This paper presents a methodology of adapting complete open-source digital tooling, ISA, IPs and manufacture-able PDKs to tapeout a minimalist RISC-V based SoC.

Forest inventory forms the foundation of forest management. Remote sensing (RS) is an efficient means of measuring forest parameters at scale. Remotely sensed species classification can be used to estimate species abundances, distributions, and to better approximate metrics such as above ground biomass.  State of the art methods of RS species classification rely on deep learning models such as convolutional neural networks (CNN). These models have 2 major drawbacks: they require large samples of each species to classify well and they lack explainablity. Therefore, rare species are poorly classified causing poor approximations of their associated parameters. We show that the classification of rare species can be improved by as much as 8 F1-points using a neuro-symbolic (NS) approach that combines CNNs with a NS framework. The framework allows for the incorporation of domain knowledge into the model through the use of mathematically represented rules, improving model explainability.

Driving requires continuous decision making from a driver taking into account all available relevant information. Automating driving tasks also automates the related decisions. However, humans are very good at dealing with bad quality, fuzzy, informal and incomplete information, whereas machines generally require solid quality information in a formalized format. Therefore, the development of automated driving functions relies on the availability of machine-usable information. A digital twin contains quality controlled information collected and augmented from different sources, ready to be supplied to such an automated driving function. An information model that describes all conceivably relevant information is necessary. To this end, a list of requirements that such an information model should meet is proposed and each requirement is argued for. Based on the anticipated services and applications that such a system should support, a collection of requirements for system architecture is derived. Information modeling is performed for selected relevant information groups. A system architecture has been proposed and validated with three different implementations, addressing several different applications to support decisions at a highway tunnel construction site in Austria and throughout the Test Bed Lower Saxony in Germany.

This is the final part of the two- part series paper, where the simulation results namely leg’s trajectory, path tracing, obsacles detected, path constructed, movements of three motors and test like two-element chain three- element chain, are presented. I have left r&d, so will not correct update or complete papers. The earlier version of two parts, which was able to get saved from computer crash is uploaded.

We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example.

Monitoring arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Thus, several efforts have been made to develop an artificial intelligence-based hypotension prediction index. Nevertheless, the use of these indices is limited because they may not provide a convincing interpretation of the association between predictors and hypotension. Herein, we developed an interpretable deep learning model that forecasts hypotension occurrences 10 min before a given 90 s ABP record. Internal and external validations of model performance reported the area under the receiver operating characteristic curve (AUC) as 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted by using predictors representing ABP trends that are automatically generated in the proposed model. Ultimately, we demonstrate high-applicability of a deep learning model that has a high accuracy performance and provides an interpretation of the association between ABP trends and hypotension in clinical practices.

Recent study suggests dynamical topological phase could be derived into additional dimension of time. By adding interpretation of this phenomenon with a topological interaction within simplified model, possibility of approaching many-timeline in real world could be suggested.

When Letizia Jaccheri became department head, she realized that she was the only female in both the leader group of her Science, technology, engineering, and mathematics (STEM) faculty and in the leader group of her department of computer science. While the percentage of female students in STEM at the university had reached almost 30% thanks to actions to which she had contributed for almost 20 years, the number of female professors in STEM was only 13%. It was then that the idea of the IDUN project was born. The central measure in the project was the employment of nine female adjunct professors, at least one at each of the seven institutes of the faculty. IDUN provided training for these professors to become role models and mentors for 3-5 mentees each. The present paper aims to give a summary of the project, as well as looking at the consequences and future implications.

This is the third part of four-part series paper, where the control dynamics built on transtibial mathematical model is explained. The trajectory development and control dynamics equations with respect to obstacles detected is explained with popular algorithms employed in robotics and self-driving vehicles but . In this paper, it is mathematically and practically proved that Lagrangian of the kinetic and potential energy of artificial leg can never be equal to zero and is always equal to non-conservative forces and thus, any model based on the assumption of conservative forces is fully invalid practically. I have left r&d, so will not correct update or complete papers. The earlier version of two parts, which was able to get saved from computer crash is uploaded.

We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions at a constant level even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.

In this paper we derive a method to determine the worst-case radiated immunity of a given printed circuit board (PCB) layout, whose benefits are threefold. First, the worst-case performance could be used as a reference to compare the radiated immunity of two competing, schematically-identical-yet-electromagnetically-distinct layouts. Second, it could be used as a criterion to determine whether a given layout is able to observe the particular requirements of a given electromagnetic compatibility (EMC) standard. And finally, since this method is element-independent, it requires no modelling for complex circuit elements such as non-linear elements, logic gates, processors, etc. to interact with electromagnetic interference (EMI). Our method determines how much power could be delivered to the non-linear circuit elements of the PCB by an EMI under a worst-case scenario. This could be done, either for the entire PCB, or for specific parts and elements of interest, such as sensitive elements, logic gates, microprocessors, etc. While a fully analytical approach with a closed-form solution is developed for the former, the latter could be obtained through an analytical derivation followed by numerical maximization. These methods would enable the rapid and straightforward evaluation of the designs’ immunity at any stage, resulting in saving cost and time.

