Research on optimization of real-time efficient storage algorithm in data information serialization

BACKGROUND

Along with the vigorous development of Internet technology, increasing the functions of the various types of equipment, network communication easy and diversity, at the same time, the amount of data is very huge, under the network bandwidth limitations, through long lead to a data need to be cut into more, one by one, transfer times, information delay problems.

RESULTS

Aiming at the problems of poor data integrity, low efficiency and poor serialization efficiency of traditional data storage information, this article introduces Protobuf technology to study the serialization of data storage information. The serpentine gap method is used to complete the allocation interval of the sequence nodes, so that the working state and the resting state always maintain a dynamic balance. According to the first-level rules, the storage data of the completed target node is obtained, and the grammatical structure and the semantics of the target data are analyzed, Meanwhile corresponding correspondences are established, and the data storage information is serialized. In order to verify the effectiveness of Protobuf's data storage information serialization method, a comparative experiment is designed. By using three methods of HDVM, Redis and Protobuf to serialize JSON data, the comparative analysis shows that HDVM has the longest processing time and Protobuf has the shortest processing time, and the data integrity is not affected. The simulation data shows that the Protobuf serialization method has short conversion time, high space utilization, and the Obvious advantages in correctness and integrity. It is vary suitable for serialization of JSON data in the case of limited bandwidth.

CNN Based Detectors on Planetary Environments: A Performance Evaluation

An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environment in which it will operate. For this reason, an algorithm based on convolutional neural networks is proposed for the detection of rocks in environments similar to Mars. The methodology proposed here is based on the use of a Single-Shot-Detector (SSD) network architecture, which has been modified to evaluate the performance. The main contribution of this study is to provide an alternative methodology to detect rocks in planetary images because most of the previous works only focus on classification problems and used handmade feature vectors.

Spiking neural state machine for gait frequency entrainment in a flexible modular robot

We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but can be modulated by external inputs. By combining these modules, we can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward crawling gait entrained to the natural frequency of the robot by a minimal system of twelve neurons organized into four modules.

Neurorobots as a Means Toward Neuroethology and Explainable AI

Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.

Embodied tactile perception and learning

Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

A Snake-Like Robot with Envelope Wheels and Obstacle-Aided Gaits

Most of the current snake-like robots can only work in a specific environment, or only have a good movement effect in a certain gait. This paper presents a design for a snake-like robot to improve the adaptability of various environments. Each standard module of the snake-like robot has three degrees of freedom: yawing, rolling, and telescoping. The envelope passive wheels are used to enable the robot to move in complex environments such as a narrow passage. We verified some simple movements such as serpentine movement and rectilinear movement and designed a method for recovering from rollover when the robot is in straight state. In addition, two novel gaits, obstacle-aided concertina gait, and obstacle-aided gait through narrow corner, are proposed in this paper. We demonstrated the feasibility for passing the narrow corner by these gaits in experiments.

Low-Cost Servomotor Driver for PFM Control

Servomotors have already been around for some decades and they are extremely popular among roboticists due to their simple control technique, reliability and low-cost. They are usually controlled by using Pulse Width Modulation (PWM) and this paper aims to keep the idea of simplicity and low-cost, while introducing a new control technique: Pulse Frequency Modulation (PFM). The objective of this paper is to focus on our development of a low-cost servomotor controller which will allow the research community to use them with PFM. A low-cost commercial servomotor is used as the base system for the development: a small PCB that fits inside the case and allocates all the electronic components to control the motor has been designed to replace the original. The potentiometer is retained as the feedback sensor and a microcontroller is responsible for controlling the position of the motor. The paper compares the performance of a PWM and a PFM controlled servomotor. The comparison shows that the servomotor with our controller achieves a faster mechanism for switching targets and a lower latency. This controller can be used with neuromorphic systems to remove the conversion from events to PWM.