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1. Learning on the Rings: Self-Supervised 3D Finger Motion Tracking Using Wearable Sensors

   https://doi.org/10.1145/3534587  

   Zhou, Hao ; Lu, Taiting ; Liu, Yilin ; Zhang, Shijia ; Gowda, Mahanth ( July 2022 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   This paper presents ssLOTR (self-supervised learning on the rings), a system that shows the feasibility of designing self-supervised learning based techniques for 3D finger motion tracking using a custom-designed wearable inertial measurement unit (IMU) sensor with a minimal overhead of labeled training data. Ubiquitous finger motion tracking enables a number of applications in augmented and virtual reality, sign language recognition, rehabilitation healthcare, sports analytics, etc. However, unlike vision, there are no large-scale training datasets for developing robust machine learning (ML) models on wearable devices. ssLOTR designs ML models based on data augmentation and self-supervised learning to first extract efficient representations from raw IMU data without the need for any training labels. The extracted representations are further trained with small-scale labeled training data. In comparison to fully supervised learning, we show that only 15% of labeled training data is sufficient with self-supervised learning to achieve similar accuracy. Our sensor device is designed using a two-layer printed circuit board (PCB) to minimize the footprint and uses a combination of Polylactic acid (PLA) and Thermoplastic polyurethane (TPU) as housing materials for sturdiness and flexibility. It incorporates a system-on-chip (SoC) microcontroller with integrated WiFi/Bluetooth Low Energy (BLE) modules for real-time wireless communication, portability, and ubiquity. In contrast to gloves, our device is worn like rings on fingers, and therefore, does not impede dexterous finger motion. Extensive evaluation with 12 users depicts a 3D joint angle tracking accuracy of 9.07° (joint position accuracy of 6.55mm) with robustness to natural variation in sensor positions, wrist motion, etc, with low overhead in latency and power consumption on embedded platforms. 

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2. A 65nm 63.3µW 15Mbps Transceiver with Switched-Capacitor Adiabatic Signaling and Combinatorial-Pulse-Position Modulation for Body-Worn Video-Sensing AR Nodes

   https://doi.org/10.1109/ISSCC42614.2022.9731793  

   Chatterjee, Baibhab ; Datta, Arunashish ; Nath, Mayukh ; K, Gaurav Kumar ; Modak, Nirmoy ; Sen, Shreyas ( February 2022 , 2022 IEEE International Solid- State Circuits Conference (ISSCC))

   Recent advances in audio-visual augmented reality (AR) and virtual reality (VR) demands 1) high speed (>10Mbps) data transfer among wearable devices around the human body with 2) low transceiver (TRX) power consumption for longer lifetime, especially as communication energy/b is often orders of magnitude higher than computation energy/switching. While WiFi can transmit compressed video (HD 30fps, compressed @6-12Mbps), it consumes 50-to-400mW power. Bluetooth, on the other hand, is not designed for video transfer. New mm-Wave links can support the required bandwidth but do not support ultra-low-power (<1mW). In recent years, Human-Body Communication (HBC) [1]–[6] has emerged as a promising low-power alternative to traditional wireless communication. However, previous implementations of HBC transmitters (Tx) suffer from a large plate-to-plate capacitance (C p , between signal electrode and local ground of the transmitter) which results in a power consumption of aC p V2f (Fig. 16.6.1) in voltage-mode (VM) HBC. The recently proposed Resonant HBC [6] tries to overcome this problem by resonating C p with a parallel inductor (L). However, the operating frequency is usually < a few 10's of MHz for low-power Electro-Quasistatic (EQS) operation, resulting in a large/bulky inductor. Moreover, the resonant LC p circuit has a large settling time (≈5Q 2 RC P , where R is the effective series resistance of the inductor) for EQS frequencies which will limit the maximum symbol rate to <1MSps for a 21MHz carrier (the IEEE 802.15.6 standard for HBC), making resonant HBC infeasible for> 10Mb/s applications. 

