This paper focuses on the development and analysis of an innovative unmanned soil excavating and transporting system, utilizing three-dimensional point cloud technology to enhance automation and productivity in earthwork operations. The study introduces a 3D point cloud-based volume estimation technique for real-time monitoring and measurement of exca-vation sites, as well as a model for controlling soil unloading volumes in conjunction with vehicle speed to ensure precise and efficient soil handling. Earthmoving in construction sites, particularly soil excavation and placement, is a critical yet risky endeavor due to manual operation inefficiencies. Therefore, the creation of a safer, unmanned soil handling system is vital for augmenting the safety and efficiency of construction operations.

Adaptive front-lighting system (AFS) is an intelligent automotive headlighting system that can adaptively adjust the angle of illumination according to road conditions. In this paper, in order to improve the safety and reliability of the AFS, the functional safety analysis of automotive AFS is developed based on the international standard ISO 26262. According to the standard functional safety analysis process, the hardware structure, models and functionalities of the AFS system are described. Then, the automotive safety integrity level (ASIL) is obtained based on the hazard analysis and risk assessment (HARA). Based on ASIL allocation principle, this paper further proposes the safety goals of the AFS. Finally, aiming to improve driving safety, we propose an improved AFS architecture based on the functional safety analysis.

We study how to spread k tokens of information to every node on an n-node dynamic network, the edges of which are changing at each round. This basic gossip problem can be completed in O(n + k) rounds in any static network, and determining its complexity in dynamic networks is central to understanding the algorithmic limits and capabilities of various dynamic network models. Our focus is on token-forwarding algorithms, which do not manipulate tokens in any way other than storing, copying and forwarding them. We first consider the strongly adaptive adversary model where in each round, each node first chooses a token to broadcast to all its neighbors (without knowing who they are), and then an adversary chooses an arbitrary connected communication network for that round with the knowledge of the tokens chosen by each node. We show that Ω(nk/log n + n) rounds are needed for any randomized (centralized or distributed) token-forwarding algorithm to disseminate the k tokens, thus resolving an open problem raised in [KLO10]. The bound applies to a wide class of initial token distributions, including those in which each token is held by exactly one node and well-mixed ones in which each node has each token independently with a constant probability. Our result for the strongly adaptive adversary model motivates us to study the weakly adaptive adversary model where in each round, the adversary is required to lay down the network first, and then each node sends a possibly distinct token to each of its neighbors. We propose a simple randomized distributed algorithm where in each round, along every edge (u, v), a token sampled uniformly at random from the symmetric difference of the sets of tokens held by node u and node v is exchanged. We prove that starting from any well-mixed distribution of tokens where each node has each token independently with a constant probability, this algorithm solves the k-gossip problem in O((n + k) log n log k) rounds with high probability over the initial token distribution and the randomness of the protocol. We then show how the above uniform sampling problem can be solved using Õ(log n) bits of communication, making the overall algorithm communication-efficient. We next present a centralized algorithm that solves the gossip problem for every initial distribution in O((n + k) log2 n) rounds in the offline setting where the entire sequence of communication networks is known to the algorithm in advance. Finally, we present an -round centralized offline algorithm in which each node can only broadcast a single token to all of its neighbors in each round.

We study randomized gossip-based processes in dynamic networks that are motivated by information discovery in large-scale distributed networks such as peer-to-peer and social networks. A well-studied problem in peer-to-peer networks is resource discovery, where the goal for nodes (hosts with IP addresses) is to discover the IP addresses of all other hosts. Also, some of the recent work on self-stabilization algorithms for P2P/overlay networks proceed via discovery of the complete network. In social networks, nodes (people) discover new nodes through exchanging contacts with their neighbors (friends). In both cases the discovery of new nodes changes the underlying network --- new edges are added to the network --- and the process continues in the changed network. Rigorously analyzing such dynamic (stochastic) processes in a continuously changing topology remains a challenging problem with obvious applications.

Aspnes et al introduced an innovative game for modeling the containment of the spread of viruses and worms (security breaches) in a network. In this model, nodes choose to install anti-virus software or not on an individual basis while the viruses or worms start from a node chosen uniformly at random and spread along paths consisting of insecure nodes. They showed the surprising result that a pure Nash Equilibrium always exists when all nodes have identical installation costs and identical infection costs. In this paper we present a substantial generalization of the model of that allows for arbitrary security and infection costs, and arbitrary distributions for the starting point of the attack. More significantly, our model GNS(d) incorporates a network locality parameter d which represents a hop-limit on the spread of infection as accounted for in the strategic decisions, due to either the intrinsic nature of the infection or the extent of neighborhood information that is available to a node. We determine that the network locality parameter plays a key role in the existence of pure Nash equilibria (NE): local (d = 1) and global games (d = ∞) have pure NE, while for GNS(d) games with 11.5n) of achieved for a special case of our global model. We study the characteristics of NE and the quality of our approximations empirically in two distinct classes of graphs: random geometric graphs and power law graphs. We find that in local and global games on these real-world networks, best response dynamics converge in linear or sub-linear time and have costs comparable to the social optimum. Finally, we study the performance of our approximation algorithms, and find that the approximation guarantees with respect to social cost are much better in practice than our theoretical bounds.