With the threatening increase in explosive-based terrorism against civil populations, the development of new devices capable of a rapid and cost-effective detection of hidden explosives has become a worldwide priority...
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ISBN:
(纸本)9781665467001
With the threatening increase in explosive-based terrorism against civil populations, the development of new devices capable of a rapid and cost-effective detection of hidden explosives has become a worldwide priority. Recently, semiconductor quantum dots have demonstrated great potential as luminescent probes for trace explosive detection. However, the growing interest in this technology and its potentiality is not accompanied by its widespread use in practical applications and in operating environments since most of the proposed devices still consist of lab-based procedures not amenable for field operation. This work explores and compares two alternative ways of employing quantum dots as sensing material to build simple, compact, and reusable devices for vapor explosive detection, beyond their typical use as fluorescent probes in solution. First, a high-performance chemiresistive sensor whose electrical resistance changes proportionally to the target gas concentration is proposed. Then, we present an optical system based on the solid-state photoluminescence of quantum dots cast on a silicon substrate. Easy fabrication, portability, low-cost, high sensitivity, and reusability make both the reported devices quite promising not only for laboratory-scale testing but also for practical applications on the field.
Time-interleaved (TI) SAR ADC architecture is preferred in the low-to-moderate resolution space to achieve >GS/s speed for its excellent power and area efficiency [1]–[3], as well as to reduce design complexity, w...
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ISBN:
(数字)9798331541019
ISBN:
(纸本)9798331541026
Time-interleaved (TI) SAR ADC architecture is preferred in the low-to-moderate resolution space to achieve >GS/s speed for its excellent power and area efficiency [1]–[3], as well as to reduce design complexity, which may lead to fewer tape-outs and faster product development. High-speed and medium-resolution ADCs are used in applications like wireless communications and portable instrumentation. Emerging applications such as RF direct sampling in fully digital phased arrays require similarly high sample rates, but they also demand RF SNDR beyond 45dB. In [4]–[6], high-speed SAR ADCs with such SNDR were reported. Often, 3 or more supply domains are utilized and tailored for optimized analog performance, as in [4], [5], making integration into a larger transceiver system difficult. In this paper, we present a 12GS/s 16× TI SAR ADC that achieves 48.1dB SNDR at 5.3GHz input, consumes 160mW from integration-friendly 1.8V and 0.8V standard process supplies and occupies $0.25\text{mm}^applications$ in 16nm FinFET.
Reducing substances are critical parameters for discriminating flavored beverages. The development of rapid and intelligent methods for detecting reducing substances can significantly enhance beverage production manag...
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Reducing substances are critical parameters for discriminating flavored beverages. The development of rapid and intelligent methods for detecting reducing substances can significantly enhance beverage production management and facilitate the construction of flavor databases. In this study, a GO@Mel-Cu nanozyme was synthesized by leveraging the strong peroxidase (POD)-like activity of Mel-Cu and the increased catalytic core surface area provided by graphene. Three-channel sensorarrays based on GO@Mel-Cu were constructed using three colorimetric substrates (OPD, TMB, and ABTS2-), enabling the discrimination of 19 reducing substances and the classification of five categories of flavored beverages. This platform not only demonstrated a limit of detection (LOD) of 1 nM to ascorbic acid but realized 100 % accuracy for 19 types of reducing substances discrimination, highlighting its potential for both the sensitive detection of reducing substances and the universal discrimination of flavored beverages. Absorbance fingerprints of the five flavored beverages were analyzed using PCA, LDA, and HCA, achieving successful intra-group species discrimination. In the blind sample testing, our sensor array successfully discriminated five flavored beverages and their binary mixtures with 98.2 % and 98 % accuracy, respectively. To further advance portable and intelligent beverage classification, ResNet 50 models were trained for sensor array image classification, achieving over 98 % accuracy. A portable and universal platform for flavored beverage discrimination was subsequently developed, enabling a portable system that provides direct output from photos to results. This work offers a promising strategy for flavored beverage discrimination and reducing substances detection, contributing to food production and regulatory applications.
