Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFrom Communication to Sensing : Recognizing and Counting Repetitive Motions with Wireless Backscattering
56
0
0.0
(
0
)
Added by
Haohua Du
Publication date
2018
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Recently several ground-breaking RF-based motion recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. To tackle this challenge, we propose a novel system, called Motion-Fi, which marries battery free wireless backscattering and device-free sensing. Motion-Fi is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-users performing certain motions simultaneously because of the relatively short transmission range of backscattered signals. Although the repetitive motions are fairly well detectable through the backscattering signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. We build a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5% count error. With little efforts in learning the patterns, our method could achieve 93.1% motion-recognition accuracy for a variety of motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100% with only 3 repetitions. Our experiments also show that the motions of multiple persons separating by around 2 meters cause little accuracy reduction in the counting process.
rate research
Read More
During the last few years, intensive research efforts are being done in the field of brain interfaces to extract neuro-information from the signals representing neuronal activities in the human brain. A recent development of these interfaces is capable of direct communication between animals brains, enabling direct brain-to-brain communication. Although these results are new and the experimental scenario simple, the fast development in neuroscience, and information and communication technologies indicate the potential of new scenarios for wireless communications between brains. Depending of the specific kind of neuro-activity to be communicated, the brain-to-brain link shall follow strict requirements of high data rates, low-latency, and reliable communication. In this paper we highlight key beyond 5G technologies that potentially will support this promising approach.
This work investigates the temporal dispersion of a wireless terahertz communication signal caused by reflection from a rough (diffuse) surface, and its subsequent impact on symbol error rate versus data rate. Broadband measurements of diffuse reflectors using terahertz time-domain spectroscopy were used to establish and validate a scattering model that uses stochastic methods to describe the effects of surface roughness on the phase and amplitude of a reflected terahertz signal, expressed as a communication channel transfer function. The modeled channel was used to simulate a quadrature phase shift keying (QPSK)- modulated wireless communication link to determine the relationships between symbol error rate and data rate as a function of surface roughness. The simulations reveal that surface roughness from wall texturing results in group delay dispersion that limits achievable data rate with low errors. A distinct dispersion limit in surface roughness is discovered beyond which unacceptable numbers of symbol errors begin to accrue for a given data rate.
During the last few years, intensive research efforts are being done in the field of brain interfaces to extract neuro-information from the signals representing neuronal activities in the human brain. Recent development of brain-to-computer interfaces support direct communication between animals brains, enabling direct brain-to-brain communication. Although these results are based on binary communication with relaxed requirements of latency and throughput, the fast development in neuro-science technologies indicates potential new scenarios for wireless communications between brains. In this paper we highlight technologies that are being used today to enable brain-to-brain communication and propose potential wireless communication architectures and requirements for future scenarios.
This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption. An ultra-high efficiency is achieved by leveraging compressed sensing (CS) for simultaneous data compression and encryption. CS enables sub-Nyquist sampling of neural signals by taking advantage of its intrinsic sparsity. It simultaneously encrypts the data with the sampling matrix being the cryptographic key. To share the key over an insecure wireless channel, we implement an elliptic-curve cryptography (ECC) based key exchanging protocol. The CS operation is executed in a custom-designed IC fabricated in 180nm CMOS technology. Mixed-signal circuits are designed to optimize the power efficiency of the matrix-vector multiplication (MVM) of the CS operation. The ECC algorithm is implemented in a low-power Cortex-M0 microcontroller (MCU). To be protected from timing and power analysis attacks, the implementation avoids possible data-dependent branches and also employs a randomized ECC initialization. At a compression ratio of 8x, the average correlated coefficient between the reconstructed signals and the uncompressed signals is 0.973, while the ciphertext-only attacks (CoA) achieve no better than 0.054 over 200,000 attacks. The prototype achieves a 35x power saving compared with conventional implementation in low-power MCUs. This work demonstrates a promising solution for future chronic neural recording systems with requirements in high energy efficiency and security.
An eight element, compact Ultra Wideband-Multiple Input Multiple Output (UWB-MIMO) antenna capable of providing high data rates for future Fifth Generation (5G) terminal equipments along with the provision of necessary bandwidth for Third Generation (3G) and Fourth Generation (4G) communications that accomplishes band rejection from 4.85 to 6.35 GHz by deploying a Inductor Capacitor (LC) stub on the ground plane is presented. The incorporated stub also provides flexibility to reject any selected band as well as bandwidth control. The orthogonal placement of the printed monopoles permits polarization diversity and provides high isolation. In the proposed eight element UWB-MIMO/diversity antenna, monopole pair 3-4 are 180o mirrored transform of monopole pair 1-2 which lie on the opposite corners of a planar 50 x 50 mm2 substrate. Four additional monopoles are then placed perpendicularly to the same board leading to a total size of 50 x 50 x 25 mm3 only. The simulated results are validated by comparing the measurements of a fabricated prototype. It was concluded that the design meets the target specifications over the entire bandwidth of 2 to 12 GHz with a reflection coefficient better than -10 dB (except the rejected band), isolation more than 17 dB, low envelope correlation, low gain variation, stable radiation pattern, and strong rejection of the signals in the Wireless Local Area Network (WLAN) band. Overall, compact and reduced complexity of the proposed eight element architecture, strengthens its practical viability for the diversity applications in future 5G terminal equipments amongst other MIMO antennas designs present in the literature.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
