging molecules as information carriers, is gaining traction in the biomedical field, particularly in the context of the Internet of Bio-Nano Things (IoBNT) for targeted drug delivery (TDD) systems. Specifically, the transportation of drug molecules in blood vessels is described as the propagation of information molecules, offering an MC perspective to designing and optimizing TDD processes. However, the existing MC-inspired TDD is predominantly physical-layer-centric, grappling with the pharmacokinetics (PK) and pharmacodynamics (PD) of drug molecules in complex vessel networks and the diversity of drug carrier designs. These complexities result in high-computational demands, posing a significant challenge to implementing personalized TDD strategies. This article introduces a three-layer bio-nano systems interconnection (BNSI) hierarchical model to address these challenges by reducing the computational load and enabling a more precise TDD strategy. Our model extracts parameters from the physical layer, which handles the PK and PD processes, and maps drug dynamics to data transmission at the network layer. This approach draws inspiration from traditional communication systems, where the physical layer manages the propagation of signals, and the network layer oversees packet routing and topology as the path planning in the blood vessel network. The application layer in our model incorporates a feedback mechanism based on drug concentration at the diseased site, allowing for flexible adjustments to achieve the desired therapeutic outcomes. Simulation results demonstrate that the BNSI model enhances computational efficiency without compromising the accuracy of TDD, thus demonstrating its feasibility. This work improves the scalability and flexibility of TDD systems and lays the groundwork for future TDD’s optimization and digital transformation.

This study introduces an innovative autonomous in vivo computing (AIVC) framework for population metaheuristic nanobiosensing within the Internet of Bio-Nano Things (IoBNT), redaiming to address challenges in precise autonomous tumor homing. Traditional tumor targeting struggles with variability in tumor microenvironment (TME) gradients, such as spatial pH and viscosity, which this framework addresses by leveraging swarm intelligence to enable nanoparticles (NPs) swarms to adapt to and navigate these dynamic gradients autonomously. The AIVC framework incorporates two bio-inspired behaviors: flocking for cooperative navigation in unimodal biological gradient fields (BGFs) and territoriality for decentralized optimization in multimodal BGFs. A novel swarm information entropy metric is introduced to dynamically regulate NP population density, balancing exploration and exploitation, while minimizing systemic risks such as immune clearance. This approach is integrated into the IoBNT architecture, where it scales individual NP interactions to collective intelligence for networked tumor targeting. Through extensive computational experiments, the system demonstrated a 43.56% improvement in average targeting efficiency over conventional methods in multimodal BGFs, validated through 1,000 trials with 95% confidence intervals. The frameworks robustness was further confirmed under noisy and oscillatory conditions. While these results offer transformative potential for intelligent healthcare solutions, future work should address implementation challenges, including NP swarm dynamics and control, on-chip validation, and clinical translation, to fully realize the frameworks potential in precision medicine.

We propose a new theoretical framework for quantification and sensitization of disease state (DS). This is in contrast to the traditional discrete description of disease, where the inherent characteristic of its progression is ignored, and a finite number of DSs are resulted, effectively leading the sensing process to the act of classifying. Central to the framework is a canonical fuzzy model of DS that allows for conversion of its linguistic description into a normalized numerical variable. This generates the mapping between one set with the domain of the universe of DSs and another set with the domain of the universe of disease labels (DLs). Subsequently, the framework is analyzed from the fuzzy-set-theoretic perspective by utilizing the medical expert knowledge system of disease severity, consisting of the Acute Physiology and Chronic Health Evaluation (APACHE), which provides useful insight that enables the disease diagnosis process to be designed and optimized as a soft computing problem. Built on this fuzzy analysis, we present two multimodal strategies, namely the reversibility combining (RVC) and reliability combining (RLC), to enhance the sensing performance measured through the degree of non-reversibility of the surjective mapping from DLs to sensing outputs (SOs). Finally, we utilize some numerical examples based on highly realistic synthetic medical data to elaborate on the proposed framework, which offers a new perspective for disease diagnosis by monitoring continuously the individual's disease progression.

An imaging radar is expected to provide high-quality images for interesting targets. To this end, an information-theoretic approach is used in this article to jointly optimize waveform and receive filter with desired cross-correlation properties. First, the problem formulation is achieved by maximizing the mutual information (MI), subject to constant modulus, high resolution, and low peak sidelobe ratio (PSLR) constraints. Second, to solve the resultant problem with a fractional quadratic objective function and various nonconvex constraints, four customized iterative loops are performed to transform the problem into a series of solvable subproblems, via minorization-maximization (MM), alternate direction penalty method (ADPM), and feasible point pursuit successive convex (FPP-SCA) approximation. Convergence of every iterative loop is proved, resulting in guaranteed convergence of the whole procedure with polynomial-time complexity. Finally, numerical examples are presented to demonstrate that the proposed method can construct a unimodular waveform and filter with better information acquisition ability and more desirable cross-correlation function.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

