Fig 1 - uploaded by Weisi Guo
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Illustration of: (a) neuron, (b) action potential, and (c) spike activity s with different stimulus levels and resulting calcium concentration c.
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Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in the combined ambient noise with fluctuated baseline, which contaminates the observations, thereby deteriorating the reliability of spike detection. This may be even worse in the face...
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Context 1
... data (e.g., the brain science, the cell communications, and sequencing [5], [6]), as the changes of calcium-ion concentration reflect the occurrence of most cell functions [7]. For example, in order to study the dynamic behaviors of neuron cells, neuron action potentials (spike activities) are required to be detected. As is shown in Fig. 1(a)-(b), such spike activity links to a calcium influx that may lead to a steep increase of the intracellular free calciumion. As such, detecting the neuron spike activities requires the track of the calcium concentration, which serves as the key to characterizing the neuron dynamics, and further understanding how neuron systems react ...
Context 2
... in real biological systems may guide the designs of synthetic neural systems that mimic neuron [8]- [10] and cell signaling [11]- [13]. In the context of neural activity inference, spikes, dynamical baseline, and critical model parameters are detected and estimated through the fluorescence observations from a calcium image. As is shown in Fig. 1(c), such inference is limited by three difficulties. Firstly, spikes lead to a rapid rise in intracellular calcium (Ca 2+ ) concentration followed by a slow decay (i.e., time-to-peak 8-40ms, and decay constant 0.3-1.5s [14]- [17]). This effect gives rise to the transients induced by individual spikes to overlap, and aggregating in a ...
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... Molecular communication systems are widespread among cells and organisms at nano-scale in biology, attracting a large number of researchers to be engaged in interdisciplinary research and cooperation recently [1]- [3]. Meanwhile, MC is widely applied to the field of environmental monitoring, biological detection and disease treatment, due to the environment in which it exists and bio-compatibility [4]- [6]. ...
As a promising communication paradigm, Molecular Communication (MC) has been developing for more than ten years. During this period, with the continually development and mature of molecular channel, more and more factors from the actual molecular environment are taken into account to model the molecular channel more precisely. Due to the fact that chemical reactions are common and crucial in molecular communication environment, this paper investigates the impacts of various reaction rates on the channel modeling and channel capacity of MC. First, a detailed molecular system model is presented with a first-order degradation reaction in the channel and a second-order reversible reaction at the receiver side. Because of the Inter-Symbol Interference (ISI) induced by the stochastic nature of molecule motions, we propose an approximation method with low complexity to describe the received molecule signal with an arbitrary length of ISI. Based on the noisy molecular signal, closed-form expressions for probabilities of detection and false alarm are derived. The performance of channel capacity is evaluated concentrating on different circumstances with decoupled reaction rates and two prevalent modulation techniques (BCSK and MoSK) are implemented to ensure the accuracy and scientific of the conclusion. Numerical results show that the channel capacity could be enhanced significantly by selecting proper messenger molecules with desirable reaction rates in specific molecular channel.
... To combat this, we will resort to a particle filter (PF) scheme. For short, PF is able to approximate the complex density (x) via a large amounts of simulated particles, denoted as x (i) k (i = 1, ..., I), accompanying with the evolving probability weights [33]. For our sequential pre-filter, we use the particles to simulate the robot's position, velocity and orientation (i.e., x ...
... k . Note that, after derivingz (s) k , a re-sampling procedure should be performed in order to eliminate particles with negligible weights [32], [33]. ...
... On the other hand, for larger threshold d th > 33m, the false-alarm will be inevitably included in the reported information, which will also degrade the localization performance. As a feasible compromise, we may properly configure the distance threshold to d th ∈ [25,33]. ...
In recent robotic applications, a critical need is to simultaneously detect communication (emission state) and estimate its trajectory. Whilst wireless sensor observations are useful, they are often uncertain due to the stochastic communication bursts and robot mobility. Over-sampling the information environment can incur excessive radio interference and energy usage. Therefore, one challenge is how to improve the efficiency of sensing under sparse and dynamic information, and make accurate inference on the robot's location. Here, we design a novel mixed detection and estimation (MDE) scheme to enhance both the accuracy and the efficiency by exploiting the mobility pattern correlations. Relying on a Markov state-space model, dynamic behaviors of robot's communication state and movement are formulated. A two-stage sequential Bayesian scheme, premised on random finite set (RFS), is developed to detect and estimate the involved unknown states. Specifically, in order to counteract the probability likelihood disappearance (caused by no information emission) and improve robustness to ambient noise, a sequential pre-filtering technique is designed, which can refine local observations and thereby significantly improve the accuracy of the system. We validate the proposed MDE scheme via both theoretical analysis and numerical simulations, demonstrating it would improve both the detection and estimation accuracy and efficiency.
