Maximum Likelihood Detection for Cooperative Molecular Communication

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter's symbol that is more likely, given the likelihood of the observations from multiple receivers (RXs). We propose three different ML detection variants according to different constraints on the information available to the FC, which enables us to demonstrate trade-offs in their performance versus the information available. The system error probability for one variant is derived in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that majority rule detection has performance comparable to ML detection when the reporting is noisy.Comment: 7 pages, 4 figurs. This work has been accepted by the IEEE ICC 201

Monolayer doping of bulk and thin body group IV semiconductors

The turn of the new year from 2019-2020 has brought us into a new decade with an unforeseen worldwide halt to what was previously considered “normal” life, due to a virus (coronavirus-19) with dimensions measured by scanning electron microscopy (SEM) to be in the nanometre range. This has emphasized the importance for the general public of acknowledging particles and materials in this nanometre range which cannot be seen without electron microscopy. Some of the technology being used to fight these viruses, such as ventilators, operate using electronics which contain semiconductor materials. Since the mid 1900 s the size of these electronics has decreased while doubling their quantity of transistors in line with Moore’s law. This has allowed for increased performance with lower power consumption. Scaling of metal-oxide-semiconductor field effect transistors (MOSFETs) has progressed from the original micrometre range to current sub-10 nm dimensions, while also moving from planar to 3-dimensional (3-D) architectures. However, increasing difficulty has been found with these new and reduced material dimensions. All fabrication processes are stressed, but doping has particularly found limitations in this region. High concentrations of dopant atoms are required at increasingly shallow depths, while maintaining the crystalline integrity of the planar or 3-D doped substrate. Traditional methods of introducing these dopant atoms, such as ion implantation, have found difficulty with damage production and conformality on state-of-the-art applications. Monolayer doping, which is a method of semiconductor doping through chemical functionalisation of the target substrate with the required dopant-containing molecules, has shown promise as an alternative method for this state-of-the-art doping.The aim of this thesis is to study the potential of monolayer doping for application to materials used in current and future transistor devices

The influence of network topology on reverse-engineering of gene-regulatory networks

AbstractModeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern computational biology investigations into gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from time-course gene expression data. Common mathematical formalisms used to represent such models capture both the relative weight or strength of a regulator gene and the type of the regulator (activator, repressor) with a single model parameter. The goal of this study is to quantify the role this parameter plays in terms of the computational performance of the reverse-engineering process and the predictive power of the inferred GRN models. We carried out three sets of computational experiments on a GRN system consisting of 22 genes. While more comprehensive studies of this kind are ultimately required, this computational study demonstrates that models with similar training (reverse-engineering) error that have been inferred under varying degrees of a priori known topology information, exhibit considerably different predictive performance. This study was performed with a newly developed multiscale modeling and simulation tool called MultiGrain/MAPPER

Suspect Fits Description: Responses to Racial Profiling in New York City

A panel discussion with Darius Charney, Jesus Gonzalez, David Kennedy, Noel Leader, and Robert Perry. September 29, 201

Convex optimization of distributed cooperative detection in multi-receiver molecular communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N -out-of- K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance

Convex optimization of distributed cooperative detection in multi-receiver molecular communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N -out-of- K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance

Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, the transmitter (TX) sends a common information symbol to multiple receivers (RXs) and a fusion center (FC) chooses the TX symbol that is more likely, given the likelihood of its observations from all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. Three ML detection variants are proposed according to different RX behaviors and different knowledge at the FC. The system error probabilities for two ML detector variants are derived, one of which is in closed form. The optimal molecule allocation among RXs to minimize the system error probability of one variant is determined by solving a joint optimization problem. Also for this variant, the equal distribution of molecules among two symmetric RXs is analytically shown to achieve the local minimal error probability. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of simpler, non-ML cooperative variants and demonstrate that these simpler cooperative variants have error performance comparable to ML detectors.Comment: 15 pages, 7 figures; submission for possible IEEE publication. arXiv admin note: text overlap with arXiv:1704.0562