A novel nanobiosensing framework named “in vivo computation” has been proposed recently, where the challenge of early tumor detection is overcome from an optimization perspective. The biological gradient field (BGF) triggered by the tumor lesion is viewed as the optimizable objective function with the tumor site being the global optimum. The externally manipulable nanorobots playing the role of agents are manipulated in the search space (i.e., the vascular network of high-risk tissue). Several computational strategies have been proposed to realize tumor targeting by overcoming the in vivo constraints which focused on the tumor detection process without any emphasis on the nanorobots aggregation at the tumor. In this paper, we focus on the utilization rate of agents, which means to improve the percentage of nanorobots that detect the tumor site after it has been found by the first arrival agent (i.e., the nanorobot that detects the tumor at the earliest), and the solution set search, which means to find the tumor region as whole as possible. This process is interpreted as the exploitation process of in vivo computation. An exploitation approach named center-aided weak priority evolution strategy (CWP-ES) is developed for the setting of nanorobot moving direction in this paper. In the approach, a direction generated by the center of agents that have found the tumor mixed with the direction generated by the weak priority evolution strategy (WP-ES) proposed in the previous work is used to steer the motion of nanorobots that have not detected the tumor. Several numerical experiments are performed in a 3D search space to show the effectiveness of this novel computational nanobiosensing approach in three BGF landscapes with different degrees of optimization complexity.

Magnetically assembled bioresorbable nanoswimmers (NSs) can be used to highlight small tumors, thereby increasing the diagnostic capability of existing medical imaging techniques. Built upon our earlier work, this article proposes a novel in vivo computational framework for early cancer detection. Engineered NSs experience a change in their physical properties under the influence of tumor-induced biological gradients. The biologically sensed data by such bio-nano things (NSs) can either trigger an autonomous target-directed motion or be assisted through external manipulation for steering the swarm toward the target. Previously developed externally manipulable in vivo computation requires constant monitoring of NSs, introducing positioning and steering errors along with a limit on the swarm size. A parallel approach called autonomous in vivo computation helps to resolve the above drawbacks, but the tumor homing is slow contributing to a higher percentage of predetection loss of NSs. We propose the spot sampling strategy for an autonomous swarm which considers the whole swarm as a single entity for the purpose of its tracking and steering. We show through computational experiments: 1) that the proposed semi-autonomous in vivo framework can achieve faster tumor sensitization in complex environments having static and mobile obstacles and 2) that the spot sampling provides sufficiently precise data to steer the swarm toward the target, saving around 90% of the monitoring resource. Our proposed framework also helps to achieve a large swarm size (number of NSs) which in return can achieve a higher deposition of NSs on malignant tumors.

We have recently proposed a framework of in vivo computation (IVC) which transforms the early tumor sensing problem into a computational problem. In the framework, a tumor-triggered biological gradient field (BGF) guides the swarm-intelligence-assisted targeting process, where externally manipulable and trackable magnetic nanorobots act as computational agents for the optimization procedure. As BGF can be viewed as an objective function which is utilized to define the fitness landscape for the agents, the inherent attributes of BGF are critical to the IVC process. All our previous investigations are based on the hypothesis that the BGF landscape remains time invariant during the tumor-targeting process, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will naturally lead to time-dependent variation of BGF, which means that the targeting process should be modeled as a dynamic function optimization problem. Based on this consideration, we focus on dynamic IVC by considering different variation patterns of BGF in this article. Two computational intelligence strategies named “swarm-based learning” and “individual-based learning” are proposed for dealing with the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategies. In addition, the above process is conducted in a 3-D search space, where the tumor vascular network is generated by an invasion percolation algorithm, which is more realistic compared to the 2-D search space in our previous works.

We have recently proposed a novel framework of in vivo computation by transforming the early tumor detection into an optimization problem. In the framework, the tumor-triggered biological gradient field (BGF) provides aided knowledge for the swarm-intelligence-assisted tumor targeting process. Our previous investigations are based on the hypothesis that the BGF landscape is time-invariant, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will bring about time-dependent variation of BGF. Thus, we focus on dynamic in vivo computation by considering different variation patterns of BGF in this paper. A computational intelligence strategy named “swarm-based learning strategy” is proposed for overcoming the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategy. In addition, the above process is conducted in a three-dimensional search space, which is more realistic compared to the two-dimensional search space in our previous work.

