Effect of Neuromodulation of Short-Term Plasticity on Information Processing in Hippocampal Interneuron Synapses

Neurons convey information about the complex dynamic environment in the form of signals. Computational neuroscience provides a theoretical foundation toward enhancing our understanding of nervous system. The aim of this dissertation is to present techniques to study the brain and how it processes information in particular neurons in hippocampus. We begin with a brief review of the history of neuroscience and biological background of basic neurons. To appreciate the importance of information theory, familiarity with the information theoretic basics is required, these basics are presented in Chapter 2. In Chapter 3, we use information theory to estimate the amount of information postsynaptic responses carry about the preceding temporal activity of hippocampal interneuron synapses and estimate the amount of synaptic memory. In Chapter 4, we infer parsimonious approximation of the data through analytical expression for calcium concentration and postsynaptic response distribution when calcium decay time is significantly smaller that the interspike intervals. In Chapter 5, we focus on the study and use of Causal State Splitting Reconstruction (CSSR) algorithm to capture the structure of the postsynaptic responses. The CSSR algorithm captures patterns in the data by building a machine in the form of visible Markov Models. One of the main advantages of CSSR with respect to Markov Models is that it builds states containing more than one histories, so the obtained machines are smaller than the equivalent Markov Model

Development of an intelligent safety gear system for high-rise elevators

Elevators have been a key element of buildings, especially tall buildings, since their widespread use began in the 19th century. As a matter of fact, high-rise buildings would not have existed without elevators. Elevators have a myriad of safety features and devices to ensure a safe journey for the passengers. One of these devices is the safety gear. Safety gears are emergency brakes that stop speeding elevators by gripping the guide rails. They are adjusted for a safe deceleration range by the technician during installation and exert a constant force. Due to their purely mechanical nature, once triggered, the safety gear is currently unable to actively adjust the braking force to counteract vibrations, to decelerate at different rates, or to stop the elevator at the closest landing. Therefore, the emergency braking event can be harsh and noticeable, leaving the passengers stuck in the elevator shaft after the braking event. This thesis aims to develop an intelligent safety gear system that is able to bring the elevator to a stop with a safe and adjustable deceleration rate. This was achieved by first, modeling a computer simulation of a small-scale elevator to be able to quickly simulate different braking event scenarios. Second, a small-scale elevator test rig was constructed to test the computer simulation with physical components. The test rig was validated by comparing its results with KONE’s high-rise safety gear test. The control system developed was able to safely stop the moving mass with the desired deceleration and a great deal of control over other parameters. Further development of the system could lead to a safer, more comfortable, and energy efficient elevator ride

Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources

Task Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system's performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation

MAKER: Candy Crane Robot

Candy Crane is a custom-made robot that looks like a traditional tower crane and is used to pick candies by the young users for fun and for learning mechatronics product design. The tower structure is made from either aluminum C-channels or plastics made from 3D printer. Two 12V DC motors and several limit switches are used to control the movement of the crane. A miniature crane to be held by the user is equipped with a linear potentiometer and rotary potentiometer and is used to control the movement of the big tower crane wirelessly through a blue tooth module. An Arduino microcontroller is used as a master to send the movement commands from the miniature crane to the Lego Mindstorm’s NXT Brick mounted on Candy Crane. The NXT Brick serves as a slave to relay the commands to drive the DC motors to place the hook to proper location. Once the hook is in the right position, the user can send the hook down to pick up the candy of his/her choice. The user can learn mechanical design, electronic design and programming from the mechatronic toy

IMPROVEMENT OF LOADABILITY IN DISTRIBUTION SYSTEM USING GENETIC ALGORITHM

Abstract -Generally during recent decades due to development of power systems, the methods for delivering electrical energy to consumers, and because of voltage variations is a very important proble

Entanglement Transfer via XXZ Heisenberg chain with DM Interaction

The role of spin-orbit interaction, arises from the Dzyaloshinski-Moriya anisotropic antisymmetric interaction, on the entanglement transfer via an antiferromagnetic XXZ Heisenberg chain is investigated. From symmetrical point of view, the XXZ Hamiltonian with Dzyaloshinski-Moriya interaction can be replaced by a modified XXZ Hamiltonian which is defined by a new exchange coupling constant and rotated Pauli operators. The modified coupling constant and the angle of rotations are depend on the strength of Dzyaloshinski-Moriya interaction. In this paper we study the dynamical behavior of the entanglement propagation through a system which is consist of a pair of maximally entangled spins coupled to one end of the chain. The calculations are performed for the ground state and the thermal state of the chain, separately. In both cases the presence of this anisotropic interaction make our channel more efficient, such that the speed of transmission and the amount of the entanglement are improved as this interaction is switched on. We show that for large values of the strength of this interaction a large family of XXZ chains becomes efficient quantum channels, for whole values of an isotropy parameter in the region $-2 \leq \Delta \leq 2$.Comment: 21 pages, 9 figure