High sensitivity, low noise front-end for long range capacitive sensors for tagless indoor human localization

Iqbal, Javed; Lazarescu, Mihai T.; Arif, Arslan; Lavagno, Luciano

doi:10.1109/rtsi.2017.8065966

Cited by 6 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We measured that T 1 takes 36 CPU clock cycles and T 3 four cycles. Hence, for 16 MHz CPU clock frequency, we have: From Figure 12, the power consumption during the data transfer period T 1 and T 3 is given by: P data transfer = P CPU active + P GPIO MCU + P FPGA static + P FPGA dynamic + P GPIO FPGA (16) where P CPU active is the CPU power in active mode, P GPIO MCU is the power consumed by the GPIO pins of the parallel port of the CPU, P FPGA static is the FPGA static power, P FPGA dynamic is the FPGA dynamic power, and P GPIO FPGA is the power consumed by the FPGA GPIO pins used for the parallel bus.…”

Section: ) Parallel Communication With Fpgamentioning

confidence: 99%

“…Capacitive sensing of conductive and dielectric properties of human body was also used for indoor person sensing [10], [11], identification [1], [12], and localization [13]- [16]. Machine learning algorithms, and especially neural networks (NNs) trained on the abstract signatures of targets can be used to improve sensor performance [17], [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

et al. 2019

Self Cite

View full text Add to dashboard Cite

Human detection, identification, and monitoring are essential for many applications aiming to make smarter the indoor environments, where most people spend much of their time (like home, office, transportation, or public spaces). The capacitive sensors can meet stringent privacy, power, cost, and unobtrusiveness requirements, they do not rely on wearables or specific human interactions, but they may need significant on-board data processing to increase their performance. We comparatively analyze in terms of overall processing time and energy several data processing implementations of multilayer perceptron neural networks (NNs) on board capacitive sensors. The NN architecture, optimized using augmented experimental data, consists of six 17-bit inputs, two hidden layers with eight neurons each, and one four-bit output. For the software (SW) NN implementation, we use two STMicroelectronics STM32 low-power ARM microcontrollers (MCUs): one MCU optimized for power and one for performance. For hardware (HW) implementations, we use four ultralow-power field-programmable gate arrays (FPGAs), with different sizes, dedicated computation blocks, and data communication interfaces (one FPGA from the Lattice iCE40 family and three FPGAs from the Microsemi IGLOO family). Our shortest SW implementation latency is 54.4 µs and the lowest energy per inference is 990 nJ, while the shortest HW implementation latency is 1.99 µs and the lowest energy is 39 nJ (including the data transfer between MCU and FPGA). The FPGAs active power ranges between 6.24 and 34.7 mW, while their static power is between 79 and 277 µW. They compare very favorably with the static power consumption of Xilinx and Altera low-power device families, which is around 40 mW. The experimental results show that NN inferences offloaded to external FPGAs have lower latency and energy than SW ones (even when using HW multipliers), and the FPGAs with dedicated computational blocks (multiply-accumulate) perform best.INDEX TERMS Indoor person identification, capacitive sensing, neural networks, hardware design, ultralow power FPGAs, hardware acceleration, embedded design optimization.

show abstract

Section: ) Parallel Communication With Fpgamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Effectively reduces the noise that is occurred by the sensor transducer. To identifying the person multiple times frequency is passed on the person [17]. MAS render services based on RFID technology.…”

Section: Introductionmentioning

confidence: 99%

Human Identification in Smart Home Environment

Priyadharshini¹,

Umamakeswari²

2019

IJEAT

View full text Add to dashboard Cite

People moved their life as automatic systems, like smart home. But here major issues is security, the sensitive personal information have been raised the privacy concerns such as (modifying the data, unauthorized access of data) among peoples would like to share their personal information are only utilized for their benefits, rather than being utilized for malignant purpose. Person Identification in smart home with real time is the major challenging in monitoring system. Capturing the still and live images with high efficiency is the complex task. This system provides the efficiency in higher rate with quick response. Data are lively gathered and lively compared with the data, so the process is undergoing the HAAR and the Convolutional Neural Network (CNN). Capturing and labeling the images is done with the live processing then the live video processing is taken the frames of images. So, the data are not stored for long time, and compared the still and live frames in the known frame databases it matches the features and notify the system with two categories as known and unknown.

show abstract

A Survey on various technologies used in human identificationfor smart home

Priyadharshini

Umamakeswari

2018

2018 3rd International Conference on Communication and Electronics Systems (ICCES)

View full text Add to dashboard Cite

High sensitivity, low noise front-end for long range capacitive sensors for tagless indoor human localization

Cited by 6 publications

References 18 publications

Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

Human Identification in Smart Home Environment

A Survey on various technologies used in human identificationfor smart home

Contact Info

Product

Resources

About