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Nanoparticle drug carriers play an important role in facilitating efficient targeted drug delivery, i.e., improving treatment success and reducing drug costs and side effects. However, the mobility of nanoparticle drug carriers poses a challenge in designing drug delivery systems. Moreover, healing results critically depend on the rate and time duration of drug absorption. Therefore, in this paper, we aim to design a controlledrelease drug delivery system with a mobile drug carrier that minimizes the total amount of released drugs while ensuring a desired rate of drug absorption during a prescribed time period. We model the mobile drug carrier as a mobile transmitter, the targeted diseased cells as an absorbing receiver, and the channel between the transceivers as a time-variant channel since the carrier mobility results in a time-variant absorption rate of the drug molecules. Based on this, we develop a molecular communication (MC) framework to design the controlled-release drug delivery system. In particular, we develop new analytical expressions for the mean, variance, probability density function, and cumulative distribution function of the channel impulse response (CIR). Equipped with the statistical analysis of the CIR, we design and evaluate the performance of the controlled-release drug delivery system. Numerical results show significant savings in the amount of released drugs compared to a constant-release rate design and reveal the necessity of accounting for drug carrier mobility for reliable drug delivery.

This paper presents a stochastic analysis of the time-variant channel impulse response (CIR) of a three dimensional diffusive mobile molecular communication (MC) system where the transmitter, the absorbing receiver, and the molecules can freely diffuse. In our analysis, we derive the mean, variance, probability density function (PDF), and cumulative distribution function (CDF) of the CIR. We also derive the PDF and CDF of the probability p that a released molecule is absorbed at the receiver during a given time period.The obtained analytical results are employed for the design of drug delivery and MC systems with imperfect channel state information. For the first application, we exploit the mean and variance of the CIR to optimize a controlled-release drug delivery system employing a mobile drug carrier. We evaluate the performance of the proposed release design based on the PDF and CDF of the CIR. We demonstrate significant savings in the amount of released drugs compared to a constant-release scheme and reveal the necessity of accounting for the drug-carrier's mobility to ensure reliable drug delivery. For the second application, we exploit the PDF of the distance between the mobile transceivers and the CDF of p to optimize three design parameters of an MC system employing on-off keying modulation and threshold detection. Specifically, we optimize the detection threshold at the receiver, the release profile at the transmitter, and the time duration of a bit frame. We show that the proposed optimal designs can significantly improve the system performance in terms of the bit error rate and the efficiency of molecule usage.

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In molecular communications, the direct detection of signaling molecules may be challenging due to the lack of suitable sensors and interference from co-existing substances in the environment. Motivated by examples in nature, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. The underlying reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed-form. To analyze these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables in each time step. We also derive insightful closed-form solutions for a special case. The accuracy of the proposed algorithm is verified by particle-based simulations. Our results show that the concentration of the product molecules has a similar characteristic over time as the concentration of the signaling molecules. We analyze the bit error rate (BER) for a threshold detector and highlight that significant improvements in the BER can be achieved by carefully choosing the molecular probe and optimizing the detection threshold.

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.

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We propose and investigate a model for shared ride trip planning in ad-hoc mobile geosensor networks. Our focus is on the communication strategies between the network nodes. In a dynamically changing network of autonomous nodes all trip plans and provisions need to be kept up-to-date. At the same time, energy consumption by broadcasting messages needs to be minimized. Hence, we have to solve an optimization problem: find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment.