SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving

In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, i.e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud. To overcome the lack of annotated point cloud data, in particular for the road segments, we introduce an auto-labeling process which transfers automatically generated labels from the camera to LiDAR. We also explore the role of imagelike projection of LiDAR data in semantic segmentation by comparing BEV with spherical-front-view projection and show that SalsaNet is projection-agnostic. We perform quantitative and qualitative evaluations on the KITTI dataset, which demonstrate that the proposed SalsaNet outperforms other state-of-the-art semantic segmentation networks in terms of accuracy and computation time. Our code and data are publicly available at https://gitlab.com/aksoyeren/salsanet.git

Energy saving market for mobile operators

Ensuring seamless coverage accounts for the lion's share of the energy consumed in a mobile network. Overlapping coverage of three to five mobile network operators (MNOs) results in enormous amount of energy waste which is avoidable. The traffic demands of the mobile networks vary significantly throughout the day. As the offered load for all networks are not same at a given time and the differences in energy consumption at different loads are significant, multi-MNO capacity/coverage sharing can dramatically reduce energy consumption of mobile networks and provide the MNOs a cost effective means to cope with the exponential growth of traffic. In this paper, we propose an energy saving market for a multi-MNO network scenario. As the competing MNOs are not comfortable with information sharing, we propose a double auction clearinghouse market mechanism where MNOs sell and buy capacity in order to minimize energy consumption. In our setting, each MNO proposes its bids and asks simultaneously for buying and selling multi-unit capacities respectively to an independent auctioneer, i.e., clearinghouse and ends up either as a buyer or as a seller in each round. We show that the mechanism allows the MNOs to save significant percentage of energy cost throughout a wide range of network load. Different than other energy saving features such as cell sleep or antenna muting which can not be enabled at heavy traffic load, dynamic capacity sharing allows MNOs to handle traffic bursts with energy saving opportunity.Comment: 6 pages, 2 figures, to be published in ICC 2015 workshop on Next Generation Green IC

Gülen sect: Reached for the state, got capital instead

A religious sect now defies the strongest political party in Turkey. There must be a reason for this alarming self-confidence. Is it rooted in history; that is, does the sect have a long heritage? Not really -it is a movement that started to take shape in the 1970s. What about economic clout? Well, sort of; but in a country where each transaction must be approved by the state, economic force can translate into business investment only as far as the state allows it

Grant-free Radio Access IoT Networks: Scalability Analysis in Coexistence Scenarios

IoT networks with grant-free radio access, like SigFox and LoRa, offer low-cost durable communications over unlicensed band. These networks are becoming more and more popular due to the ever-increasing need for ultra durable, in terms of battery lifetime, IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this paper, we study the impact of coexisting competing radio access technologies on the system performance. Considering \mathpzc K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, and experienced delay as a function of distance to the serving access point. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how introduction of a new coexisting technology may degrade the system performance in terms of success probability and battery lifetime. We further investigate solutions in which this destructive effect could be compensated, e.g., by densifying the network to a certain extent and utilizing joint reception

Channel-predictive link layer ARQ protocols in wireless networks

Communication performance over a wireless channel should be considered according to two main parameters: energy and throughput. The reliable data transfer is a key to these goals. The reliable node-to-node data transfer is performed by link layer protocols. One prominent approach is Automatic Repeat Request (ARQ) protocol. The traditional ARQ protocols attempt to recover the erroneously transmitted frames by retransmitting those frames, regardless of the channel state. Since this channel state unaware behaviour may cause unnecessary retransmissions, traditional ARQ protocols are expected to be energy inefficient. Some ideas have been proposed such as stochastic learning automaton based ARQ, and channel probing based ARQ. However, these algorithms do not attempt to estimate the channel\u27s existing condition. Instead, the retransmission decision is made according to a simple feedback, on whether the previous frame was successful. This thesis presents four proposed algorithms, which incorporates the channel state estimate in the feedback process to judiciously select a frame (re)transmission timing instant. Algorithms have been applied on Stop-and-Wait (S-W) ARQ, and the performance have been compared with respect to simple S-W ARQ, and probing based S-W ARQ. In probing based ARQ, when the channel deteriorates, transmitter starts probing channel periodically, but the periodicity is chosen arbitrarily, regardless of the fading state. In contrast, the proposed algorithms estimate the channel\u27s existing condition by using feedbacks, and the probing interval is chosen according to the Average Fading Duration (AFD) of received signal. Simulations are performed with Rayleigh Fading Channel. The performance results show that at the cost of some additional delay, significiant gain on energy saving and throughput performance can be achieved when AFD based intelligent probing is done