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1. FabHandWear: An End-to-End Pipeline from Design to Fabrication of Customized Functional Hand Wearables

   https://doi.org/10.1145/3463518  

   Paredes, Luis ; Reddy, Sai Swarup ; Chidambaram, Subramanian ; Vagholkar, Devashri ; Zhang, Yunbo ; Benes, Bedrich ; Ramani, Karthik ( June 2021 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Current hand wearables have limited customizability, they are loose-fit to an individual's hand and lack comfort. The main barrier in customizing hand wearables is the geometric complexity and size variation in hands. Moreover, there are different functions that the users can be looking for; some may only want to detect hand's motion or orientation; others may be interested in tracking their vital signs. Current wearables usually fit multiple functions and are designed for a universal user with none or limited customization. There are no specialized tools that facilitate the creation of customized hand wearables for varying hand sizes and provide different functionalities. We envision an emerging generation of customizable hand wearables that supports hand differences and promotes hand exploration with additional functionality. We introduce FabHandWear, a novel system that allows end-to-end design and fabrication of customized functional self-contained hand wearables. FabHandWear is designed to work with off-the-shelf electronics, with the ability to connect them automatically and generate a printable pattern for fabrication. We validate our system by using illustrative applications, a durability test, and an empirical user evaluation. Overall, FabHandWear offers the freedom to create customized, functional, and manufacturable hand wearables.
2. Deep Learning based End-to-End Wireless Communication Systems without Pilots

   https://doi.org/10.1109/TCCN.2021.3061464  

   Ye, Hao ; Li, Geoffrey Ye ; Juang, Biing-Hwang Fred ( January 2021 , IEEE Transactions on Cognitive Communications and Networking)
3. Chauffeur: Benchmark Suite for Design and End-to-End Analysis of Self-Driving Vehicles on Embedded Systems

   https://doi.org/10.1145/3477005  

   Maity, Biswadip ; Yi, Saehanseul ; Seo, Dongjoo ; Cheng, Leming ; Lim, Sung-Soo ; Kim, Jong-Chan ; Donyanavard, Bryan ; Dutt, Nikil ( October 2021 , ACM Transactions on Embedded Computing Systems)

   Self-driving systems execute an ensemble of different self-driving workloads on embedded systems in an end-to-end manner, subject to functional and performance requirements. To enable exploration, optimization, and end-to-end evaluation on different embedded platforms, system designers critically need a benchmark suite that enables flexible and seamless configuration of self-driving scenarios, which realistically reflects real-world self-driving workloads’ unique characteristics. Existing CPU and GPU embedded benchmark suites typically (1) consider isolated applications, (2) are not sensor-driven, and (3) are unable to support emerging self-driving applications that simultaneously utilize CPUs and GPUs with stringent timing requirements. On the other hand, full-system self-driving simulators (e.g., AUTOWARE, APOLLO) focus on functional simulation, but lack the ability to evaluate the self-driving software stack on various embedded platforms. To address design needs, we present Chauffeur, the first open-source end-to-end benchmark suite for self-driving vehicles with configurable representative workloads. Chauffeur is easy to configure and run, enabling researchers to evaluate different platform configurations and explore alternative instantiations of the self-driving software pipeline. Chauffeur runs on diverse emerging platforms and exploits heterogeneous onboard resources. Our initial characterization of Chauffeur on different embedded platforms – NVIDIA Jetson TX2 and Drive PX2 – enables comparative evaluation of these GPU platforms in executingmore »an end-to-end self-driving computational pipeline to assess the end-to-end response times on these emerging embedded platforms while also creating opportunities to create application gangs for better response times. Chauffeur enables researchers to benchmark representative self-driving workloads and flexibly compose them for different self-driving scenarios to explore end-to-end tradeoffs between design constraints, power budget, real-time performance requirements, and accuracy of applications.« less
4. Two-factor Password-authenticated Key Exchange with End-to-end Security

   https://doi.org/10.1145/3446807  

   Jarecki, Stanislaw ; Jubur, Mohammed ; Krawczyk, Hugo ; Saxena, Nitesh ; Shirvanian, Maliheh ( August 2021 , ACM Transactions on Privacy and Security)

   We present a secure two-factor authentication (TFA) scheme based on the user’s possession of a password and a crypto-capable device. Security is “end-to-end” in the sense that the attacker can attack all parts of the system, including all communication links and any subset of parties (servers, devices, client terminals), can learn users’ passwords, and perform active and passive attacks, online and offline. In all cases the scheme provides the highest attainable security bounds given the set of compromised components. Our solution builds a TFA scheme using any Device-enhanced Password-authenticated Key Exchange (PAKE), defined by Jarecki et al., and any Short Authenticated String (SAS) Message Authentication, defined by Vaudenay. We show an efficient instantiation of this modular construction, which utilizes any password-based client-server authentication method, with or without reliance on public-key infrastructure. The security of the proposed scheme is proven in a formal model that we formulate as an extension of the traditional PAKE model. We also report on a prototype implementation of our schemes, including TLS-based and PKI-free variants, as well as several instantiations of the SAS mechanism, all demonstrating the practicality of our approach. Finally, we present a usability study evaluating the viability of our protocol contrasted with the traditionalmore »PIN-based TFA approach in terms of efficiency, potential for errors, user experience, and security perception of the underlying manual process. 1« less
5. End-to-End Throughput in Multi-Hop Wireless Networks With Random Relay Deployment

   https://doi.org/10.1109/TSIPN.2018.2806846  

   Liang, Yuan ; Li, Tongtong ( January 2018 , IEEE Transactions on Signal and Information Processing over Networks)