This paper introduces a cost-effective method for the fabrication of stretchable circuits on polydimethylsiloxane (PDMS) using inkjet printing of silver nanoparticle ink. The fabrication method, presented here, allows for the development of fully stretchable and wearable sensors. Inkjet-printed sinusoidal and horseshoe patterns are experimentally characterized in terms of the effect of their geometry on stretchability, while maintaining adequate electrical conductivity. The optimal fabricated circuit, with a horseshoe pattern at an angle of 45°, is capable of undergoing an axial stretch up to a strain of 25% with a resistance under 800 Ω. The conductivity of the circuit is fully reversible once it is returned to its pre-stretching state. The circuit could also undergo up to 3000 stretching cycles without exhibiting a significant change in its conductivity. In addition, the successful development of a novel inkjet-printed fully stretchable and wearable version of the conventional pulse oximeter is demonstrated. Finally, the resulting sensor is evaluated in comparison to its commercially available counterpart.
Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4.
The main purpose of this paper is to achieve as low as possible leakage current (I OFF) to meet the requirements for ultra-low power (ULP) applications. The proposed methodology is based on studying the effect of the most effective FinFET design parameters that directly impact its leakage current. The parameters explored in this paper are the effective channel lengths L eff , gate stacks, gate contact materials, and gatesidewall spacers (L sp). The results show that utilizing a symmetrical dual-k material for 7-nm underlap tri-gate FinFETs appreciably allows a sufficient ON current and low leakage current and hence low stand by power consumption. Specifically, the effect of spacer length L sp and L HK is investigated to get low leakage current keeping I ON /I OFF as high as possible. Moreover, the effective channel length in subthreshold conduction (L eff) is maintained greater than the gate length (L g) and the threshold voltage (V th) is adjusted by the proper metal gate work function. The performance of the proposed n-and p-FinFET devices is verified using Sentaurus TCAD simulator from Synopsys. The resulted I OFF is 17 pA/µ m for n-FinFET and 14.7 pA/µm for p-FinFET which are the lowest leakage currents found in recent publications. The achieved I ON /I OFF ratio for both proposed devices is found to be 12.3 × 10 6 and 11 × 10 6 , respectively, which are comparable to the published data. These parameters are obtained for an appropriate choice of L sp = 10 nm and L HK = 5 nm. In addition, the short channel effects variations with L HK have been investigated. INDEX TERMS 7 nm Bulk FinFET, leakage current, SymD-k spacer, TCAD, ultra-low power.
Free Space Optical (FSO) communication systems have extensively invaded the speed of smart city evolution due to the current surge in demand for wireless communication spots that can match recent challenges due to high technical leaps in smart city evolution. As the number of users is vastly increasing throughout all networks in the form of machines, devices, and variously distinct objects, FSO is a hugely recommended robust communication system that mitigates a lot of RF disadvantages on the field with no need for licensing, fast rollout time, and low cost. This paper shows an exploit of a Low Power Field Programmable Gate Array (FPGA) based FSO communication system designed for Line of Sight (LOS) Building to Building Communication over a distance of 12 m using a 650 nm Visible Light (VL) red laser source via On-Off Keying (OOK) and higher-level Intensity Modulation (IM)/Pulse Width Modulation (PWM) schemes. The implemented system reached a doubled data rate than OOK of 230 kbps using the IM technique. Traffic monitoring and building security status can be frequently updated between adherent buildings, each scanning its zone real-time conditions and sharing them with the neighboring links.
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