2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)最新文献_第5页

Design and characterization of a 65nm CMOS wireless RFID reader for ECoG tag 用于ECoG标签的65nm CMOS无线RFID阅读器的设计与表征

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974201

D. Venuto, J. Rabaey

引用次数: 0

A 12μW NPN-based temperature sensor with a 18.4pJ K2 FOM in 0.18μm BCD CMOS 基于12μW npn的温度传感器，在0.18μm BCD CMOS中具有18.4pJ的K2 FOM

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974246

Long Xu, J. Huijsing, K. Makinwa

引用次数: 1

Optical sensor and interface technologies for implantable biomedical devices 用于植入式生物医学设备的光学传感器和接口技术

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974259

J. Ohta

引用次数: 0

Adaptive supply voltage and duty cycle controller for yield-power optimization of ICs 用于集成电路输出功率优化的自适应电源电压和占空比控制器

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974233

Soonyoung Cha, L. Milor

引用次数: 0

Human-centric computing — The case for a Hyper-Dimensional approach 以人为中心的计算——超维度方法的案例

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974205

J. Rabaey, Abbas Rahimi, Sohum Datta, M. Rusch, P. Kanerva, B. Olshausen

{"title":"Human-centric computing — The case for a Hyper-Dimensional approach","authors":"J. Rabaey, Abbas Rahimi, Sohum Datta, M. Rusch, P. Kanerva, B. Olshausen","doi":"10.1109/IWASI.2017.7974205","DOIUrl":"https://doi.org/10.1109/IWASI.2017.7974205","url":null,"abstract":"Some of most compelling application domains of the IoT and Swarm concepts relate to how humans interact with the world around it and the cyberworld beyond. While the proliferation of communication and data processing devices has profoundly altered our interaction patterns, little has been changed in the way we process inputs (sensory) and outputs (actuation). The combination of IoT (Swarms) and wearable devices offers the potential for changing all of this, opening the door for true human augmentation. The epitome of this would be a direct interface to the human brain. Yet, making sense of the plethora of information received from the often noisy sensors and making reliable decisions within very tight latency bounds (< 10 ms) typically demands huge computational workloads to be performed in wearable form factors at extreme energy efficiency. In this presentation, we will make the case why alternative non-Von Neumann computational paradigms and architectures may be the right choice for these cognitive processing tasks. Even more, we will focus on a computational model called Hyper-Dimensional Computing (HDC), and illustrate with concrete examples of why this approach may be the right one in a post-Moore data-driven arena.","PeriodicalId":332606,"journal":{"name":"2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126360864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Performance of W-band FMCW Doppler radar FALCON-I as sensing system of atmosphere w波段FMCW多普勒雷达falcon - 1作为大气传感系统的性能

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974243

T. Takano, Yohei Kawamura, H. Nakata

引用次数: 1

DeepEmote: Towards multi-layer neural networks in a low power wearable multi-sensors bracelet DeepEmote:低功耗可穿戴多传感器手环中的多层神经网络

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974208

M. Magno, Michael Pritz, Philipp Mayer, L. Benini

{"title":"DeepEmote: Towards multi-layer neural networks in a low power wearable multi-sensors bracelet","authors":"M. Magno, Michael Pritz, Philipp Mayer, L. Benini","doi":"10.1109/IWASI.2017.7974208","DOIUrl":"https://doi.org/10.1109/IWASI.2017.7974208","url":null,"abstract":"Wearable smart sensing is a promising technology to enhance user experience that has already been exploited in sport/fitness, as well as health and human monitoring. Wearable sensing systems not only provide continuous data monitoring and acquisition, but are also expected to process, and make sense of the acquired data by classification in similar ways as human experts do. Supporting continuous operation on ultra-small batteries poses unique challenges in energy efficiency. In this paper, we present an ultra-low-power bracelet with several sensors that is able to run multi-layer neural networks learning algorithms to process data efficiently. The design combines low-power design, energy efficient algorithms and makes this bracelet suitable for long-term uninterrupted usage with small coin batteries. We demonstrate in-field measurement results that prove that neural networks applications can fit within the mW power and memory envelope of a commercial ARM Cortex M4F microcontroller. We show that a fully connected network of 26 neurons achieve an accuracy of 100% on emotion detection, using only 2% of memory available. Field trials demonstrate that the wearable device can achieve a 2-month lifetime while performing one emotion detection classification every 10 minutes.","PeriodicalId":332606,"journal":{"name":"2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129008642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 39

Three-dimensional modeling and analysis of antennas in cochlear implants 人工耳蜗天线的三维建模与分析

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974203

M. Paun, V. Paun

引用次数: 1

Target following on nano-scale Unmanned Aerial Vehicles 用纳米级无人机跟踪目标

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974242

D. Palossi, Jaskirat Singh, M. Magno, L. Benini

{"title":"Target following on nano-scale Unmanned Aerial Vehicles","authors":"D. Palossi, Jaskirat Singh, M. Magno, L. Benini","doi":"10.1109/IWASI.2017.7974242","DOIUrl":"https://doi.org/10.1109/IWASI.2017.7974242","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) with high level autonomous navigation capabilities are a hot topic both in industry and academia due to their numerous applications. However, autonomous navigation algorithms are demanding from the computational standpoint, and it is very challenging to run them on-board of nano-scale UAVs (i.e., few centimeters of diameter) because of the limited capabilities of their MCU-based controllers. This work focuses on the object tracking capability, (i.e., target following capability) on such nano-UAVs. We present a lightweight hardware-software solution, bringing autonomous navigation on a commercial platform using only on-board computational resources. Furthermore, we evaluate a parallel ultra-low-power (PULP) platform that enables the execution of even more sophisticated algorithms. Experimental results demonstrate the benefits of our solution, achieving accurate target following using an ARM Cortex M4 microcontroller consuming ≈ 130mW. Our evaluation on a PULP architecture shows the proposed solution running up-to 60 frame-per second in a power envelope of ≈ 30mW leaving more than 70% of the computational resources free for further on-board processing of more complex algorithms.","PeriodicalId":332606,"journal":{"name":"2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Covering our world with sensors 用传感器覆盖我们的世界

2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) Pub Date : 2017-06-01 DOI: 10.1109/IWASI.2017.7974219

N. Verma

{"title":"Covering our world with sensors","authors":"N. Verma","doi":"10.1109/IWASI.2017.7974219","DOIUrl":"https://doi.org/10.1109/IWASI.2017.7974219","url":null,"abstract":"Information technology has had profound impacts on our lives. The problem is that, so far, technology has required our explicit attention to provide services. This limits the scenarios in which it can or we would like it to take action. On the other hand, perceptive systems aim to understand our activities and intentions to proactively, collaboratively, and adaptively provide services. This requires systems to form projections of the world, but also construct models for how to respond. This talk starts by looking at how deploying large numbers of form-fitting sensors, which are explicitly associated with the physical objects we interact with (including each other), can provide contextually-relevant and structured data for enabling the construction of such models. Then, a possible platform technology for creating such sensors is examined, namely Large-Area Electronics (LAE). The challenges of realizing full systems from this are explored. In particular, perceptive systems present demanding functional requirements, but, through emerging algorithms from statistical signal processing and machine learning, also open up new opportunities for addressing technological limitations. Several LAE systems for human monitoring are presented, demonstrating the potentials.","PeriodicalId":332606,"journal":{"name":"2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116804118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0