Border security and surveillance System using IoT

Security along the international border is a critical process in security assessment; It must be exercised the 24x7. With the advancements in wireless IoT technology, it has become much easier to design, develop and deploy a cost-effective, automatic and efficient system for intrusion detection in the context of surveillance. This paper set up to set up the most efficient surveillance solution, we propose a Border Surveillance Systems and sensitive sites. this surveillance and security system is to detect and track intruders trespassing into the monitoring area along the border, it able which triggers off precocious alerts and valuation necessary for the catch of efficient measurements in case of a threat. Our system is based on the classification of the human gestures drawn from videos envoy by Drones equipped with cameras and sensors in real-time. All accomplished experimentation and acquired results showed the benefit diverted from the use of our system and therefore it enables our soldiers to watch the borders at each and every moment to effectively and at low cost.

COVID – 19 OUTBREAK ANALYSIS ON THE BASIS OF MACHINE LEARNING

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Covid-19, SIRD model, Linear Regression, XGBoost, Random Forest Regression, SVR, LightGBM, Machine learning, Intervention.

HAND GESTURE RECOGNITION IN REAL-TIME

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Tensor Flow, Machine learning, React js, handmark model, media pipeline

Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers

Human gestures have been considered as one of the important human-computer interaction modes. With the fast development of wireless technology in urban Internet of Things (IoT) environment, Wi-Fi can not only provide the function of high-speed network communication but also has great development potential in the field of environmental perception. This paper proposes a gesture recognition system based on the channel state information (CSI) within the physical layer of Wi-Fi transmission. To solve the problems of noise interference and phase offset in the CSI, we adopt a model based on CSI quotient. Then, the amplitude and phase curves of CSI are smoothed using Savitzky-Golay filter, and the one-dimensional convolutional neural network (1D-CNN) is used to extract the gesture features. Then, the support vector machine (SVM) classifier is adopted to recognize the gestures. The experimental results have shown that our system can achieve a recognition rate of about 90% for three common gestures, including pushing forward, left stroke, and waving. Meanwhile, the effects of different human orientation and model parameters on the recognition results are analyzed as well.

Human Computer Interaction – Hand Gesture Recognition

The creation of intelligent and natural interfaces between users and computer systems has received a lot of attention. Several modes of knowledge like visual, audio, and pen can be used individually or in combination have been proposed in support of this endeavour. Human communication relies heavily on the use of gestures to communicate information. Gesture recognition is a subject of science and language innovation that focuses on numerically quantifying human gestures. It is possible for people to communicate properly with machines using gesture recognition without the use of any mechanical devices. Hand gestures are a form of nonverbal communication that can be applied to several fields, including deaf-mute communication, robot control, human–computer interaction (HCI), home automation, and medical applications. Many different methods have been used in hand gesture research papers, including those focused on instrumented sensor technology and computer vision. To put it another way, the hand sign may be categorized under a variety of headings, including stance and motion, dynamic and static, or a combination of the two. This paper provides an extensive study on hand gesture methods and explores their applications.

Auditory feedback on proprioception increases bodily awareness

Multiple studies have shown the importance of movement and physical exercise like dance for human wellbeing and mental health. Yet, factors influencing proprioception and body awareness in the context of exercise remain largely unexplored. This is mostly due to the lack of tools and techniques to record, manipulate and intervene on body awareness during real-time movements. To this end, we designed FUGA, a wearable device delivering continuous real-time auditory feedback on human gestures. Here we tested whether we could manipulate bodily awareness during physical exercise and dance using auditory feedback on proprioception. Following a within-subject design, we tested the effects of the device using different sounds in three populations of dancers: novice, amateurs and professionals. We found that across populations the wearable had a significant effect on the participant’s rating of feelings of bodily awareness, reward, immersion, embodiment, and self-efficacy. We discuss these results in the light of recent theories of predictive coding and active inference, emphasizing the role of action, proprioceptive and auditory sensory feedback in human behavior. Building upon these results, we suggest future studies to explore the potential of auditory proprioceptive feedback for mental health.

Animals in Human Situations in Ancient Egyptian Ostraca and Papyri

It has been said that the ancient Egyptians were raised to tolerate all kinds of toil and hardship; they nevertheless also liked to amuse themselves with comic relief in their everyday life. For example, ancient Egyptian drawing can be quite accurate and at times even spirited. What scholars have described as caricatures are as informative and artistic as supposed serious works of art. Ancient Egyptians have left countless images representing religious, political, economic, and/or social aspects of their life. Scenes in Egyptian tombs could be imitated on ostraca (potsherds) that portray animals as characters performing what would normally be human roles, behaviors, or occupations. These scenes reveal the artists’ sense of comedy and humor and demonstrate their freedom of thought and expression to reproduce such lighthearted imitations of religious or funeral scenes. This paper will focus on a selection of drawings on ostraca as well as three papyri that show animals—often dressed in human garb and posing with human gestures—performing parodies of human pursuits (such as scribes, servants, musicians, dancers, leaders, and herdsmen).

Early-Emerging and Highly-Heritable Sensitivity to Human Communication in Dogs

Dogs exhibit similarities to humans in their sensitivity to cooperative-communicative cues, but the extent to which they are biologically prepared for communication with humans is heavily debated. To investigate the developmental and genetic origins of these traits, we tested 375 eight-week-old dog puppies on a battery of social-cognitive measures. We hypothesized that if dogs’ social skills for cooperating with humans are biologically prepared, then these skills should emerge robustly in early development, not require extensive socialization or learning, and exhibit heritable variation. Puppies were highly skillful at using diverse human gestures and we found no evidence of learning across test trials, suggesting that they possess these skills prior to their first exposure to these cues. Critically, over 40% of the variation in dogs’ point-following abilities and attention to human faces was attributable to genetic factors. Our results suggest that these social skills in dogs emerge early in development and are under strong genetic control.Highlights-Genetic factors account for nearly half of variation in dog social skills-Puppies displayed social skills and interest in human faces from 8 weeks old-Puppies successfully used human gestures from the very first trial

Cooperative Communication with Humans Evolved to Emerge Early in Dogs

While we know that dogs evolved from wolves through a process of domestication, it remains unclear how this process may have affected dog cognitive development. Here we tested dog (N=44) and wolf (N=37) puppies, 5-18 weeks old, on a battery of temperament and cognition tasks. Dog puppies were more attracted to humans, read human gestures more skillfully and made more eye contact with humans than wolf puppies. The two species were similarly attracted to objects and performed similarly on nonsocial measures of memory and inhibitory control. These results demonstrate the role of domestication in enhancing the cooperative communication skills of dogs through selection on attraction to humans, which altered developmental pathways.One Sentence SummaryDomestication altered dogs’ developmental pathways to enhance their cooperative communication with humans.