In this paper, a novel fuzzy-receiver operating characteristics (F-ROC) is proposed to evaluate the performance of fuzzy-inspired biosensing (FIB). The development process of diseases is fuzzy, and the traditional classification of diseases is an either-or hard classification, which cannot reflect the developmental stages of diseases correctly. FIB utilizes fuzzy theory to realize the disease classification, and the traditional ROC curve is not suitable as an evaluation mechanism for the disease fuzzy process. Therefore, this paper proposes a general model to describe the transfer process of the membership function of FIB, and proposes the reasons why the traditional ROC is not applicable. After that, this paper rewrites the parameter definitions in the ROC curve and establishes the F-ROC curve to evaluate the transfer performance of FIB. An example of tumor stage classification using multi-contrast-agent strategies (MCAS) strategy is utilized to verify the evaluation of the FIB performance of the proposed F-ROC curve.

Different natural biological processes are possible because of the collaboration among simple living cells. Similarly, computerized systems, such as multiagent systems (MASs), rely on multiple interacting agents with simplified and reduced capability, to collectively solve difficult problems that are impossible for individual agents to solve on their own. This work highlights an autonomous tumor sensitization strategy in complex human vasculature, where target detection is achieved through the swarm coordination mechanism, with no prior knowledge of tumor location. We propose that small-scale biocompatible organisms, such as nanoparticles, can perform deterministic tasks following the simple principles of aggregation and migration. We aim to show through computational experiments that tumor-triggered biophysical gradients can be leveraged by nanoparticles to collectively move toward the potential tumor hypoxic regions. Although individual nanoparticles have no target-directed locomotion ability due to limited communication and computation capability, we demonstrate that once passive collaboration is achieved, they can successfully avoid obstacles and detect the tumor. Numerical experiments demonstrate that the overall targeting efficiency could improve considerably from 10% to 90% through passive collaboration among nanoparticles. Furthermore, with the introduction of noisy search space and mobile obstacles, the targeting performance would reduce by 25%. Such self-regulating particles can be used as homing agents for target amplification, and hence can assist in early cancer detection through contrast-enhanced medical imaging. We believe that our work will motivate self-dependent and noncentralized approach for magnification of tumor location.

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, the niche technology has been widely used. Specifically, the niche genetic algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, effectively keep the diversity of the population, and prematurely avoid the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis.

Several evolution strategies for in vivo computation are proposed with the aim of realizing tumor sensitization and targeting (TST) by externally manipulable nanoswimmers. In such targeting systems, nanoswimmers assembled by magnetic nanoparticles are externally manipulated to search for the tumor in the high-risk tissue by a rotating magnetic field produced by a coil system. This process can be interpreted as in vivo computation, where the tumor in the high-risk tissue corresponds to the global maximum or minimum of the in vivo optimization problem, the nanoswimmers are seen as the computational agents, the tumor-triggered biological gradient field (BGF) is used for fitness evaluation of the agents, and the high-risk tissue is the search space. Considering that the state-of-the-art magnetic nanoswimmer control method can only actuate all the nanoswimmers heading in the same direction simultaneously, we introduce the orthokinetic movement strategies into the agent location updating in the existing swarm intelligence algorithms. Especially, the gravitational search algorithm (GSA) is revisited and the corresponding in vivo optimization algorithm called orthokinetic GSA (OGSA) is proposed to carry out the TST. Furthermore, to determine the direction of the orthokinetic agent movement in every iteration of the operation, we propose several strategies according to the fitness ranking of the nanoswimmers in the BGF. To verify the superiority of the OGSA and choose the optimal evolution strategy, some numerical experiments are presented and compared with that of the brute-force search, which represents the traditional method for TST. It is found that the TST performance can be improved by the weak priority evolution strategy (WP-ES) in most of the scenarios.

By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy.

A joint design of the transmit weight sequence and receive filter is proposed to improve target information acquisition in high-resolution radar. First, using the criterion for target information acquisition maximization, the design is cast as a nonconvex fractional quadratically constrained quadratic problem (QCQP). Then, by employing a bivariate auxiliary function introduced in Dinkelbach’s algorithm to decouple the fractional objective function, an algorithm with polynomial computational complexity is developed to solve the QCQP using a cyclic maximization procedure alternating between two semidefinite relaxation (SDR) problems. Through exploiting a suitable rank-one decomposition, it is verified that the optimal solution obtained from the alternative iterative process is also optimal to the original QCQP. Finally, numerical examples are presented to demonstrate the performance of the proposed design.

Probabilistic amplitude shaping (PAS) has proved to be a promising way to achieve the shaping gain for an additive white Gaussian noise channel with a distribution matcher (DM). However, the DM schemes may suffer a rate loss and increase both complexity and latency, when they are not suited for the input bit stream with redundancy. In this paper, a novel PAS strategy is proposed for a joint source and channel coded modulation system, where the residual source redundancy after source coding can be exploited to obtain both shaping and coding gains. It is shown that the residual source redundancy can be controlled by choosing the row weight distribution for source code, thus making the probability distribution of the modulated symbols be optimized under an appropriate interleaver design. To guarantee the error-floor performance, the source code is constrained by predicting the probability distribution of source bits within target finite-length frames. By jointly designing source and channel codes, the residual source redundancy can also be exploited to achieve the coding gain. Compared with the state-of-the-art code pairs, the proposed code pairs have better error-floor performance, and achieve higher coding and shaping gains without suffering from the rate-loss and the latency caused by DM.

Stroke is a serious manifestation of various cerebrovascular diseases and one of the most dangerous diseases in the world today. Volume quantification and location detection of chronic stroke lesions provide vital biomarkers for stroke rehabilitation. Recently, deep learning has seen a rapid growth, with a great potential in segmenting medical images. In this work, unlike most deep learning-based segmentation methods utilizing only magnetic resonance (MR) images as the input, we propose and validate a novel stroke lesion segmentation approach named multi-inputs UNet (MI-UNet) that incorporates brain parcellation information, including gray matter (GM), white matter (WM) and lateral ventricle (LV). The brain parcellation is obtained from 3D diffeomorphic registration and is concatenated with the original MR image to form two-channel inputs to the subsequent MI-UNet. Effectiveness of the proposed pipeline is validated using a dataset consisting of 229 T1-weighted MR images. Experiments are conducted via a five-fold cross-validation. The proposed MI-UNet performed significantly better than UNet in both 2D and 3D settings. Our best results obtained by 3D MI-UNet has superior segmentation performance, as measured by the Dice score, Hausdorff distance, average symmetric surface distance, as well as precision, over other state-of-the-art methods.

The articles in this special section focus on flexible sensing and medical imaging for cerebro-cardiovascular health care services. Healthcare and disease management are receiving increasing attention. Cerebro-cardiovascular diseases (CCVDs) are the leading cause of death globally. Cerebrocardiovascular diseases include a variety of medical conditions that affect the blood vessels of the brain, the cerebral circulation, and the heart. The common presentations of CCVDs include an ischemic stroke or mini-stroke and sometimes a hemorrhagic stroke, heart failure, hypertensive heart disease, etc. The important contributing risk factors include high blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, and excessive alcohol consumption, among others. A rapidly growing field, biomedical and health engineering research for CCVDs is unique in that it involves a variety of specialties such as neurology, surgery, cardiology, psychology and rehabilitation, and must meet the growing need for sophisticated, up-to-date biomedical and health informatics on clinical data, diagnostic testing, and therapeutic issues.

We propose two novel evolution strategies of swarm intelligence for nanoswimmer-oriented in vivo computation, which corresponds to the computing model of the direct targeting strategy (DTS) where externally manipulable magnetic nanoswimmers are employed for cancer detection. In the DTS, the nanoswimmers move in the high-risk tissue region guided by an external magnetic field to search for the early cancer that cannot be visualized using traditional imaging modalities due to their limited resolution. Subject to the constraint of the state-of-the-art controlling technology which can only generate a uniform magnetic field to steer all the nanoswimmers simultaneously, we revisit the conventional gravitational search algorithm (GSA) and propose the orthokinetic gravitational search algorithm (OGSA) to carry out the DTS. Furthermore, we propose the general evolution strategy (G-ES) and the weak priority evolution strategy (WP-ES) and apply them to the OGSA for the path planning of magnetic nanoswimmers. To prove the superiority of the OGSA in the DTS, we present some simulation examples and make comparison with the "brute-force" search, which corresponds to the traditional systemic targeting strategy. Furthermore, we compare the performance of WP-ES and G-ES in the OGSA. It is found that the WP-ES can improve the performance of swarm intelligence algorithms (e.g., GSA) in the DTS.

In this paper, the uplink adaptation for massive multiple-input-multiple-output (MIMO) networks without the knowledge of user density is considered. Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells. The asymptotic signal-to- interference-plus-noise ratio (SINR) of the proposed scheme is then derived, and the distribution of interference power considering the randomness of the users' locations is proved to be Gaussian. By tracking the mean and variance of interference power, an online robust rate adaptation algorithm ensuring a target packet outage probability is proposed for the scenario where the interfering channel and the user density are unknown.

We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the “brute-force” search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biophysical gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.

Recent progress on bioresorbable and biocompatible miniature systems provides prospects for developing novel nanobots operating inside the human body. These nanoscale systems are expected to dissolve in vivo and cause no side effect after completing their tasks. Motivated by these advancements, we have developed the analytical framework of touchable communication (TouchCom) to describe the process of targeted drug delivery (TDD) using physically transient nanobots. Built upon our previous work, we develop a novel latticed channel model of TouchCom for an interconnected capillary network near a targeted tumor area. Specifically, we propose a two-dimensional grid to synthesize the microcirculation environment, which is used to describe the propagation process of nanobots. Based on this model, we consider the influence of blood pressure on the efficiency of TDD, and introduce a compensation strategy with the help of an external guiding field to mitigate the misalignment between the direction of blood pressure and the tumor location.

