Asynchronous Pulse Code Multiple Access (APCMA) has been proposed as a pulse-based LPWA technology for massive IoT, offering robustness against collisions. However, in ultra-dense environments, pulse overlaps cause misdetections. This paper proposes an enhanced CSS-APCMA decoding method that leverages per-message chirp frequency-offset consistency and accounts for clock jitter. The receiver was implemented using a USRP and GNU Radio, and large-scale experiments were conducted with 1,000 transmitters operating in the 920 MHz band. The results demonstrate that the proposed method maintains a high reception rate while significantly suppressing misdetections and achieving higher data rates compared to conventional CSS-APCMA in ultra-dense environments.

For the realization of an Internet of Things (IoT) with high densities of nodes it is necessary that wireless communication protocols are developed that offer (1) low energy consumption, (2) simplicity of encoding and decoding, (3) an asynchronous mode of communication, and (4) an effective but simple method to deal with interference between transmissions. This paper presents the implementation, experimentation, and analysis of a protocol on the MAC sublayer that is based on the encoding of information by the silent intervals between pulses. This encoding allows for few conflicts between messages that are broadcast on the same band overlapped in time. This is demonstrated experimentally, while no adverse effect is detected on transmissions from broadcasts of a different group of nodes using the same 315 MHz band but with a different code length as coding parameter. Theoretical analysis confirms that the probability of conflicts between messages is low, even if the number of nodes increases to the order of ten thousand. This protocol facilitates the implementation of IoT nodes that are restricted in terms of hardware and energy resources.

Asynchronous Boolean totalistic cellular automata have recently attracted attention as promising models for implementation by reaction-diffusion systems. It is unknown, however, to what extent they are able to conduct computation. In this paper, we introduce the so-called non-camouflage property, which means that a cell's update is insensitive to neighboring states that equal its own state. This property subsumes the Boolean totalistic property, which signifies the existence of states in a cell's neighborhood, but is not concerned with how many cells are in those states. We argue that the non-camouflage property is extremely useful for the implementation of reaction-diffusion systems, and we construct an asynchronous cellular automaton with this property that is Turing-complete by directly simulating Turing machines. We also construct another asynchronous cellular automaton, but this model incorporates the so-called freezing property [1], which restricts the direction of transitions of each cell to one-way. We show that this model is Turing-complete, since it can simulate the temporal evolution of elementary cellular automata. These results indicate the feasibility of computation by reaction-diffusion systems.

The Internet of Nano-Things will employ wireless sensor nodes that are not only extremely small in size and large in number but also severely restricted in their power supply. While a large part of the design of such networks will rely on the engineering of physical aspects, such as developing technology for efficient energy harvesting, sensing, and communication, a significant role will also be played by the algorithms underlying the operation of nodes to ensure that optimal use is made of the limited supply of energy. This paper discusses concepts that will facilitate the design of such algorithms, such as spike-based encoding of signals and data aggregation through gossiping. We propose a method to extract information from a sensor network using nonpacket spike-based communication with local interactions between nodes. Rather than gathering sensor values from each individual node and sending them to a central destination node, measurements from the nodes are processed through the interactions between nodes, while diffusing in the network, to obtain an integrated value characteristic for all nodes' measurements.

This paper presents a nonlinear model of the human brain activity response to visual stimuli according to Blood-Oxygen-Level-Dependent (BOLD) signals scanned by functional Magnetic Resonance Imaging (fMRI). A BOLD signal usually contains a low frequency signal component (trend), which is often ignored by the existing models or removed by approximation methods. However, such detrending could also destroy the dynamics of the BOLD signal and miss an important response. This paper shows a model that, in the absence of detrending, can predict the BOLD signal with smaller errors than existing models. For detrending, the presented model has also a lower Schwarz information criterion than existing models, which implies that the presented model will be less likely to overfit the experimental data.

Random fluctuations will be a major factor interfering with the operation of nanometer scale electronic devices. This article presents circuit architectures that can exploit such fluctuations, if signals have a particle-like (discrete, token-based) character. We define an abstract circuit primitive that, though lacking functionality when used with fluctuation-free signals, becomes universal when fluctuations are allowed. Key to the power of a signal’s fluctuations is the ability to explore the state space of a circuit. This ability is used to resolve deadlock situations, which could otherwise only be averted by increased design complexity. The results in this article suggest that in the design of future computers, signal fluctuations, rather than being an impediment to be avoided at any cost, may be an important ingredient to achieve efficient operation.

We examine the timing dependence of nanophotonic devices based on optical excitation transfer via optical near-field interactions at the nanometer scale. We theoretically analyze the dynamic behavior of a two-input nanophotonic switch composed of three quantum dots based on a density matrix formalism while assuming arrival-time differences, or skew, between the inputs. The analysis reveals that the nanophotonic switch is resistant to a skew longer than the input signal duration, and the tolerance to skew is asymmetric with respect to the two inputs. The skew dependence is also experimentally examined based on near-field spectroscopy of InGaAs quantum dots, showing good agreement with the theory. Elucidating the dynamic properties of nanophotonics, together with the associated spatial and energy dissipation attributes at the nanometer scale, will provide critical insights for novel system architectures.

Asynchronous cellular automata (ACAs) have much promise as architectures for future computers with molecular-scale devices, since they are less likely to suffer from clock-related problems (i.e. wiring overhead, heat dissipation, etc.) and they are suitable for bottom-up manufacturing techniques due to their homogeneous structures. Computation on ACA can be accomplished by designing configurations on the cell space such that their evolutions emulate the operations of asynchronous circuits. However, these ACA models still require tens of transition rules, which may seem too high for efficient physical realizations. In this paper, we present a new cellular automaton in which each cell comprises a group of simple sub-cells that are locally coupled with each other. Based upon an effective set of asynchronous primitive operators, this novel ACA can be used to construct any arbitrary logic circuit, while the number of transition rules is less than half the number in previous ACA models.