Precision and energy-efficient sensor interfaces have always been needed in loT applications. To digitize weak signals (tens of mVs), such as shunt-based current and biomedical sensors, the interface should have low n...
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ISBN:
(数字)9798331541019
ISBN:
(纸本)9798331541026
Precision and energy-efficient sensor interfaces have always been needed in loT applications. To digitize weak signals (tens of mVs), such as shunt-based current and biomedical sensors, the interface should have low noise [1]–[4]. Moreover, to accommodate different use cases, the bandwidth (BW) and power dissipation of sensor interfaces should be highly scalable [5]–[7]. sensor interfaces based on discrete-time (DT) ADCs, such as SAR ADCs [8], [9] and FIA-based ΔΣ ADCs [5]–[7] are BW/power scalable, but they usually suffer from kT/C noise (Fig. 27.3.1 top, left). Using a continuous-time (CT) first stage, such as an instrumentation amplifier (IA) [1], [3], [4] (Fig. 27.3.1 top, right), or an active RC integrator [10], [11], can suppress the sampling noise of the following stages, however, they usually consume fixed static power, limiting their BW/power scalability.
Demands for single-photon-sensitive high-dynamic-range (HDR) imaging in security, automotive, and medical applications have driven development of scalable single-photon avalanche diode (SPAD)-based image sensors. In r...
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Volatile organic compounds (VOCs) are organic chemical compounds found in various products that easily vaporize and reach in the environment under normal conditions. They adversely affect the environment and human hea...
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The development of a multimodal sensing platform with multiple layers for electronic skin (e-skin) sensing of temperature and pressure has attracted considerable interest to practical applications in soft robotics, hu...
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ISBN:
(纸本)9780738125626
The development of a multimodal sensing platform with multiple layers for electronic skin (e-skin) sensing of temperature and pressure has attracted considerable interest to practical applications in soft robotics, human-machine interfaces, and wearable health monitoring. In this work, we demonstrated a new platform technology with multiple sandwiched layers of highly oriented carbon nanotube membrane and polyacrylonitrile for the integration of pressure and temperature sensory functionalities into a single platform that is thin, ultra-lightweight, flexible, and wearable. The key technology of in situ deposition of sensor platform on objects or in robot interface makes this a unique method for the development of e-skins for robotic applications, offering a new approach to wearable electronics and portable health care.
With the increasing demand of the accuracy of direction of arrival (DOA) estimation, the spare array configurations have attracted considerable interest because of their ability of possessing increased array aperture ...
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We present a four-channel ultrafast wavelength-swept distributed feedback (DFB) semiconductor laser array based on the reconstruction-equivalent-chirp (REC) technique using instantaneous injection current modulation. ...
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作者:
Daniela RusCSAIL
Massachusetts Institute of Technology Cambridge MA
The rapid growth of AI technologies has brought unprecedented advancements across numerous domains, from healthcare to autonomous systems, yet this progress has been accompanied by substantial energy demands. Generati...
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ISBN:
(数字)9798331541019
ISBN:
(纸本)9798331541026
The rapid growth of AI technologies has brought unprecedented advancements across numerous domains, from healthcare to autonomous systems, yet this progress has been accompanied by substantial energy demands. Generative AI has revolutionized the accessibility of advanced machine learning models, putting powerful AI capabilities directly into the hands of everyday users. Tools that generate text, images, video, and audio have made AI more democratic, fueling creativity, productivity, and innovation across industries. This explosion of generative AI applications, from chatbots to art generators, has transformed AI from a niche tool into an indispensable asset in our digital lives, as many can now access these technologies through their phones or computers. However, this widespread accessibility comes with significant trade-offs, particularly in terms of the energy required to train and deploy these enormous models. Large generative models such as GPT, DALL-E, and other multi-modal AI systems demand immense computational resources, translating to high energy consumption both during the extensive training process and when they are used in practice.
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