A new computing-inspired multiple-cancer detection procedure (MCDP) is proposed. In the MCDP, the cancer areas to be detected can be regarded as solutions of an objective function, the tissue region around the cancer areas can be mapped to the parameter space of the solutions, and the nanorobots correspond to the agents in the optimization procedure. The process that the nanorobots look for the cancer areas by swimming in the tissue region can be mapped to the process that the agents search for the solutions in the parameter space. Niche Genetic Algorithm (NGA) is widely used in multimodal function optimization and non-monotonic function optimization. It can search all global optimums of multiple hump function in a running, keep the diversity of the population effectively, and avoid premature of solutions got from normal GA. Inspired by the optimization procedure of NGA, the multiple cancer detection procedure (MCDP) has been studied and the NGA-inspired cancer detection procedure has been proposed in order to locate the targets efficiently at the same time by taking into account realistic in vivo propagation and controlling of nanorobots. Finally, some comparative numerical examples are presented to demonstrate the effectiveness of the NGA-inspired MCDP.

The information-theoretic techniques can ensure security in communication regardless of the computational power of the attackers. The requirements for applying such techniques require: 1) an advantage over the eavesdroppers' quality of reception and 2) the location information on the eavesdropper. Traditionally, the performance of a secure communication link is measured using the metrics of secrecy capacity or outage probability, which are both related to the relative quality of the legitimate link compared with that of the eavesdropper link. In this paper, we present a new metric, called secrecy pressure, which measures the security level of the surface/environment where the legitimate link is embedded but is independent of the position of the eavesdropping node. The metric can be also visualized as a secrecy map. The analytical results show how the optimization of the secrecy pressure measure can lead to decide the optimum transmit antenna orientation and/or the position and power of an additional interfering node (friendly jammer).

Information-theoretical approaches can ensure security, regardless of the computational power of the attackers. Requirements for the application of this theory are: 1) assuring an advantage over the eavesdropper quality of reception and 2) knowing where the eavesdropper is. The traditional metrics are the secrecy capacity or outage, which are both related to the quality of the legitimate link against the eavesdropper link. Our goal is to define a new metric, which is the characteristic of the security of the surface/environment where the legitimate link is immersed, regardless of the position of the eavesdropping node. The contribution of this paper is twofold: 1) a general framework for the derivation of the secrecy capacity of a surface, which considers all the parameters that influence the secrecy capacity and 2) the definition of a new metric to measure the secrecy of a surface: the secrecy pressure. The metric can be also visualized as a secrecy map, analogously to weather forecast. Different application scenarios are shown: from “forbidden zone” to Gaussian mobility model for the eavesdropper. Moreover, the secrecy outage probability of a surface is derived. This additional metric can measure, which is the secrecy rate supportable by the specific environment.

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs' resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter category are heuristical and lack theoretical performance guarantees. In this work, we formulate dynamic VM management as a large-scale Markov Decision Process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, MadVM can be implemented in a distributed system, which should suit the needs of real data centers. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.

Numerous methods for detecting audio splicing have been proposed. Environmental-signature-based methods are considered to be the most effective forgery detection methods. The performance of existing audio forensic analysis methods is generally measured in the absence of any anti-forensic attack. Effectiveness of these methods in the presence of anti-forensic attacks is therefore unknown. In this paper, we propose an effective anti-forensic attack for environmental-signature-based splicing detection method and countermeasures to detect the presence of the anti-forensic attack. For anti-forensic attack, dereverberation-based processing is proposed. Three dereverberation methods are considered to tamper with the acoustic environment signature. Experimental results indicate that the proposed dereverberation-based anti-forensic attack significantly degrades the performance of the selected splicing detection method. The proposed countermeasures exploit artifacts introduced by the anti-forensic processing. To detect the presence of potential anti-forensic processing, a machine learning-based framework is proposed. In particular, the proposed anti-forensic detection method uses a rich-feature model consisting of Fourier coefficients, spectral properties, high-order statistics of musical noise residuals, and modulation spectral coefficients to capture traces of dereverberation attacks. The performance of the proposed framework is evaluated on both synthetic data and real-world speech recordings. The experimental results show that the proposed rich-feature model can detect the presence of anti-forensic processing with an average accuracy of 95%.

