Nano Communication Networks最新文献

This manuscript presents a novel model for generating synthetic data for a biological molecular communication (MC) system to train a Neural Network (NN) for the purpose of discriminating transmitted bits. To achieve this, a deep learning algorithm was trained using the synthetic data and tested against experimentally measured data. The polynomial curve fitting coefficients are chosen as features. The featurization stage is followed by a NN that captures different aspects of the temporal correlation of the received signals. The real data was collected from an MC testbed that employed transfected Escherichia coli (E. coli) bacteria expressing the light-driven proton pump gloeorhodopsin from Gloeobacter violaceus. By stimulating the bacteria with externally controlled light, protons were secreted, which changed the pH level of the environment. A pH detector was then used to measure the pH of the environment. We propose the use of a deep convolutional neural network to detect the transmitted bits. This paper discusses the data augmentation, processing, and NNs that are pertinent to practical MC problems. The trained algorithm demonstrated an accuracy of over 99.9% in detecting transmitted bits from received signals at a bit rate of 1 bit/min, without requiring any specific knowledge of the underlying channel.

Metal oxide semiconductor (MOS) technology has reached its maximum profitable limits due to increase in leakage current, static power dissipation, limited switching frequency. One of the better solutions to overcome these problems is the quantum-dot cellular automata (QCA) technology, it boasts the absence of physical transportation charges, relying solely on Coulombic force for interaction between the cells also it is a transistor less technology does not require any external voltage bias. In the current integrated circuits, the data being transferred more than ever, the error correction and coding techniques find significance in reliable data communication. Recognizing the increasing importance of error correction in data communication, particularly with the widespread data transfer, this research specifically focuses on the implementation of an enhanced convolutional encoder using QCA for error correction in data transmission. Comparative study with the state-of-art is also carried out to examine performance of proposed design. As a result of our study, we were able to reduce the cell count by 33.34% and power dissipation is reduced by 77% with the proposed 1/2 rate encoder and the proposed 1/3 rate encoder has 15.9% less cell count and power dissipation is reduced by 72% as compared to existing design.

As an emerging nanotechnology, Quantum-dot Cellular Automata (QCA) has attracted extensive attentions due to its characteristics of high density, high speed, and low energy consumption. Modern high-performance System-on-Chips (SoCs) with multiple processors require interconnection networks to connect each core for improving data throughput and reducing latency, while the crossbar network is broadly used as a non-blocking interconnection architecture with high efficiency. In this paper, a method to design an efficient QCA-based N × N crossbar network utilizing optimally designed N:1 multiplexers (MUXs) is proposed, followed by a multi-layer and a single-layer implementation of its 8 × 8 design. A simplified matrix model is then introduced to provide a concise and intuitive switch control strategy, and expressions for cell count, area, latency, QCA cost, and QCA complexity of the proposed crossbar networks are derived according to the size N. Experimental results manifest that the proposed 8 × 8 crossbar networks have significant advantages on most performance parameters compared with other existing QCA-based networks.

The widespread use of graphene patch antennas is escalating as evidence of their applicability in areas like 6 G communications and THz spectroscopy. Geometric uncertainty and fabrication issues while downsizing makes terahertz antenna design problematic. Graphene's electromagnetic and mechanical qualities make it ideal for miniaturizing antennas for Terahertz use. In the THz spectrum, a graphene antenna requires careful dielectric material selection since performance fall, especially efficiency. This paper compares dual band multi-layered genetic algorithm-based optimized antennas for the THz applications, especially spectroscopy and 6 G utilizing sole layer duple substrates concept, i.e., two distinct substrates at the same level between the ground and patch. Different antennas are designed using various substrates like Rogers RO3010, RO3210, RT5880, RT5880LZ, TMM 13i, Taconic TLY-3, RF-10, Silicon, & Teflon. Two segments of four antennas are planned; one has silicon as a common substrate with four additional materials, and another has Teflon. The proposed antenna's performance is assessed in terms of bandwidth, beamwidth, directivity, efficiency, gain, radiation pattern, return loss, and VSWR. The results reveal that Silicon and Rogers RT5880 LZ substrates-based antenna perform better in a segment I, with bandwidth (GHz) of 150.1 and 156.9, directivity (dBi) of 5.93 and 4.23, efficiency (%) of 76.65 and 78.98, and gain (dB) of 4.97 and 3.3. While in segment II, Teflon and Taconic RF-10-based antenna perform better with features 158 and 198 bandwidth, 6.43 and 4.43 directivity, 74 and 83 efficiency, and 4.67 and 3.65 gain.

This paper presents a significant contribution to the field of nanoscale computing by proposing an innovative reversible Arithmetic and Logic Unit (ALU) implemented in Quantum-Dot Cellular Automata (QCA). Reversible logic and QCA technology offer promising alternatives to conventional CMOS technology, addressing the challenges of operating at nanoscale dimensions. The primary objective is to develop a highly efficient ALU capable of performing 26 distinct arithmetic and logical operations. The ALU design is based on a novel reversible full adder-subtractor optimized for minimal quantum cost, which is crucial for energy-efficient quantum computation. The evaluation encompasses various criteria related to reversibility, such as gate count, number of constant inputs, number of garbage outputs, and quantum cost. QCA-specific criteria, including cell count, occupied area, and clock cycles, are also considered. The outcomes of this research contribute to the advancement of cell-efficient nanoscale computing, with implications for quantum computation, emerging technologies, and future integrated circuit design.

