TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization

Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be divided into two parts. During message transmission, one part is transmitted through a socket and the other part uses shared memory. The part within shared memory is never copied or serialized during its lifetime. We have integrated TZC with ROS and ROS2 and find that TZC can be easily combined with current open-source platforms. By using TZC, the overhead of IPC remains constant when the message size grows. In particular, when the message size is 4MB (less than the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2 IPC from hundreds of milliseconds to less than 1 millisecond. We also demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in autonomous driving scenarios. We show that by using TZC, the braking distance can be shortened by 16% than ROS

Preparation and Characterization of Waterborne Polyurethaneurea Composed of Dimer Fatty Acid Polyester Polyol

A series of polyurethaneurea (PUU) aqueous dispersions, which were stable at ambient temperature for more than 1 year, were prepared with C36-dimer-fatty-acid-based polyester polyol, isophorone diisocyanate, dimethylol propionic acid, and ethylenediamine. The particle size of all these PUU (DPU) aqueous dispersions (<100 nm) was less than that of comparable specimens, that is, poly-(neopentyl glycol adipate) polyester-polyol-based PUU (APU) aqueous dispersions, and the polydispersity index was very narrow (≤1.13). The films prepared with the DPU aqueous dispersions exhibited excellent waterproof performance, such as low amount of water absorption (1.3 wt%), and good mechanical properties (hardness and tensile strength), resulting from the strong hydrogen bonding in urea carbonyl groups and the perfect ordered structure of hard segments compared with those prepared with the APU aqueous dispersions. The surface hydrophobicity of the films prepared with modified DPU aqueous dispersions, which were modified with a fluorinated polyacrylate emulsion, was excellent, as the water contact angle on the surface of such films rose up to 100. The mechanical properties of such modified DPU films were further enhanced

(1SR,2RS,3SR,5SR,6RS)-6-[(Z)-1-Acet­oxy-2-phenyl­ethen­yl]-3-eth­oxy-2-phenyl­bicyclo­[3.1.0]hexan-1-yl acetate

The mol­ecule of the title compound, C26H28O5, is chiral with five stereogenic centres; however, the centrosymmetric triclinic group gives a racemic crystal. The fused ring system adopta boat conformation in which the cyclo­propane ring plane is roughly perpendicular to the styryl group plane, forming a dihedral angle of 74.78 (19)°. The dihedral angle between the two benzene rings is 77.24 (6)°

App Parameter Energy Profiling: Optimizing App Energy Drain by Finding Tunable App Parameters

In this paper, we observe that modern mobile apps come with a large number of parameters that control the app behavior which indirectly affect the app energy drain, and using incorrect or non-optimal values for such app parameters can lead to app energy drain deficiency or even energy bugs. We argue conventional app energy optimization using an energy profiler which pinpoints energy hotspot code segments in the app source code may be ineffective in detecting such parameter-induced app energy deficiency. We propose app parameter energy profiling which identifies tunable app parameters that can reduce app energy drain without affecting app functions as a potentially more effective solution for debugging such app energy deficiency. We present the design and implementation of Medusa, an app parameter energy profiling framework. Medusa overcomes three key design challenges: how to filter out and narrow down candidate parameters, how to pick alternative parameter values, and how to perform reliable energy drain testing of app versions with mutated parameter values. We demonstrate the effectiveness of Medusa by applying it to a set of Android apps which successfully identifies tunable energy-reducing parameters

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these deep parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app\u27s energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps

Investigation on the Structural Behavior of Shear Walls with Steel Truss Coupling Beams under Seismic Loading

Based on existing experimental results, the finite element analyses were carried out on shear wall structures with steel truss coupling beams. This work studied the seismic behaviors and the working mechanism of the steel truss coupling beam at the ultimate state and put forward two parameters: the area ratio of web member to chord and the stiffness ratio of coupling beam to shear wall. The seismic optimum design method of the coupling beam was also proposed. Afterwards, a comparative analysis was implemented on the three-dimensional shear wall model with steel truss coupling beams designed by the proposed design method. The results show that the structures designed by the proposed method have excellent seismic behaviors, the steel truss coupling beams have enough stiffness to connect shear walls effectively, and its web members have appropriate cross sections to dissipate seismic energy

OptSample: A Resilient Buffer Management Policy for Robotic Systems based on Optimal Message Sampling

Modern robotic systems have become an alternative to humans to perform risky or exhausting tasks. In such application scenarios, communications between robots and the control center have become one of the major problems. Buffering is a commonly used solution to relieve temporary network disruption. But the assumption that newer messages are more valuable than older ones is not true for many application scenarios such as explorations, rescue operations, and surveillance. In this paper, we proposed a novel resilient buffer management policy named OptSample. It can uniformly sampling messages and dynamically adjust the sample rate based on run-time network situation. We define an evaluation function to estimate the profit of a message sequence. Based on the function, our analysis and simulation shows that the OptSample policy can effectively prevent losing long segment of continuous messages and improve the overall profit of the received messages. We implement the proposed policy in ROS. The implementation is transparent to user and no user code need to be changed. Experimental results on several application scenarios show that the OptSample policy can help robotic systems be more resilient against network disruption