It has been shown in the existing literature that data symbols could help to relieve the pilot contamination issue of massive multiple-input multiple-output systems. In this paper, we show that pilot information of interference users could be exploited jointly with the data-assisted transmission mechanism. Specifically, we consider the uplink transmission of a massive multiple-input multiple-output network where there are backhauls with significant delay between base stations. Although real-time inter-base station cooperation is infeasible; pilot information, whose update frequency is very low, of closest interference users can be notified to the serving base station. Conventionally, estimating the interference channel will lead to larger pilot overhead. In our proposed scheme, however, the detected uplink data could provide sufficient degree- of-freedom to estimate the channels of the closest interference users without increasing pilot overhead. Hence, the inter-cell interference can be suppressed efficiently. In order to obtain useful insights on system-level performance, a stochastic geometry based framework is established to analyze the distribution of the signal-to-interference ratio of the proposed scheme. The closed-from expression of the asymptotic bound is thereby derived. It is shown that the analytical expression fits the numerical simulations very well, and the proposed scheme has significant gain over the data-assisted uplink scheme without backhaul.

A novel data-assisted channel estimation scheme is proposed for uplink massive multiple-input multiple-output (MIMO) systems to alleviate the performance bottleneck due to pilot contamination. Specifically, the uplink resource within a fading block is first divided into a number of data blocks, which are encoded respectively; then the detected data blocks are utilized iteratively to refine the channel estimation, improving the signal-to-interference-plus-noise ratio (SINR) of the following data blocks. In the existing literature, the system performance can not scale up with the number of base station's antenna due to the effect of pilot contamination. The proposed scheme provides a promising approach to improve the uplink SINR by the order θ(LM/L+M) without increasing the length of pilot sequence, where L and M are the number of transmission symbols within a fading block and the number of base station's antennas respectively. It is shown by simulations that the uplink performance of massive MIMO systems is significantly increased by the proposed scheme.

Audio splicing is one of the most common manipulation techniques in the audio forensic world. In this paper, the magnitudes of acoustic channel impulse response and ambient noise are considered as the environmental signature and used to authenticate the integrity of query audio and identify the spliced audio segments. The proposed scheme firstly extracts the magnitudes of channel impulse response and ambient noise by applying the spectrum classification technique to each suspected frame. Then, correlation between the magnitudes of query frame and reference frame is calculated. An optimal threshold determined according to the statistical distribution of similarities is used to identify the spliced frames. Furthermore, a refining step using the relationship between adjacent frames is adopted to reduce the false positive rate and false negative rate. Effectiveness of the proposed method is tested on two data sets consisting of speech recordings of human speakers. Performance of the proposed method is evaluated for various experimental settings. Experimental results show that the proposed method not only detects the presence of spliced frames, but also localizes the forgery segments. Comparison results with previous work illustrate the superiority of the proposed scheme.

The performance of wireless networks is fundamentally limited by interference [or, equivalently, the signal-to-interference ratio (SIR)]. In an attempt to characterize the interference as a direction-selective quantity and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel network description framework, namely the interference azimuth spectrum (IAS). The IAS represents the distribution of interference in the angular domain and is parallel to the conventional power azimuth spectrum (PAS) used in propagation channels. We also extend this concept to the directional characterization of average achievable rate, assuming that interference is treated as noise. Provided with this analytical framework, we present the notion of local area outage, defined as the probability that a receiver is in the state of outage within a local area where both interference and desired signal are assumed to be wide-sense stationary (WSS). We further propose the geometry-based stochastic models (GBSMs) as a part of the IAS framework, where the interfering terminals are randomly distributed according to a specific probability density function (pdf) of their positions. The GBSMs are applicable to a wide variety of wireless network environments without and with interferer clustering. The proposed methodology would provide useful insight on the design and performance assessment of future networks, featured by opportunistic, randomized, and dense placement of nodes.

In this paper a new method is proposed for classifying randomly deployed sensor nodes over an area of interest into distinctive categories. The problem of event region and event boundary detection in Wireless Sensor Networks (WSNs) is addressed. Particularly, analysis is provided for a scenario whereby an area of interest featuring two distinctive phenomena is being monitored with a randomly deployed network of wirelessly connected sensor nodes. Each sensor node in the network is asked to acknowledge whether or not it classifies itself as an event-region node based only on its own environment reading. The key decision factor employed in this approach is the statistical attributes of received signal distribution at each sensor node. Applying this algorithm results in reducing the required bandwidth for transmitting the environmental reading to the base station to be proportional to the size of the event-region. This is opposed to other approaches where the required bandwidth is proportional to the size of the entire network.

In order to evaluate the performance of emerging wireless systems such as relay, sensor, and mobile ad hoc networks with multihop coverage extensions, spatial-temporal network channel models are required. These models should include the directional characteristics of network information flows in order to exploit the spatial domain due to the deployment of advanced antenna systems. Furthermore, the models should be capable of handling non-stationary scenarios with dynamic evolution of network nodes. In an attempt to solve these two problems which have not been fully addressed in the existing literature, and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel spatial-temporal network modeling framework, where the relevant figure-of-merit (FOM) may be received signal power, channel capacity, event detection error exponent, etc., depending on the network's type. Our methodology would be most useful for the design and analysis of future networks featured by decentralized, random, and dense placement of nodes.

