A 2.4-GHz Quadrature Local Oscillator Buffer Insensitive to Frequency-Dependent Loads for Bluetooth Low Energy Applications

Song, Eunju; Kim, Sinyoung; Han, Junghwan; Kwon, Kuduck

doi:10.1109/lmwc.2020.3016733

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The distance computation is also affected by the adopted BLE advertising channel and whether the channel is known on the RX side. Based on the results from [14], the distortion caused by the frequency-dependent gains [15], together with the frequency-dependent free-space signal propagation, may all be compensated. Primarily in controlled experiments using RPIs, where the channel on which a packet was received is known, the issue of multi-path propagation, typical for indoor scenarios, can also be minimized.…”

Section: Ble Rss Parametersmentioning

confidence: 99%

Acceptable Margin of Error: Quantifying Location Privacy in BLE Localization

Shubina

Ometov

Niculescu

et al. 2023

2023 International Conference on Localization and GNSS (ICL-GNSS)

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Location privacy poses a critical challenge as the use of mobile devices and location-based services becomes more and more widespread. Proximity-detection data can reveal sensitive information about individuals, making it essential to preserve their location data. One way to achieve privacy protection is by adding noise to ground-truth data, which can introduce uncertainty while still allowing moderate utility for proximitydetection services and Received Signal Strength (RSS)-based localization. However, it is important to carefully adjust the amount of noise added in order to balance the privacy and accuracy concerns. This paper expands our previous work on evaluating location privacy bounds based on measurement error and intentionally added noise. Our model builds upon existing work in differential privacy and introduces other techniques to estimate privacy bounds specific to proximity data. By using real-world measurement data, we measure the privacy-accuracy trade-off and suggest cases where additional noise could be added. Our framework can be utilized to inform privacypreserving location-based applications and guide the selection of appropriate noise levels in order to achieve the desired privacyaccuracy balance.

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Section: Ble Rss Parametersmentioning

confidence: 99%

Acceptable Margin of Error: Quantifying Location Privacy in BLE Localization

Shubina

Ometov

Niculescu

et al. 2023

2023 International Conference on Localization and GNSS (ICL-GNSS)

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“…Bluetooth communication realizes the information interaction function between sensors and the computer. The Bluetooth communication module uses low-energy radio communication technology to realize data transmission, with the maximum rate of 1 Mb/s ( Song et al, 2020 ) and the effective communication of 15 m. The multichannel wireless surface EMG module is designed with a highly extending function and could be extended to 4–8 channels. The surface EMG device is shown in Figure 4 .…”

Section: A Wireless Surface Emg Acquisition Systemmentioning

confidence: 99%

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Cao

Cai

et al. 2021

Front. Comput. Neurosci.

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Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

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High-Power Wireless Module Implement in Industrial Internet of Things Field

Chu

Lin

2021

2021 16th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)

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A 2.4-GHz Quadrature Local Oscillator Buffer Insensitive to Frequency-Dependent Loads for Bluetooth Low Energy Applications

Cited by 4 publications

References 9 publications

Acceptable Margin of Error: Quantifying Location Privacy in BLE Localization

Acceptable Margin of Error: Quantifying Location Privacy in BLE Localization

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

High-Power Wireless Module Implement in Industrial Internet of Things Field

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