Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.publishedVersio

Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text

The wide use of social media and digital technologies facilitates sharing various news and information about events and activities. Despite sharing positive information misleading and false information is also spreading on social media. There have been efforts in identifying such misleading information both manually by human experts and automatic tools. Manual effort does not scale well due to the high volume of information, containing factual claims, are appearing online. Therefore, automatically identifying check-worthy claims can be very useful for human experts. In this study, we describe our participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing steps and applied different models to identify whether a given text is worthy of fact checking or not. We use the oversampling technique to balance the dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models for the experiments. We used BERT-m for the official submissions and our systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English, respectively. In further experiments, our evaluation shows that transformer models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and English languages where a different scenario is observed for Spanish.Comment: Accepted in CLEF 202

Design of triple-band h slot patch antenna

This paper attempts to design a triple band h-slot antenna by using feed line technique. These bands cover GSM mobile phone system (0.9 and 1.8 GHz) and ISM band which is used for Bluetooth and wireless local area network bands applications. The CST microwave studio software is used as a tool for simulation. This antenna is an attractive candidate for important applications like mobile phone communication systems, mobile phone jammer application, and so on

Machine Learning Technique Based Fake News Detection

False news has received attention from both the general public and the scholarly world. Such false information has the ability to affect public perception, giving nefarious groups the chance to influence the results of public events like elections. Anyone can share fake news or facts about anyone or anything for their personal gain or to cause someone trouble. Also, information varies depending on the part of the world it is shared on. Thus, in this paper, we have trained a model to classify fake and true news by utilizing the 1876 news data from our collected dataset. We have preprocessed the data to get clean and filtered texts by following the Natural Language Processing approaches. Our research conducts 3 popular Machine Learning (Stochastic gradient descent, Na\"ive Bayes, Logistic Regression,) and 2 Deep Learning (Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms. After we have found our best Naive Bayes classifier with 56% accuracy and an F1-macro score of an average of 32%

Design and analysis of triple-band microstrip patch antenna with h-shaped slots

Multi-band antennas are very important in many application systems such as mobile phone jammer. A new shape triple-band microstrip antenna is proposed in this paper. By embedding h-shaped slots placed in the centre of a microstrip patch, the triple-band character can be achieved. Procedures to select the length and location of the h-shaped slots were discussed in detail. The required antenna gain, input impedance, radiation pattern and return losses were achieved

Monitoring In-Home Emergency Situation and Preserve Privacy using Multi-modal Sensing and Deep Learning

Videos and images are commonly used in home monitoring systems. However, detecting emergencies in-home while preserving privacy is a challenging task concerning Human Activity Recognition (HAR). In recent years, HAR combined with deep learning has drawn much attention from the general public. Besides that, relying entirely on a single sensor modal-ity is not promising. In this paper, depth images and radar presence data were used to investigate if such sensor data can tackle the challenge of a system's ability to detect abnormal and normal situations while preserving privacy. The recurrence plots and wavelet transformations were used to make a two-dimensional representation of the presence radar data. Moreover, we fused data from both sensors using data-level, feature-level, and decision-level fusions. The decision-level fusion showed its superiority over the other two techniques. For the decision-level fusion, a combination of the depth images and presence data recurrence plots trained first on convolutional neural networks (CNN). The output was fed into support vector machines, which yielded the best accuracy of 99.98%.acceptedVersio

Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis

The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,605 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community. In the spirit of further research, we plan to make this dataset and our experimental resources publicly accessible to the wider research community.Comment: Zero-Shot Prompting, Few-Shot Prompting, LLMs, Comparative Study, Fine-tuned Models, Bangla, Sentiment Analysi

Addressing scale-up challenges and enhancement in performance of hydrogen-producing microbial electrolysis cell through electrode modifications

