Vodafone (Germany)

Handheld ultrasound (HHUS) devices are increasingly used in clinical practice due to their portability and cost-effectiveness. However, HHUS image quality remains variable, and a standardized expert-based evaluation protocol is lacking. This study compares the image quality of B-mode recordings of five HHUS models with a high-end reference system (HEUS) using a blinded assessment to identify differences, covariates, offering a standardized evaluation framework.In this blinded, multi-center study, video sequences of the liver, pancreas, and sigmoid colon were recorded in ten volunteers using 5 HHUS devices - Vscan Air, Butterfly iQ+, Philips Lumify, Clarius C3, and Clarius C3 HD3 - and one HEUS (GE Logiq E10). Twenty-two physicians with many years of abdominal ultrasound experience rated blinded clips for 5 image quality parameters.The HEUS consistently received the highest ratings. Image quality differed significantly between HHUS devices. Clarius systems showed the smallest deviation from the reference, followed by Vscan Air and Philips Lumify. All image parameters significantly correlated with the overall impression, without a single dominant factor. The patients' BMI had no relevant influence. Examiner experience was less impactful than device familiarity and personal preference.The image quality of HEUS remained superior to all tested HHUS devices. Further subdivision of image quality aspects did not provide additional information. Furthermore, we could not identify significant covariates in our setup. These findings could justify simplified setups with reduced questionnaires and requirements for examiners.

The integration of extremely large (XL) antenna arrays with ultra wideband (UWB) communication is pivotal for next-generation networks. However, the multitude of candidate beams renders conventional analog phase shifters (APSs)-based training prohibitively costly, necessitating true-time delay (TTD)-based frequency-dependent beamforming. We design a codebook mapping distinct orthogonal frequency-division multiplexing (OFDM) subcarriers to unique spatial angles, enabling full angular coverage within a single OFDM symbol. To address practical TTD limitations–finite delay range and resolution–we propose a multi-stage delay-phase-precoding (MSDPP) architecture that mitigates hardware-induced distortions. Simulations demonstrate that MSDPP achieves near-optimal spectral efficiency (SE) with at least a 2 dB gain over state-of-the-art designs, alongside up to 63% higher energy efficiency (EE).

In joint communication and sensing (JC&S) systems, parameter estimation enables localization, synchronization, interference mitigation, and initial beam acquisition. The accuracy of these estimates is constrained by synchronization quality and the presence of linear and non-linear distortions affecting the sensing signal. For angle estimation, a wide range of algorithms have been proposed in the literature. This work evaluates the resilience of true-time delay (TTD), minimum variance distortionless response (MVDR), and rootMUSIC angle estimation approaches to phase impairments. The comparison is based on the mean absolute error (MAE) of angle of arrival (AoA) estimates. The results show that under typical impairment scenarios, all estimators can achieve an MAE below 1°. Moreover, the TTD approach offers significantly lower computational complexity compared to MVDR and rootMUSIC.

The instruction set architecture of vector processors operates on vectors stored in the vector register file which needs to handle several concurrent accesses by functional units with multiple ports. When the vector processor is running with high utilization, access conflicts become a major source of performance degradation. With a software model of a vector processor, we take a deep dive into the runtime impact of conflicts and their characteristics, and on ways to manage them, i.e., avoidance, resolution, and mitigation. For conflict avoidance, we study the existing approaches of banking with different static bank layouts and propose a dynamic bank layout to overcome their shortcomings. Our approach assigns newly written registers a temporarily unique starting bank. For conflict resolution, we compare different arbitration algorithms and optimize round-robin arbitration for mixed-width arithmetics by prioritizing wide operands. For conflict mitigation, operand queues of varying depths are studied. Our inventions are likely to increase the area efficiency of vector processors, either because they allow to use shallower operand queues while keeping the same performance, or reduce the area even further by using less banks, albeit at a performance impairment of 10% or less. The insights of the study can further be applied to other shared memory systems.