Nanodevices are the focus of research enhancing the detection and treatment of diseases in the human body. Focusing on the scenario where nanosensors are flowing with the blood in the human circulatory system (HCS), in this work, we investigate a model to predict their distribution along the various vessel segments. Although various approaches report solutions for localizing nanosensors in the body, it is also relevant to derive their stationary distribution along the vessel segments as a prior step to assess their actuation and sensing capabilities in the body. We use a Markov chain formulation to derive the stationary distribution of nanosensors. We evaluate the transition probabilities relying on the representation of vessels with electric circuit components. We implement the electric circuit representation of the left ventricle in the heart and the arteries to find the blood flow at vessel bifurcations and then compute the Markov chain probabilities. Our system also allows to reveal the dynamics of the movement of nanosensors with the human activity. We illustrate results in two regimes, as low and high activity, to mimic the case when being at rest or doing sports.

In the evolving landscape of terahertz communication, the behavior of channels within indoor environments, particularly through glass doors, has garnered significant attention. This paper comprehensively investigates terahertz channel performance under such conditions, employing a measurement setup operational between 113 and 170 GHz. Analyzing scenarios frequently induced by human activity and environmental factors, like door movements, we established a comprehensive theoretical model. This model seamlessly integrates transmission, reflection, absorption, and diffraction mechanisms, leveraging the Fresnel formula, multi-layer transmission paradigm, and knife-edge diffraction theory. Our experimental results and theoretical predictions harmoniously align, revealing intricate dependencies, such as increased power loss at higher frequencies and larger incident angles. Furthermore, door interactions, whether opening or oscillations, significantly impact the terahertz channel. Notably, door edges lead to a power blockage surpassing the transmission loss of the glass itself but remaining inferior to metallic handle interferences. This paper's insights are pivotal for the design and fabrication of terahertz communication systems within indoor settings, pushing the boundaries of efficient and reliable communication.

The application of DNA monitoring has recently expanded into the information technology realm of DNA computing and storage systems, leveraging its capabilities for molecular computing and high-density data storage. Essential to this advancement are multi-color fluorescent signals attached to informational DNA, enabling simultaneous multidata reading in the DNA memory or storage system. Various fluorescent detection techniques, such as real-time polymerase chain reaction, next-generation sequencing, and fluorescence resonance energy transfer, have been investigated for DNA reactions and sequence data. Although proximity (less than 10 nm) between different fluorescent signals can lead to interference, controlling the distance of fluorescent molecules attached to DNA is a feasible solution. This study demonstrates a DNA molecular memory system using multiple fluorescent molecules. We examined the independent hybridization of three different fluorescent DNA molecules to DNA templates with three sites for fluorescent attachment on 17 nt DNAs. The study focused on two multi-bit DNA molecules hybridized to the template DNA, assessing their fluorescence emission intensities at various excitation wavelengths. Two multi-bit DNA molecules, which were hybridized onto the template DNA, were investigated for fluorescence emission intensities by various excitation wavelengths. Although the emission intensities of the two multi-bit DNA molecules were not significantly increased by another fluorescent molecule, each excitation wavelength has provided more effective emission intensity levels for DNA signal detection. Furthermore, we developed a three-bit DNA molecular memory system using triple-level DNA molecules. These multi-color DNA systems could be extended to arithmetic and logical computing.

To meet the needs of the market launch, placement and routing(P&R) algorithms for conventional circuits have started to adopt a hierarchical design, divide and conquer philosophy for the layout of VLSI circuits. Quantum-dot cellular automata (QCA) circuits are considered a solution to overcome the limitations of Moore’s Law. However, the current automated design of QCA circuits is still in its preliminary stages. This paper uses a hierarchical structure, which borrows from traditional circuits that should be laid out in large-scale circuits, to hierarchically process QCA circuits, laying them out layer by layer while using the A* algorithm for wiring to find feasible solutions. The algorithm is implemented using the C++ programming language, and simulation results verify the correctness of the algorithm.

In quorum sensing (QS), bacteria exchange information by using molecular signals to work together. In this paper, we study diffusion-based molecular communication with the QS mechanism between the transmitter node and receiver node which are composed of a population of mobile bacteria in a cluster, respectively. The expression of average bit error probability (BEP) at a receiver bacterium is derived. Furthermore, we use the projected gradient descent (PGD) algorithm to solve the optimization problem whose objective is to minimize the average BEP under emission rate constraints which require that the emission rate of each transmitter bacterium has lower and upper bounds. Finally, the numerical results show the PGD algorithm has good convergence behaviors and it is more efficient in finding the optimal emission rate with fewer iterations than genetic algorithm. The obtained results are expected to provide guidance in designing QS-based molecular communication system with lower average BEP.