This paper proposes a novel constellation alignment and interference cancellation scheme for wireless systems. Compared with the existing literatures on interference channel capacity analysis with lattice codes, a general signal processing algorithm is proposed for the systems using mutual baseband signal processing components, i.e., channel coding and QAM modulation. Specifically, multiple interference transmitters are proposed to align their QAM constellations at the signal receiver; the receiver first demodulates the aligned interference symbols by a novel constellation, then detects the aligned interference message which is the bitwise exclusive or of each individual interference message, and finally, cancels the aligned interference and detects the desired signal. Since the complicated joint detection in the existing lattice-based interference alignment literatures is avoided, the overall complexity is dramatically reduced. With the proposed scheme, the signal receiver can exploit both modulation and channel coding structure of interferences to improve the quality of interference cancellation. Hence, it is shown that compared with the existing wireless systems without constellation alignment, the proposed scheme could significantly improve the receiver's performance in the scenario with multiple interference sources.

In the research of time varying communication channels, signal amplitude attenuation is considered to be the main cause of channel varying memories. The Gilbert-Elliot channel has been used to model this kind of memories for a long time with a decision-feedback decoder or an equivalent genie-aided decoder being its optimal decoders. In the first part of this paper, we show that the decoders have not performed optimal signal detection for the channel. The expression of the achievable mutual information rate by the genie-aided decoder has been revisited and updated recently. The non-optimality of the decoders can be confirmed by showing the maximum value of the updated expression is smaller than the channel's information capacity, which is obtained by a system-model-independent algorithm. In the second part of this paper, we argue that the phase change of transmitted signal caused by the Doppler effect plays a much more important role in determining the channel varying memory than their amplitude fading does. The Gilbert-Elliot channel, capturing amplitude fading, is therefore not ideal for modelling the memory. And finding its optimal decoder, which is shown to be very difficult, may have little physical significance.

A novel channel representation for a two-hop decentralized wireless relay network (DWRN) is proposed, where the relays operate in a completely distributive fashion. The modeling paradigm applies an analogous approach to the description method for a double-directional multipath propagation channel, and takes into account the finite system spatial resolution and the extended relay listening/transmitting time. Specifically, the double-directional information azimuth spectrum (IAS) is formulated to provide a compact representation of information flows in a DWRN. The proposed channel representation is then analyzed from a geometrically-based statistical modeling perspective. Finally, we look into the problem of relay network tomography (RNT), which solves an inverse problem to infer the internal structure of a DWRN by using the instantaneous double-directional IAS recorded at multiple measuring nodes exterior to the relay region.

The authors explore the potential application of cognitive interrogator network (COIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate target localisation. The authors first present the COIN architecture in which the central base station (BS) continuously and intelligently customises the illumination modes of the distributed interrogators in response to the system's changing knowledge of the channel condition and subject movement. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale COIN with randomly distributed active sensors are derived based upon the implemented cognitive intelligence. As an important component to facilitate the adaptive illumination of the environment, the sequential-hypothesis-testing framework is proposed to estimate the tag antenna orientation. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitive schemes on the system performance.

A novel channel model for a two-hop decentralized wireless relay network (DWRN) is presented, where the relays operate in a completely distributive fashion. The model is based on an analogous approach to the conventional description methods for wideband directional multipath channels. First, the concept of information azimuth-delay spectrum (IADS) is devised for a simplified DWRN with perfect receiver conditions. The IADS describes the distribution of the incoming information at the destination in the azimuth-delay domain. This definition, parallel to the widely-used power azimuth-delay spectrum (PADS) for physical multipath channels, represents a compact description of virtual DWRN channels. Subsequently, several key quantities derived from the IADS are introduced, which provide an intuitive way for analysis of a DWRN. The proposed approach is then applied to an elliptical random network (ERN) with uniformly distributed relay nodes. Numerical examples are used to demonstrate the usefulness of the suggested channel description method.

In this paper we demonstrate the application of Hidden Markov Models (HMM) for localization and tracking in ultra wide band (UWB) radar networks. To improve localization, a Voronoi region based approach is utilized to form a HMM for detection and tracking of mobile target. The observations used for the HMM localization are obtained from the power delay profile of the received signals. In UWB systems the use of entire power delay profiles instead of the total power only, allows to reach higher localization accuracy. This is due to the power delay profile being a measure of the power as well as the time of arrival. Simulation results suggest a performance gain of 7dB over the maximum likelihood estimation for localization in presence of path loss at intermediate values of signal to noise ratio (SNR).

