GetMobile: Mobile Computing and Communications

The use of batteries as a portable power source has gained tremendous importance for powering mobile devices and for enabling greener and less energy-intensive transportation. Whether the demand is for a smart phone, laptop, tablet or the next wearable, the consumer demands a thinner and lighter battery while maintaining or increasing battery life at the lowest possible cost. This demand has caused many companies to increasingly consider the battery and its performance when designing their next device. While, in the past, it may have been acceptable to take an "off-the-shelf" battery that meets specifications and "plug it in", many companies are starting to implement "smart" designs that take into account specific battery chemistries and characteristics to improve overall performance. This requires a knowledge of how a battery works. With this in mind, this article gives an explanation of battery fundamentals, why batteries degrade over their life, what limits their performance, the trade-offs between high power and high energy, and current efforts to build a better battery. While many of the overall properties are similar for many battery chemistries, here we will focus on the lithium-ion battery, specifically the cobaltdioxide/ graphite lithium-ion battery, one of the most popular battery chemistries for mobile devices.

Anthony Ferrese

GetMobile Mobile Computing and Communications

Battery Fundamentals

电池基础

Hearing loss is particularly harmful for language acquisition and neuro-development if it is left undetected in early childhood. Newborn hearing screening technologies using otoacoustic emissions (OAE) rely on detecting soft sounds generated by a healthy cochlea. High-income countries like the United States frequently implement hearing screening programs for every child at birth. However, such universal hearing screening is significantly less common in low- and middle-income countries, partly due to the conventional wisdom that the test requires sensitive and expensive acoustic hardware that costs thousands of dollars. In our MobiSys paper[1], we present the first wireless earbud design for low-cost hearing screening. Our hardware-software system reliably performs hearing screening using low-cost acoustic hardware, while being in the form-factor of a wireless earbud (Figure. 1a,b). The earbud hardware is designed to work across a large age range from newborns to adults. By developing low-cost and open-source wearable technology, our work may help address global health inequities in hearing screening by democratizing these medical devices.

Justin Chan

Antonio Glenn

Malek Itani

Lisa R. Mancl

Emily R. Gallagher

No Ear Left Behind: Wireless Earbuds for Low-Cost Hearing Screening

一个耳朵都不能少：用于低成本听力筛查的无线耳塞

As radar sensors become an integral component of Internet of Things (IoT) systems, the challenge of high power consumption poses a significant barrier, especially for battery-operated devices. This article introduces NeuroRadar, a groundbreaking solution that leverages a radar front-end capable of generating spike sequences, which can be efficiently processed by energy-saving Spiking Neural Networks (SNNs). We explore the innovative design and implementation of NeuroRadar, showcasing its effectiveness in applications like gesture recognition and human tracking. By achieving dramatically lower power consumption compared to traditional radar systems, NeuroRadar represents a new paradigm in energy-efficient IoT sensing.

Kai Zheng

Kun Qian

Timothy Woodford

Xinyu Zhang

A Neuromorphic Radar Sensor for Low-Power IoT Systems

用于低功耗物联网系统的神经形态雷达传感器

Deploying deep learning (DL) on mobile devices has become increasingly prevalent. DL software libraries are crucial for efficient on-device inference, alongside algorithms and hardware. However, there has been limited understanding on the performance of modern DL libraries. We fill this gap by benchmarking 6 popular DL libraries and 15 diverse models across 10 mobile devices, which reveal an unsatisfactory landscape of mobile DL: their performance is highly disparate and fragmented across different models and hardware, and the impacts often surpass algorithm or hardware optimizations, such as model quantization and GPU/NPU-based computing. Finally, we provide practical implications for stakeholders in the DL library ecosystem, and envision a more ambitious picture of future mobile AI landscape in the LLM era.

Qiyang Zhang

Mengwei Xu

Benchmarking Mobile Deep Learning Software

移动深度学习软件基准测试

Recent developments of networking technologies have witnessed a growing interest on human-oriented services such as Tactile Internet and VR/AR/XR, most of which are data-hungry and computationally expensive. Semantic communication and semantic-ware networking (SAN) are promising solutions to meet the above need and support emerging smart applications and services based on the network and user generated data. In this paper, we provide an overview of the ongoing standardization activity of semantic communication and semantic-aware networking at IMT-2030 (6G) standard promotion group, including the overview of the concepts, key functional components, reference architectures, and use cases.

Guangming Shi

Yong Xiao

An Introduction to Semantic Communication and Semantic-Aware Networking Standardization for 6G

6G语义通信与语义感知网络标准化简介

Lighting estimation is an important and long-standing task in computer vision and graphics communities[1]. Over the past few years, we have observed a rising demand for creating immersive user experiences in mobile augmented reality (AR) applications. In particular, environment lighting estimation is essential to render visually coherent virtual objects that need to be overlaid on physical scenes in mobile AR. In this work, we explore the key research questions in lighting understanding for an emerging application domain, mobile AR on multi-camera handheld devices, particularly try-on applications where end users leverage handheld mobile devices, such as smartphones, to overlay products of interest on their faces. Mobile AR try-on apps promote online shopping and often require photorealism to provide user experiences on par with physical try-on.

短名	GetMobile: Mobile Comp. and Comm.
Journal Impact	0.54
国际分区	TELECOMMUNICATIONS(Q4)

涉及主题	计算机科学工程类操作系统万维网电信业务物理计算机网络广告会话（web分析）嵌入式系统生物人工智能心理学量子力学
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: ，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 0.00%

GetMobile: Mobile Computing and Communications

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Battery Fundamentals

No Ear Left Behind: Wireless Earbuds for Low-Cost Hearing Screening

A Neuromorphic Radar Sensor for Low-Power IoT Systems

Benchmarking Mobile Deep Learning Software

An Introduction to Semantic Communication and Semantic-Aware Networking Standardization for 6G

No Ear Left Behind: Wireless Earbuds for Low-Cost Hearing Screening

A Neuromorphic Radar Sensor for Low-Power IoT Systems

Benchmarking Mobile Deep Learning Software

An Introduction to Semantic Communication and Semantic-Aware Networking Standardization for 6G

Multi-Camera Lighting Estimation for Mobile Augmented Reality