An integrated system for quantitatively characterizing different handgrips and identifying their cortical substrates

Motor recovery of hand function in stroke patients requires months of regular rehabilitation therapy, and is often not measured in a quantitative manner. The first goal of this project was to design a system that can quantitatively track hand movements and, in practice, related changes in hand movements over time. The second goal of this project was to acquire hand and finger movement data during functional imaging (in our case we used magnetoencephalography (MEG)) to be used for characterizing cortical plasticity associated with training. To achieve these goals, for each hand, finger flexion and extension were measured with a data glove and wrist rotation was calculated using an accelerometer. To accomplish the first goal of the project, we designed and implemented Matlab algorithms for the acquisition of behavioral data on different handgrips, specifically power and precision grips. We compiled a set of 52 objects (26 man-made and 26 natural), displayed one at the time on a computer screen, and the subject was asked to form the appropriate handgrip for picking up the object image presented. To accomplish the second goal, we used the setup described above during an MEG scanning session. The timescales for the signals from the glove, accelerometer, and MEG were synchronized and the data analyzed using Brainstorm. We validated proper functionality of the system by demonstrating that the glove and accelerometer data during handgrip formation correspond to the appropriate neural responses

An integrated system for quantitatively characterizing different handgrips and identifying their cortical substrates

Motor recovery of hand function in stroke patients requires months of regular rehabilitation therapy, and is often not measured in a quantitative manner. The first goal of this project was to design a system that can quantitatively track hand movements and, in practice, related changes in hand movements over time. The second goal of this project was to acquire hand and finger movement data during functional imaging (in our case we used magnetoencephalography (MEG)) to be used for characterizing cortical plasticity associated with training. To achieve these goals, for each hand, finger flexion and extension were measured with a data glove and wrist rotation was calculated using an accelerometer. To accomplish the first goal of the project, we designed and implemented Matlab algorithms for the acquisition of behavioral data on different handgrips, specifically power and precision grips. We compiled a set of 52 objects (26 man-made and 26 natural), displayed one at the time on a computer screen, and the subject was asked to form the appropriate handgrip for picking up the object image presented. To accomplish the second goal, we used the setup described above during an MEG scanning session. The timescales for the signals from the glove, accelerometer, and MEG were synchronized and the data analyzed using Brainstorm. We validated proper functionality of the system by demonstrating that the glove and accelerometer data during handgrip formation correspond to the appropriate neural responses

LIGHTLIKE SUBMANIFOLDS OF AN INDEFINITE LORENTZIAN PARA-SASAKIAN STATISTICAL MANIFOLD

In this paper, we introduce an indefinite LP-Sasakian statistical manifold and study lightlike submanifold of an indefinite LP-Sasakian statistical manifold. We also introduce some relations among induced geometrical objects with respect to dual connections in a lightlike submanifold of an indefinite LP-Sasakian statistical  manifold. One example related to this concept is also presented. Finally, we show that an invariant lightlike submanifold of an indefinite LP-Sasakian  statistical manifold is an indefinite LP-Sasakian statistical manifold.

Denial of Service: Techniques of Attacks and Mitigation

As cloud computing technology has many advantages but cloud security or cloud software security threats and attacks at various levels are also a big concern of all the organizations. Those systems are connected to the internet in the cloud network can be effected by different types of attacks and one of the prominent attack is DoS (denial of service) attack. DoS attack has been considered as one of the important security threat in cloud computing systems at various level that has proven difficult to alleviate. This attack perpetrated in many ways such as consuming computational resources, disruption of information and obstructing the communication media. Once the attack is successful in consuming resources on the victim computers, the attacker then could control and direct them to attack as a group. This means DoS attack also allows the attacker to get the administrative control of the systems. Dos attack can be launched for sending the flood or crashes the services of the system. In this paper, we present the different types of DoS attacks and techniques for launched the DoS attack at various level and the techniques applied to mitigate the harmful effects of the DoS attack