In medium to high frequency power transformers, apart from minimizing the power loss in the core and copper, the design of its thermal circuit plays an important role in containing the temperature rise in different parts. It is extremely complex because there exist multiple loss centers with different heat removal characteristics. Moreover, the power loss is concentrated around a small core volume and the rest of it is available for convection and radiation. This article proposes to use a hybrid core configuration to improve the heat removal feature of the transformer. This article establishes through practical validation on suitability i.e., the magnetic compatibility of cores in the hybrid magnetic circuit. It further explores the application prospect of the proposed concept where the thermal performances have been compared using different core types in two different application domains. In the first case, the core could always remain energized to its rated value, and in the second design, both the windings could always draw their respective rated current.

Compressing video sequences with different content complexity results in different bitrates for the same quality level or in different quality levels for the same bitrate; for instance it is well known that content with high spatial complexity and/or high motion requires high bitrates for compression with adequate quality. To address this, per-title optimization is used recently (e.g., by Netflix) to generate appropriate rate-quality representations for different Video on demand (VoD) content to be streamed via adaptive video streaming. However, this cannot be adopted for live video streaming as it requires encoding (multiple times) each video content. Spatial Information (SI) and Temporal Information (TI) have been often used as indicators of video complexity, for instance for preparing and describing content for video quality assessment tests, and for rate-distortion modeling. However, it has been questioned recently if different metrics could lead to a better estimation of ”compressibility” of video. In this paper we compare existing and proposed metrics in terms of their ability to estimate ”compressibility”. This supports quality-rate estimation and the possibility to create appropriate ”quality ladders” (different quality representations) for adaptive live video streaming. We observe in particular that metrics related to the variance of pixel values provide a good estimation of compressiblity for the considered datasets.

This paper presents a fully integrated second-order level-crossing sampling data converter for sensing sparse signals with real-time detection of turning points. Compared with con- ventional level-crossing sampling ADCs which sample at fixed voltage levels, the proposed circuits update tracking thresholds using linear prediction, which forms sloped sampling levels. The prediction and calculation are done digitally by modifying the digital control logic of a conventional SAR ADC. The system sepa- rates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the quantized amplitudes of the selected sampling points and the timestamps between the selected sampling points. The main advantages of the proposed circuits are data savings and power savings for the following digital signal processing or communication circuits. The test chip was fabricated using a 180nm CMOS process. When sensing sparse signals such as ECG signals, the proposed ADC saves 30% compared to a conventional SAR ADC with an equal quantizer resolution and achieves a compression factor of 6.17.

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed.

Although the concept of engineered molecular communication has been around for quite some time, practical approaches with truly biocompatible setups are still scarce. However, molecular communication has a large potential in future medical applications and may be a solution to size constraints of antenna based transmission systems. In this work we therefore present a testbed using biocompatible magnetic nanoparticles. Based on previous work, all testbed components have been improved regarding performance and size, making a large step forwards regarding miniaturisation and a data transmission approach. In addition, a setup for localised twodimensional sensing of magnetic nanoparticles is presented. All improvements are evaluated individually and combined to achieve a net data rate of more than 6 bit/s, significantly higher than any other comparable biocompatible setup.

People have become increasingly aware of the environment in which they live in recent years. This awareness has prompted the development of a reliable environmental monitoring system. This paper presents an energy-efficient and low-power sensor node that could be used for outdoor Internet of Things (IoT) applications to monitor real-time environment conditions such as humidity and temperature remotely. The proposed sensor node could directly connect with a cellular base station over GSM/GPRS quad-bands 850MHz,900MHz,1800MHz, and 1900MHz without needing any Access Point(AP) in the network. The sensor node consists of a central controller based on Atmega328, a communication module on SIM800, DHT22 temperature, and a humidity sensor with a solar-powered charging system. The system is specially designed for robust IoT outdoor applications such as environment monitoring in wilds, large-scale farms, forests, etc. For optimal usage of the power of the battery, an energy-saving mechanism is proposed based on deep sleep scheduling, thus considerably reducing the power consumption and prolonging the up-time of the sensor node.

Cryptocurrencies acquire user confidence by making the whole creation and transaction history transparent to the public. In exchange, the transaction history accurately captures the complete range of user activities related to cryptocurrencies. In this paper, the use of data mining methods in Bitcoin transactions is analyzed and summarized. Cryptocurrencies, similar to the well-known Bitcoin, were targeted to ensure transaction security and privacy and overcome the drawbacks of traditional banking systems as well as other centralized systems. We also conduct a thorough analysis of the literature on the challenges and applications of electronic currencies. We outline the evolution of digital currency from electronic cash to cryptocurrencies and put the spotlight on the methods used to increase user privacy. We also highlight security threats in the existing cryptocurrency systems that jeopardize the privacy of Bitcoin users. Finally, we identify several research gaps and trends that need to be looked at further.