In this paper, the performance of vector precoding in multiple input multiple output broadcast channels (MIMO BC) is investigated and compared with other channel decomposition techniques utilized for implementing zero forcing (ZF) preceding. It is a known result that ZF precoding requires pseudo inversion of the channel matrix, where this operation is only optimum when the transmitter power is unconstrained. The problem when the transmitter is subject to average or maximum power constraints is well known, where results published have indicated that ZF precoding approaches the maximum capacity bound if the dimensionality of the system is greater than the number of transmitter antennas. A vector precoding technique for MIMO BC channels is investigated in this paper where pseudo inversion is circumvented by employing joint co-operation between transmitter and receiver for all users. This technique adopts a time scheduling approach to service the users which facilitates decentralized multi-user detection at the receiver. This approach yields an improvement to the bit error rate probability by approximately an order of magnitude as compared to the ZF approach utilizing other channel decomposition techniques. The scheme also enables an increase in the capacity of the MIMO BC, with less computational complexity as compared to the techniques employing Moore-Penrose pseudo inverse.

The diversity gains achievable in the generalised distributed antenna system with cooperative users (GDAS-CU) are considered. A GDAS-CU is comprised of M largely separated access points (APs) at one side of the link, and N geographically closed user terminals (UTs) at the other side. The UTs are collaborating together to enhance the system performance, where an idealised message sharing among the UTs is assumed. First, geometry-based network models are proposed to describe the topology of a GDAS-CU. The mean cross-correlation coefficients of signals received from non-collocated APs and UTs are calculated based on the network topology and the correlation models derived from the empirical data. The analysis is also extendable to more general scenarios where the APs are placed in a clustered form due to the constraints of street layout or building structure. Subsequently, a generalised signal attenuation model derived from several stochastic ray-tracing-based pathloss models is applied to describe the power-decaying pattern in urban built-up areas, where the GDAS-CU may be deployed. Armed with the cross-correlation and pathloss model preliminaries, an intrinsic measure of cooperative diversity obtainable from a GDAS-CU is then derived, which is the number of independent fading channels that can be averaged over to detect symbols. The proposed analytical framework would provide critical insight into the degree of possible performance improvement when combining multiple copies of the received signal in such systems.

In this paper, we explore the application of cooperative communications in ultra-wideband (UWB) wireless body area networks (BANs), where a group of on-body devices may collaborate together to communicate with other groups of on-body equipment. Firstly, time-domain UWB channel measurements are presented to characterize the body-centric multipath channel and to facilitate the diversity analysis in a cooperative BAN (CoBAN). We focus on the system deployment scenario when the human subject is in the sitting posture. Important channel parameters such as the pathloss, power variation, power delay profile (PDP), and effective received power (ERP) crosscorrelation are investigated and statistically analyzed. Provided with the model preliminaries, a detailed analysis on the diversity level in a CoBAN is provided. Specifically, an intuitive measure is proposed to quantify the diversity gains in a single-hop cooperative network, which is defined as the number of independent multipaths that can be averaged over to detect symbols. As this measure provides the largest number of redundant copies of transmitted information through the body-centric channel, it can be used as a benchmark to access the performance bound of various diversity-based cooperative schemes in futuristic body sensor systems.

We explore the potential application of Cognitive Interrogator Network (CIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate source localization. We first present the CIN architecture in which the central base station (BS) continuously and intelligently customizes the illumination modes of the distributed transceivers in response to the system’s changing knowledge of the channel conditions and subject movements. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale CIN with randomly distributed interrogators are derived based upon the implemented cognitive intelligences. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitions on the system performance.

In this paper, geometry-based channel models are proposed to describe the topology of generalized distributed antenna systems with cooperative diversity (GDAS-CD). The system architecture comprises M largely separated access points (APs) at one side of the link, and N geographically closed user terminals (UTs) at the other. The UTs are assumed to be operating in cooperative mode to enhance the system diversity. The average cross-correlation of signals received from noncollocated APs and UTs is derived based on the network topology and the correlation models derived from the empirical data. The analysis is also extended to more general scenarios when the APs are placed in a clustered form. The presented results would provide useful insight into the degree of possible performance improvement from the GDAS-CD.

A geometry-based channel model is proposed to describe the topology of a generalized distributed antenna system with cooperative diversity (GDAS-CD). The system architecture comprises a number of largely separated access points (APs) each with multiple antennas within an AP at one side of the link, and several geographically closed user terminals (UTs) each having multiple antennas within a UT at the other side. The UTs are assumed to be cooperative devices. The average cross-correlation of signals received from non-collocated APs and UTs is derived based on the system topology and the empirical models proposed by Sorensen. Subsequently, we investigate the diversity gains obtainable from the GDAS-CD based on the proposed model, which would provide insight into the degree of possible performance improvement when combining multiple copies of the received signal.