This paper addresses the problem of estimating the poses of multiple objects under sparse and noisy data in robotics systems in unknown environments. Due to the data sparsity caused by limited data acquisition policies for saving resources and the data noises caused by environmental disturbance, the robustness of the conventional methods for the estimation degenerates. This paper proposes a robust multiobject pose estimation method to solve the problem, which can be applicable to not only ground environments but also space environments. First, a robot builds a map as a model its surrounding environments by simultaneous localization and mapping (SLAM). Then, multiple objects are detected by a visual sensor with a deep learning-based algorithm. Finally, the poses of multiple objects are robustly estimated by probabilistic filters with range data and localize them in the built map. The performance of the proposed method was tested with a real robotic system with visual and range sensors. Experimental results showed that the proposed method can robustly estimate the poses of multiple objects under sparse and noisy data.
Packet classification is the function that eliminates or forwards arriving packets at a network device according to packet classification policies. Packet classification using rule lists is widely used because of its low memory requirements and ease of implementation. However, as the number of rules increases, the classification time increases, and the packet communication latency increases. In this paper, we propose a method that rewrites rules to eliminate the precedence constraint and places the rule with high matching frequency at a higher position. This method of expanding and restructuring rules, which makes rule replacement easier, can reduce latency quickly without disrupting network equipment operation. Computer experiments using ClassBench [1], a benchmark for packet classification algorithm, confirm the effectiveness of the proposed method.
This paper presents a comprehensive comparison between Reduced Capability (RedCap) and 5G New Radio (NR). RedCap is the first standardized 5G technology for the Internet of Things (IOT). RedCap devices were introduced in third Generation Partnership Project (3GPP) Release 17 as a NR light with the target of cost-efficiency and energy saving using narrower bandwidth and lower modulation complexity. Redcap bridges the Narrow Band IOT and 5G NR. Reducing complexity in modulation, bandwidth, antenna configuration, and modem capabilities, significantly reduced the cost and energy consumption. RedCap and 5G NR broadband devices in terms of the specification, throughput and architectures focused on power consumption in band n78 are compared. The reduced capabilities in RedCap devices make them simplified front end modules with lower complexity and power consumption. The results highlight design trade-offs relevant to developing IOT and consumer RedCap devices in mid-band spectrum. Scattering parameters, gain, and throughput are presented by Advanced Designed System (ADS) and MATLAB simulation. The results confirm that RedCap devices achieve sufficient throughput and energy efficiency for a wide range of low data rate use cases, such as smartwatches and industrial sensors.
This paper presents a novel Ultra-Wideband Orthogonal Frequency Division Multiplexing with Index Modulation (UWB-OFDM-IM) system, which combines the spectral efficiency of OFDM-IM with the robustness of UWB in multipath environments. The proposed system divides subcarriers into groups, activating only a subset to transmit QPSK-modulated symbols while leveraging subcarrier indices to carry additional information bits. The simulation results of the proposed technique compared with conventional UWB-OFDM. The results are as follows: 8.5 dB SNR gain at a BER of 10-3, a 1.8 dB reduction in PAPR, and a 6.02 dB improvement in spectral containment at 5 GHz. These improvements make UWB-OFDM-IM suitable for energy-constrained IoT applications, offering reliable connectivity in dense and multi-path environments.
Authentication is a critical security component in both cloud and IoT environments, and Keystone offers multiple methods to perform various deployment scenarios and security requirements. This paper presents a comprehensive analysis of authentication mechanisms available in OpenStack's identity service, Keystone. It systematically examines four key authentication methods: Password Authentication, Time-based One-Time Passwords (TOTP), OAuth Authentication, and Application Credential Authentication. Through hands-on demonstrations and comparative analysis, we evaluated the strengths and limitations of each method. The findings aim to guide cloud and IoT administrators and architects, specifically in 5G networks, in selecting and implementing the most suitable authentication methods for their OpenStack deployments based on their specific security requirements, integration needs, and operational constraints.