Nanometer-scale structures suitable for computing have been investigated by several research groups in recent years. A common feature of these structures is their dynamic evolution through cascaded local interactions embedded on a discrete grid. Finding configurations capable of conducting computations is a task that often requires tedious experiments in laboratories. Formal methods—though used extensively for the specification and verification of software and hardware computing systems—are virtually unexplored with respect to computational structures at atomic scales. This paper presents a systematic approach toward the application of formal methods in this context, using techniques like abstraction, model-checking, and symbolic representations of states to explore and discover computational structures. The proposed techniques are applied to a system of CO molecules on a grid of Copper atoms, resulting in the design of a complete library of combinational logic gates based on this molecular system. The techniques are also applied on (more general) systems of cellular automata that employ an asynchronous mode of timing. The use of formal methods may narrow the gap between Physical Chemistry and Computer Science, allowing the description of interactions of nanometer scale systems on a level of abstraction suitable to devise computing devices.

We propose a self-replicating machine that is embedded in a two-dimensional asynchronous cellular automaton with von Neumann neighborhood. The machine dynamically encodes its shape into description signals, and despite the randomness of cell updating, it is able to successfully construct copies of itself according to the description signals. Self-replication on asynchronously updated cellular automata may find application in nanocomputers, where reconfigurability is an essential property, since it allows avoidance of defective parts and simplifies programming of such computers.

The continuous minituarization of integrated circuits may reach atomic scales in a couple of decades. Some researchers have already built simple computation engines by manipulating individual atoms on metal surfaces. This paper presents a systematic approach to automate the design of logic gates using molecule cascades. Temporal logic is used to characterize molecular interactions and specify the behavior of logic gates. Model-checking techniques are used for the exploration of structures behaviorally equivalent to the logic gates. As an example, a complete library of combinational logic gates has been designed using a particular molecular system. This new approach provides a methodology to bridge the gap between physical chemists and computer scientists in seeking computational structures at atomic scales.

Delay-insensitive (DI) circuits are a class of asynchronous circuits whose correctness of operation is robust to arbitrary delays in modules or interconnection lines. Keller clarified the precise operating conditions of the class of DI-circuits and presented a universal set of primitive modules from which any circuit in the class is realizable. Later, Patra and Fussell presented an alternative universal set of primitive modules and claimed that there is no universal set of primitives satisfying Keller's conditions in which the largest number of input and output lines of each primitive module is less than five. We present new types of primitive modules, each having at most three input and output-lines and show they form a universal set of primitives. We achieve this reduction in complexity by allowing the input and output-lines of modules to be bidirectional and to be able to buffer signals. The use of buffers in interconnection lines allows higher throughput of signals and results in circuits requiring less feedback lines, thus improving the efficiency of DI-circuits. The proposed class of Dl-circuits is especially useful for implementations on cellular automata - an architecture that promises efficient implementations and manufacturing in nanotechnology due to its regular structure.

In nonstationary environments, it is difficult to apply traditional genetic algorithms (GAs) because they use strong selection pressure and lose the diversity of individuals rapidly. We propose a GA with neutral variation that can track environmental changes. The idea of this GA is inspired by Kimura's neutral theory (1983). The scheme of this GA allows neutral characters, which do not directly affect the fitness with respect to environments, thus increasing the diversity of individuals. In order to demonstrate the properties of this GA, we apply it to a permutation problem called the ladder-network, of which the imposed alignment on the output changes regularly. We show that the GA with neutral variation can adapt better to environmental changes than a traditional GA.

This paper proposes a linear neural network for principal component analysis whose weight vector lengths converge to the variances of the principal components in the input data. The neural network breaks the symmetry in its learning process by the differences in weight vector lengths and, as opposed to other linear neural networks described in literature, does not need to assume any asymmetries in its structure to extract the principal components. We prove the asymptotic stability of a stationary solution of the network's learning equation. Simulations show that the set of weight vectors converge to this solution. Comparison of convergence speeds shows that in the simulations the proposed neural network is about as fast as Sanger's generalized Hebbian algorithm (GHA) network, the weighted subspace rule network of Oja et al., and Xu's LMSER network (weighted linear version).

This paper proposes a neural network that stores and retrieves sparse patterns categorically, the patterns being random realizations of a sequence of biased (0,1) Bernoulli trials. The neural network, denoted as categorizing associative memory, consists of two modules: 1) an adaptive classifier (AC) module that categorizes input data; and 2) an associative memory (AM) module that stores input patterns in each category according to a Hebbian learning rule, after the AC module has stabilized its learning of that category. We show that during training of the AC module, the weights in the AC module belonging to a category converge to the probability of a "1" occurring in a pattern from that category. This fact is used to set the thresholds of the AM module optimally without requiring any a priori knowledge about the stored patterns.

A measure that equips competitive learning neural networks with noise suppressing capabilities in the learning phase is presented. Analysis shows that weight vectors of neural networks employing the measure are effectively protected from being trained by much shorter (and noisy) input vectors. An ART2a-like scheme is equipped with the measure, while omitting the typical noise-reduction and contrast-enhancement mechanisms of ART2a. Experiments show that this scheme is more robust to noise in the learning phase than ART2a.