Utilizing the vast bandwidths available in the THz and sub-THz bands poses significant challenges, particularly with regard to the power consumption of the analog front end. For example, the analog-to-digital converter (ADC) power consumption is suspected to increase significantly due to the increase in bandwidth. As shown in prior work, reducing the ADC’s amplitude resolution down to 1-bit and using tailored modulation schemes, such as zero-crossing modulation (ZXM), can partly mitigate this issue. However, the power amplifier (PA) at the transmitter typically dominates the overall power consumption. Thus, in this work we evaluate the PA efficiency of ZXM under non-linear distortions and propose two techniques to enhance PA efficiency: i) offsetting the in-phase and quadrature components by half a symbol period, and ii) maximizing output power by allowing strong distortions. Our results demonstrate that combining these approaches achieves high amplification efficiency and leads to performance gains in terms of symbol error rate.

Industrial Ethernet networks are well-established communication systems in industrial production facilities. They are used in particular in applications with high demands on real-time capability and transmission reliability. In the context of applications with mobility requirements, such as mobile robots or rotating machine parts, however, they reach their practicable limits. In these cases, wireless communication systems are necessary. In addition to enabling the aforementioned applications, they promise further advantages, such as cost savings through simplified installation. However, the same requirements are placed on wireless systems as on their wired counterparts. This paper structures these requirements’ implications on industrial communication systems by deriving four mandatory properties that need to be fulfilled by any communication system for industrial applications. Current commercially available technologies are reviewed with respect to the mandatory properties. Addressing their shortcomings, an overview of current research approaches aiming to improve industrial wireless systems in the automation applications is given.

Dilated convolutions have become popular in recent Deep Neural Networks (DNNs). However, they introduce additional zeros within the weights. To efficiently compute these layers within image processing DNNs, we implemented a 10 TOPS neural network SoC in 22 nm that supports dilated convolutions with reduced energy consumption. By exploiting this advantage, the energy can be reduced by a factor of 4.46 even for DNNs such as DeepLabV3+ with a majority of standard convolutional layers. Keywords: accelerator, DNN, dilated convolution, image pipeline

As one of the promising intelligent transportation frameworks, vehicular platooning has the potential to bring about sustainable and efficient mobility solutions. One of the challenges in the development of platooning is maintaining the string stability, which ensures that there is no amplification of the signal of interest along the platoon chain. String stability is dependent on reliable inter-vehicle communications and proper controller design. Therefore, in this paper, we formulate a radio resource management (RRM) problem with the purpose of satisfying the reliability of the vehicle-to-vehicle (V2V) links and string stability of the platoon. We tackle the optimization problem from different angles. First, we devise centralized classical approaches based on difference of two convex functions (d.c.) programming, in which we assume the base station (BS) has full knowledge over the V2V channel gains. In the second strategy, we develop decentralized resource allocation approaches based on multi-agent reinforcement learning (MARL). In essence, we model each transmitter vehicle in the platoon as an autonomous agent that tries to find an optimal policy according to its local estimated information to maximize the total expected reward. We also investigate whether the integration of federated learning (FL) with decentralized MARL algorithms can bring any potential benefits. This comparison between classical and machine learning (ML)-based RRM strategies helps us make crucial observations in terms of robustness, sensitivity, and efficacy of the policies that are learned by reinforcement learning (RL)-based resource allocation algorithms.

As we move towards the era of 6G and IoE (Internet of Everything), there is more and more seamless interconnection and interaction among things, people, data, and processes in one intelligent network. In IoE, whether 5G or 6G, the ecosystem embodies a plethora of connected devices and people, user-context heterogeneity, and fully distributed interactions, adding up to a highly complex ecosystem. The 5G will increase cellular bandwidth by huge amounts, making it much easier for the IoE to network large numbers of devices together.Adaptation to the right situational context, especially when it comes to security and protection has become a key requirement for IoE and other complex ecosystems. Especially the Access Control (AC) part is required to be robust, scalable, and resilient (from a security point of view). A dynamic, context-aware security with a breakout from the traditional statically mapped roles and mechanisms is essential today.Robust authentication can be achieved for IoT and IoE using a technique called Device Fingerprinting (DF) that we thoroughly investigate in this paper. We look at the mechanism itself, where and how it applies, and how Access Control (AC) works and scales with DF. We also model the DF generation process and look at different cases with different devices, and the respective overhead incurred by DF compared to the payload as well as the security overhead incurred for different frame sizes and devices as a percentage of the overall traffic (DF and payload combined). All models and assessments provided serve as a basis for future publications based on the continuous ongoing work we are doing in this area.