Abstract
A low-cost single-layer substrate-integrated waveguide (SIW) cavity-backed slot antenna is proposed for millimeter-wave applications. The structure is designed to operate at the W-band. The T-shaped slot antenna is placed on the back-side of the SIW and fed by a grounded coplanar waveguide (GCPW) transmission line. A transition between the (GCPW) and the SIW is also designed. The simulated results provide that the antenna has a stable gain over the frequency range (98.79-100.56) GHz with a maximum value of around 6 dBi also high radiation efficiency.

Abstract
This paper proposes the use of a Gaussian window on the array factor as an interference mitigation method, aiming to avoid the computational complexity of the MVDR algorithm at the cost of a slight performance reduction. We show that by optimizing the parameters of the Gaussian window, it is possible to effectively mitigate the interfering signal if it is received within a certain angular range from the desired signal, while being still effective beyond that range. Finally, we show that the effectiveness of this approach is maintained across the full frequency reception range of the Ka-band, and confirm its validity using 8 × 8 and 16 × 16 array sizes. © 2022 IEEE.

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

Abstract
The subjectiveness of multimedia content description has a strong negative impact on tag-based information retrieval. In our work, we propose enhancing available descriptions by adding semantically related tags. To cope with this objective, we use a word embedding technique based on the Word2Vec neural network parameterized and trained using a new dataset built from online newspapers. A large number of news stories was scraped and pre-processed to build a new dataset. Our target language is Portuguese, one of the most spoken languages worldwide. The results achieved significantly outperform similar existing solutions developed in the scope of different languages, including Portuguese. Contributions include also an online application and API available for external use. Although the presented work has been designed to enhance multimedia content annotation, it can be used in several other application areas.

Abstract
Decision trees are often preferred when implementing Machine Learning in embedded systems for their simplicity and scalability. Hoeffding Trees are a type of Decision Trees that take advantage of the Hoeffding Bound to allow them to learn patterns in data without having to continuously store the data samples for future reprocessing. This makes them especially suitable for deployment on embedded devices. In this work we highlight the features of a HLS implementation of the Hoeffding Tree. The implementation parameters include the feature size of the samples (D), the number of output classes (K), and the maximum number of nodes to which the tree is allowed to grow (Nd). We target a Xilinx MPSoC ZCU102, and evaluate: the design's resource requirements and clock frequency for different numbers of classes and feature size, the execution time on several synthetic datasets of varying sizes (N) and the execution time and accuracy for two datasets from UCI. For a problem size of D=3, K=5, and N=40000, a single decision tree operating at 103MHz is capable of 8.3x faster inference than the 1.2 GHz ARM Cortex-A53 core. Compared to a reference implementation of the Hoeffding tree, we achieve comparable classification accuracy for the UCI datasets.

Abstract
Premature ventricular contractions (PVC), or extrasystoles, represent a type of cardiac arrhythmia that is common among the general population and, notably, among athletes or individuals who exercise frequently. PVC may be asymptomatic and not clinically relevant when their rate is low, up to around 0.5%, or may be symptomatic and clinically relevant when it is high, in the order of or above 10%. ECG analysis in association with a cardiac stress test is important to detect and characterize PVC and to diagnose the heart condition and operation. In this paper, we describe and test a simple signal processing approach that can be used to effectively pinpoint subtle PVC occurrences in various physical effort conditions. In this regard, we discuss i) three important conditions to be met such that PVC are categorized as benign, ii) the design and implementation of a cardiac stress test and ECG data collection, iii) the algorithm analyzing and extracting information from the detected PVC occurrences, and iv) we present and discuss the obtained results, and conclude on their significance. © 2022 IEEE.