The current standard of diabetes management depends upon the invasive blood pricking techniques. In recent times, the availability of minimally invasive continuous glucose monitoring devices have made some improvements in the life of diabetic patients however it has its own limitations which include painful insertion, excessive cost, discomfort and an active risk due to the presence of a foreign body under the skin. Due to all these factors, the non-invasive glucose monitoring has remain a subject of research for the last two decades and multiple techniques of non-invasive glucose monitoring have been proposed. These proposed techniques have the potential to be evolved into a wearable device for non-invasive diabetes management. This paper reviews research advances and major challenges of such techniques or methods in recent years and broadly classifies them into four types based on their detection principles. These four methods are: optical spectroscopy, photoacoustic spectroscopy, electromagnetic sensing and nanomaterial based sensing. The paper primarily focuses on the evolution of non-invasive technology from bench-top equipment to smart wearable devices for personalized non-invasive continuous glucose monitoring in these four methods. With the rapid evolve of wearable technology, all these four methods of non-invasive blood glucose monitoring independently or in combination of two or more have the potential to become a reality in the near future for efficient, affordable, accurate and pain-free diabetes management.

In this work, a novel compact and accurate glucose concentration measurement system is developed using the well-established photoacoustic Near Infra-Red spectroscopy. The proposed in-vitro instrumentation methods are in a small form factor, making it a viable candidate and precursor for an in-vivo non-invasive wearable blood glucose monitoring in the near future. The accuracy comes from the phase sensitive detection of the electrical signal. This detection technique uses an off-the shelf modulator/demodulator integrated circuit configured as a lock-in amplifier to increase the signal to noise ratio multifold. No prior work on photoacoustic spectroscopy, has taken advantage of this detection methodology in such a small form factor. The dimension of the lock-in-amplifier is 13mm $\times$ 10.65mm $\times$ 2.65mm. The maximum linear dimension of the exciting laser is 5.6 mm. The acoustic sensor (transducer) has a dimension of 42mm $\times$ 12mm. Furthermore, the measurement and analyses of the observed data uses multiple stochastic and machine learning techniques to bring out the best correlation fit between the glucose concentration and a specific feature of the electrical signal. With these methods and techniques, a strong correlation was confirmed between the glucose concentration and the amplitude of the electrical signal. The computed correlation coefficient between the signal amplitude and glucose concentration is 97% while the p-value is 5.6E-6. To the best of our knowledge, this is the first work to report photoacoustic spectroscopy for glucose concentration measurement in a compact form, with lock-in amplifier and aided with machine learning algorithms for future application as a wearable device.

As model size and the number of layers increase, Deep Neural Networks (DNNs) demand enormous computational power and throughput to meet exceedingly high prediction accuracy’s of today’s machine learning (ML) applications. Spatial hardware accelerators have been proposed that optimize the dataflow and exploit sparsity to provide a significant decrease in power consumption. As spatial architectures are traditionally designed with metallic interconnects, significant power is expended for data movement for different dataflows. In this paper, we exploit extended wireless technology to design a power-efficient and high-throughput DNN accelerator, e-WiNN, that can be configured for all representative dataflows and arithmetic precisions. We leverage novel circuit design by utilizing Dadda-algorithm based Multiply-and-Accumulate (MAC) circuits for 4-bit, 8-bit and 16-bit inputs to reduce area, power and delay constraints in 14 nm predictive technology. Our novel wireless transmitter integrates on- off keying (OOK) modulator with power amplifier that results in significant energy savings. To reduce the area overhead, we cluster wireless transceivers into groups of four such that both weights and input features can be effectively multicast to reduce the data movement. The energy efficient transceiver circuit is implemented in state-of-the-art BSIM 32 nm FinFET technology model and our link budget considers required RF power for different frequencies and inter-PE distance at three different antenna directivities including isotropic. Our detailed RTL modeling and cycle-accurate simulation results show that e-WiNN achieves 36.3% latency reduction and 76.1% energy saving when compared to state-of-art wire interconnected accelerators; 70.3% area reduction and 41.6% energy saving at the cost of 11% latency increase when compared to prior wireless accelerators on various neural networks (AlexNet, VGG16, and ResNet-9/50).

This article introduces a low-cost, compact, and energy-efficient soil moisture monitoring system designed primarily to support small farmers in California’s San Joaquin Valley with effective crop management. The customized system integrates an Irrometer Watermark Soil Moisture Sensor, a Sensor Adapter, and a wireless enabled PSoC 6 BLE Pioneer Kit to measure soil water tension. Results are displayed in real time on an external LCD in a simple, easy-to-understand format, enabling underprivileged and less educated farmers to make timely decisions for improved crop management. This article further examines future opportunities to integrate the system with web-based technologies, enabling remote monitoring and AI-driven data analysis. The proposed solution addresses critical challenges in soil moisture management faced by small farmers, aiming to enhance efficiency and boost crop productivity through accessible, affordable technology.