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3. SELF: A High Performance and Bandwidth Efficient Approach to Exploiting Die-Stacked DRAM as Part of Memory

   https://doi.org/10.1109/MASCOTS.2017.23  

   Guo, Yuhua ; Liu, Qing ; Xiao, Weijun ; Huang, Ping ; Podhorszki, Norbert ; Klasky, Scott ; He, Xubin ( September 2017 , 2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS))

   Die-stacked DRAM (a.k.a., on-chip DRAM) provides much higher bandwidth and lower latency than off-chip DRAM. It is a promising technology to break the “memory wall”. Die-stacked DRAM can be used either as a cache (i.e., DRAM cache) or as a part of memory (PoM). A DRAM cache design would suffer from more page faults than a PoM design as the DRAM cache cannot contribute towards capacity of main memory. At the same time, obtaining high performance requires PoM systems to swap requested data to the die-stacked DRAM. Existing PoM designs fall into two categories - line-based and page-based. The former ensures low off-chip bandwidth utilization but suffers from a low hit ratio of on-chip memory due to limited temporal locality. In contrast, page-based designs achieve a high hit ratio of on-chip memory albeit at the cost of moving large amounts of data between on-chip and off-chip memories, leading to increased off-chip bandwidth utilization and significant system performance degradation. To achieve a similar high hit ratio of on-chip memory as pagebased designs, and eliminate excessive off-chip traffic involved, we propose SELF, a high performance and bandwidth efficient approach. The key idea is to SElectively swap Lines in a requested page that are likely to be accessed according to page Footprint, instead of blindly swapping an entire page. In doing so, SELF allows incoming requests to be serviced from the on-chip memory as much as possible, while avoiding swapping unused lines to reduce memory bandwidth consumption. We evaluate a memory system which consists of 4GB on-chip DRAM and 12GB offchip DRAM. Compared to a baseline system that has the same total capacity of 16GB off-chip DRAM, SELF improves the performance in terms of instructions per cycle by 26.9%, and reduces the energy consumption per memory access by 47.9% on average. In contrast, state-of-the-art line-based and page-based PoM designs can only improve the performance by 9.5% and 9.9%, respectively, against the same baseline system. 

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4. Touch-Based Magnetic Communication through Your Hand

   https://doi.org/10.1109/ICIOT.2018.00009  

   Allawadi, Arvind ; Liu, Kaikai ( July 2018 , 2018 IEEE International Congress on Internet of Things (ICIOT))

   Near field communication (NFC), which emerged only a decade ago, has been rapidly adopted in business services including point-of-sale (POS) systems, payments, identification, ticketing, and various other types of services. NFC offers great and varied promise in providing secure and implicit paired communication capability in smartphones. As a short-range wireless communication technology, the level of "secure" is contributed by the short-range nature. Compared with other competitive technologies, NFC achieves physical-level security but sacrifices convenience. For example, NFC cannot achieve device-free or hands-free payment transactions like the service provided by PayPal called PayPal beacon which utilizes Bluetooth-low-energy (BLE) technology. In this paper, we propose a low-cost wearable device that can achieve better physical-level security than NFC provides. This system is compatible with existing NFC-based POS systems and can help users realize a convenient hands-free payment transaction. Specifically, a custom NFC wristband was designed to channel its magnetic field through the human arm. By confining the magnetic field in NFC to the area around the body, we could minimize energy radiation, reduce the possibility of communication sniffing and hijackings, and improve security. To evaluate this approach, we conducted various experiments via different configurations. The results showed that the communication range for the human body channel was greater than that of the air and water channels. In addition, through this study we demonstrated that the human body is a naturally secure channel, and hacking and nearby interference are minimized during such communication. Our system also defines a new way of communication, for example, people can share confidential information with a simple handshake without pulling out and touching, or tapping smartphones. 

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5. SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control

   https://doi.org/10.1145/3477006  

   Malawade, Arnav ; Odema, Mohanad ; Lajeunesse-degroot, Sebastien ; Al Faruque, Mohammad Abdullah ( October 2021 , ACM Transactions on Embedded Computing Systems)

   Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reliably in real-time, requiring between several hundred watts to one kilowatt of power. This power consumption can dramatically reduce vehicles’ driving range and affect emissions. To address this problem, we propose SAGE: a methodology for selectively offloading the key energy-consuming modules of DL architectures to the cloud to optimize edge, energy usage while meeting real-time latency constraints. Furthermore, we leverage Head Network Distillation (HND) to introduce efficient bottlenecks within the DL architecture in order to minimize the network overhead costs of offloading with almost no degradation in the model’s performance. We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge, devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies. Compared to edge-only computation, SAGE reduces energy consumption by an average of 36.13% , 47.07% , and 55.66% for an AV with one low-resolution camera, one high-resolution camera, and three high-resolution cameras, respectively. SAGE also reduces upload data size by up to 98.40% compared to direct camera offloading. 

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