Bioelectrohydrogenesis using a microbial electrolysis cell (MEC) is a promising technology for simultaneous hydrogen production and wastewater treatment which uses electrogenic microbes. Microbial activity at the anode and hydrogen evolution reaction at the cathode can be controlled by electrode–microbe interaction and electron transfer. The selection of anode electrode material is governed by electrochemical oxidation of substrates and subsequent electron transfer to the anode. Similarly, a good cathodic material should reduce the overpotential at the cathode and enhance the hydrogen evolution reaction and H2 recovery. This review mainly focused on modifications in electrode materials and cheaper novel alternatives to improve the performance for MEC and overcome its scale-up challenges for practical applications. Performance of various anode and cathode materials based on Ni alloys, stainless steel, polyaniline, palladium, and carbon has been discussed. The scalability of the material should consider its inexpensive fabrication procedure and efficiency. NPRP grant NPRP12S-0304-190218 from the Qatar National Research Fund (a member of Qatar Foundation)

Molecular docking evaluation of celecoxib on the boron nitride nanostructures for alleviation of cardiovascular risk and inflammatory

Celecoxib (CXB) is a nonsteroidal anti-inflammatory drug (NSAID) that can be used to treat rheumatoid arthritis and ischemic heart disease. In this research, density functional theory (DFT) and molecular docking simulations were performed to study the interaction of boron nitride nanotube (BNNT) and boron nitride nanosheet (BNNS) with CXB and its inhibitor effect on pro-inflammatory cytokines. The calculated adsorption energies of CXB with the BNNT were determined in aqueous phase. The results revealed that adsorption of CXB molecule via its SO2 group on BNNT is thermodynamically favored than the NH2 and CF3 groups in the solvent environment. Adsorption of CXB on BN nanomaterials are weak physisorption in nature. This can be attributed to the fact that both phenyl groups in CXB are not on the same plane and require significant activation energies for conformational changes to obtain greater H-π interaction. Both BNNT and BNNS materials had huge sensitivity in electronic change and short recovery time during CXB interaction, thus having potential as molecular sensor and biomedical carrier for the delivery of CXB drug. IL-1A and TNF-α were implicated as vital cytokines in diverse diseases, and they have been a validated therapeutic target to manage cardiovascular risk in patients with inflammatory bowel disease. A molecular docking simulation confirms that the BNNT loaded CXB could inhibit more pro-inflammatory cytokines including IL-1A and TNF-α receptors as compared to BNNS loaded to CXB

The elevation in circulating anti-angiogenic factors is independent of markers of neutrophil activation in preeclampsia

Background - Severe preeclampsia is associated with increased neutrophil activation and elevated serum soluble endoglin (sEng) and soluble Flt-1 (sFlt-1) in the maternal circulation. To dissect the contribution of systemic inflammation and anti-angiogenic factors in preeclampsia, we investigated the relationships between the circulating markers of neutrophil activation and anti-angiogenic factors in severe preeclampsia or systemic inflammatory state during pregnancy. Methods and results - Serum sEng, sFlt-1, placenta growth factor, interleukin-6 (IL-6), calprotectin, and plasma a-defensins concentrations were measured by ELISA in 88 women of similar gestational age stratified as: severe preeclampsia (sPE, n = 45), maternal systemic inflammatory response (SIR, n = 16) secondary to chorioamnionitis, pyelonephritis or appendicitis; and normotensive controls (CRL, n = 27). Neutrophil activation occurred in sPE and SIR, as a-defensins and calprotectin concentrations were two-fold higher in both groups compared to CRL (P < 0.05 for each). IL-6 concentrations were highest in SIR (P < 0.001), but were higher in sPE than in CRL (P < 0.01). sFlt-1 (P < 0.001) and sEng (P < 0.001) were ˜20-fold higher in sPE compared to CRL, but were not elevated in SIR. In women with sPE, anti-angiogenic factors were not correlated with markers of neutrophil activation (a-defensins, calprotectin) or inflammation (IL-6). Conclusions - Increased systemic inflammation in sPE and SIR does not correlate with increased anti-angiogenic factors, which were specifically elevated in sPE indicating that excessive systemic inflammation is unlikely to be the main contributor to severe preeclampsia