SOCIAL MEDIA AND SOCIAL RELATIONSHIPS: A CASE STUDY IN KURDISTAN SOCIETY

These days, Social Media which is includes (Facebook, Instagram, Twitter, Linkedin) is an extremely well known social correspondence media. Indi-viduals use Social Media to express their musings, thoughts, sonnets, and distresses on them. In the period of data superhighway, greater part of the young people are not sharing their challenges, issues, irregularity, power-lessness and disappointment with their folks in Kurdistan of Iraq. Be that as it may, they share with their companions on Social Media. Hence, their companions are making remarks, giving havens and affections to them. Because of absence of instruction and encounters on innovation, gatekeepers in Kurdistan don't know about the correspondences and addictions on social Medias. In this manner, there are producing holes in social relationships in the community. In this paper, a review has based and finding the effect of social media on personal and community relationships. Calculation dissects the practices of youngsters' by gathering data from a survey. Guardians and educators conclusions are additionally viewed as about the exercises of understudies on home and foundations. Here, age cutoff points of focused adolescents are somewhere in the range of 16 and 60. From this investigation, powerless connection amongst guardians and their adolescent youngsters have been taken note. The significant issue was that teenagers are investing more energy on social media and guardians need them to the table amid contemplate time and educational time

Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability

Homeostatic Fault Tolerance in Spiking Neural Networks : A Dynamic Hardware Perspective

Fault tolerance is a remarkable feature of biological systems and their self-repair capability influence modern electronic systems. In this paper, we propose a novel plastic neural network model, which establishes homeostasis in a spiking neural network. Combined with this plasticity and the inspiration from inhibitory interneurons, we develop a fault-resilient robotic controller implemented on an FPGA establishing obstacle avoidance task. We demonstrate the proposed methodology on a spiking neural network implemented on Xilinx Artix-7 FPGA. The system is able to maintain stable firing (tolerance ±10%) with a loss of up to 75% of the original synaptic inputs to a neuron. Our repair mechanism has minimal hardware overhead with a tuning circuit (repair unit) which consumes only three slices/neuron for implementing a threshold voltage-based homeostatic fault-tolerant unit. The overall architecture has a minimal impact on power consumption and, therefore, supports scalable implementations. This paper opens a novel way of implementing the behavior of natural fault tolerant system in hardware establishing homeostatic self-repair behavior

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings