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1. A Magnetic Sensor based Auto-tracking system for 2.4 GHz Near-field Phased-Array based Wireless Power Transfer System in Neuromodulation Applications

   Nabanita Saha, Erik Pineda (January 2023, 2023 USNC-URSI National Radio Science Meeting)

   This paper presents a magnetic sensor based autotracking method for a phased array based wireless power transfer system to be implemented in neuromodulation applications. This method is proposed to track the position of the receiver(placed on a freely moving animal) and transmit the microwave signal with a focused beam to the target receiver. The coordinate locations of the target are obtained from the magnetic sensor and converted into phase information for the phased array. The system is constructed by a 2.4 GHz near-field 4×4 phased array transmitter antenna with 4-bit phase shifters. The phased array TX antenna steers the beam from -5° to -155° in the θ plane. The magnetic sensor can detect the location of the receiver and the in this steering range. The process of tracking the the target and focusing the beam has been evaluated by simulation. 

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2. Systolic-Array Spiking Neural Accelerators with Dynamic Heterogeneous Voltage Regulation

   Lee, Jeong-Jun; Chen, Jianhao; Zhang, Wenrui; Li, Peng (July 2021, The 2021 International Joint Conference on Neural Networks (IJCNN))

   null (Ed.)

   Spiking neural networks (SNNs) have emerged as a new generation of neural networks, presenting a brain-inspired event-driven model with advantages in spatiotemporal information processing. Due to the need for high power consumption of compute-intensive neural accelerators, adequate power delivery network (PDN) design is a key requirement to ensure power efficiency and integrity. However, PDN design for SNN accelerators has not been extensively studied despite its great potential benefit in energy efficiency. In this paper, we present the first study on dynamic heterogeneous voltage regulation (HVR) for spiking neural accelerators to maximize system energy efficiency while ensuring power integrity. We propose a novel sparse-workload-aware dynamic PDN control policy, which enables high energy efficiency of sparse spiking computation on a systolic array. By exploring sparse inputs and all-or-none nature of spiking computations for PDN control, we explore different types of PDNs to accelerate spiking convolutional neural networks (S-CNNs) trained with the dynamic vision sensor (DVS) gesture dataset. Furthermore, we demonstrate various power gating schemes to further optimize the proposed PDN architecture, which leads to a more than a three-fold reduction in total energy overhead for spiking neural computations on systolic array-based accelerators. 

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3. Intensity-modulated nanoplasmonic interferometric sensor for MMP-9 detection

   https://doi.org/10.1039/C8LC01391H  

   Qian, Yifeng; Zeng, Xie; Gao, Yongkang; Li, Hang; Kumar, Sushil; Gan, Qiaoqiang; Cheng, Xuanhong; Bartoli, Filbert J. (March 2019, Lab on a Chip)

   To elucidate the secretary function of immune cells, we develop a nanoplasmonic circular interferometric biosensor based on intensity interrogation for label-free and dynamic sensing of molecular secretion. Exceptional sensitivity has been demonstrated through coupling free light and surface plasmon polariton (SPPs) waves, which generates a constructive and deconstructive interference pattern with high contrast and narrow linewidth when illuminated by white light. Alternatively, by adopting a narrow-band LED source and a CCD camera in this work, the transmission intensity of multiple sensing units is monitored simultaneously with a simple collinear optical setup. This intensity-modulated sensing platform yields a resolution of 4.1 × 10 −5 refractive index unit (RIU) with a high temporal resolution of 1 s and a miniaturized footprint as small as 9.8 × 9.8 μm 2 for a single sensing unit. By integrating the signals from multiple sensor units, the resolution of a 12 × 12 sensor array was found to reach 7.3 × 10 −6 RIU. We apply this sensor array to detect matrix metalloproteinase 9 (MMP-9) secretion from human monocytic cells, THP-1, at different time points after lipopolysaccharide (LPS) simulation and the results are in good agreement with enzyme-linked immunosorbent assay (ELISA) tests, but without the need for labeling. The spatial, temporal and mass resolutions of the sensor array are found to exceed other label-free technologies. These biomolecular arrays, incorporated in a microfluidic sensor platform, hold great potential for the study the dynamics and interplay of cell secretion signals and achieving a better understanding of single cell functions. 

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4. Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review

   https://doi.org/10.3390/s20143889  

   Khan, Md Ashfaque; Rao, Mulpuri V. (July 2020, Sensors)

   null (Ed.)

   In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H2), alcohols (R-OH), methane (CH4), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO2), sulfur-dioxide (SO2), ammonia (NH3), hydrogen sulfide (H2S) and carbon dioxide (CO2). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H2 gas sensing, whereas the GaN/(TiO2–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO2 NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed. 

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5. Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

   https://doi.org/10.3390/s22010212  

   Lin, Maohua; Abd, Moaed A.; Taing, Alex; Tsai, Chi-Tay; Vrionis, Frank D.; Engeberg, Erik D. (January 2022, Sensors)

   Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants. 

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