This study presents a spatiotemporal autoregressive transformer (START) that combines the strengths of spatiotemporal transformers and vector autoregression (VAR) to predict crime by type, handling nonstationary data and capturing long-term dependencies. Moreover, Start utilizes one-hot encoding to differentiate crime types, enabling the model to assign specific attention to individual crime categories. Seasonal trend decomposition using LOESS (STL) is employed to decompose time series into trend, seasonality, and remainder components by effectively capturing residual variations. VAR attention enhances prediction accuracy by focusing on specific crime types and prioritizing relevant information. The proposed approach is evaluated on publicly available crime datasets from New York, Chicago, and Los Angeles and three state-of-the-art evaluation measures: mean error (ME), mean absolute percentage error (MAPE), and root mean square error (RMSE). Our simulation results demonstrate the significance of the proposed method in enhancing crime prediction accuracy for the most critical crimes compared to state-of-the-art methods. Enhanced crime prediction may help law enforcement agencies allocate resources efficiently by focusing on the most critical areas.

Diabetes is a chronic disease with exponential growth and poses significant challenges to global healthcare. Regular blood glucose (BG) monitoring is key for avoiding diabetic complications. Traditional BG measurement techniques are invasive and minimally invasive, causing pain, discomfort, cost, and infection risks. To address these issues, we developed a noninvasive BG monitoring approach on photoplethysmography (PPG) signals using multi-view attention and cascaded BiLSTM hierarchical feature fusion approach. Firstly, we implemented a convolutional multi-view attention block to extract the temporal features through adaptive contextual information aggregation. Secondly, we built a cascaded BiLSTM network to efficiently extract the fine-grained features through bidirectional learning. Finally, we developed a hierarchical feature fusion with bilinear polling through cross-layer interaction to obtain higher-order features for BG monitoring. For validation, we conducted comprehensive experimentation on up to 6 days of PPG and BG data from 21 participants. The proposed approach showed competitive results compared to existing approaches by RMSE of 1.67 mmol/L and MARD of 17.88%. Additionally, the clinical accuracy using Clarke error grid (CEG) analysis showed 98.80% of BG values in Zone A+B. Therefore, the proposed approach offers a favorable solution in diabetes management by noninvasively monitoring the BG levels.

Cuffless blood pressure (BP) estimation is critical for managing growing concerns about hypertension and cardiovascular diseases. Despite recent advancements in multimodal (ECG and PPG) BP estimation methods, which have achieved varying degrees of success, several challenges remain to be addressed. These include capturing the full spectrum of BP-relevant information, redundant feature spaces, and handling the multigrade classification. To address these issues, we propose a Multimodal Fusion BP Network (MuFuBP-Net), featuring a novel dual-feature pipeline architecture designed to extract hierarchical and modality-specific features from both ECG and PPG signals. Additionally, the Cascading Cross-Feature Enhancer (CCFE) module integrates multiple fusion strategies with a squeeze-and-excitation mechanism to apply channel-wise attention to spatial features, enabling dynamic re-weighting. We also employed a Sequence Context Network (SCN) module to capture global sequential features. Subsequently, a Probabilistic Feature Encoder (PFE) encodes the multilevel features from both pipelines into a compact latent space, preserving their discriminative characteristics. Our approach achieved MAE $\pm$ SDE of 2.99 $\pm$ 4.37 mmHg (SBP) and 2.63 $\pm$ 4.19 mmHg (DBP) on MIMIC-II, and 2.27 $\pm$ 4.15 mmHg (SBP) and 1.63 $\pm$ 2.96 mmHg (DBP) on MIMIC-III dataset, meeting AAMI, BHS, and IEEE grade A standards. The proposed approach demonstrated competitive results compared to existing techniques, highlighting its significance as a reliable solution for cuffless BP monitoring.

3D object detection by autonomous vehicles is integral to intelligent transportation. Existing systems often compromise essential foreground point features and local spatial interactions through random down-sampling, focusing primarily on local feature extraction. However, this neglects interactions among distant yet significant points, limiting semantic information and detection performance due to inherent point cloud data sparsity. Addressing this, our proposed Geometric Feature Aggregation and Self-Attention in a Multi-Head Transformer (GFA-SMT) architecture leverages Graph Convolutional Networks and multi-channel transformers to enhance weak semantic information of distant sparse objects. GFA-SMT comprises three modules: Distance Suppression for Local Receptive Fields (DsLRF), Geometric Feature Aggregator with Multi-head Self Attention (GFaSA), and Predicted Key-point Weighting and Refinement (PKwR). DsLRF preserves foreground features, GFaSA encodes similar features and aggregates edge features, while PKwR focuses on key-points for enhancing geometric knowledge of distant and sparse objects. Extensive experiments on KITTI, DIARV2X-I and NuScenes datasets show significant enhancements in widely used techniques, resulting in notable increases in average precision (AP) for 3D object detection: 4.08%, 5.56%, and 4.62%, respectively, on the KITTI test dataset. GFA-SMT enhances point cloud detection accuracy, particularly at medium and long distances, with minimal impact on run-time performance and model parameters.

Current 3D object detection methods face limitations in both single-modal and multimodal approaches. Single-modal detectors struggle with inadequate depth perception or difficulty distinguishing semantically similar objects, while multimodal systems, which combine LiDAR and camera data, encounter challenges like integration complexity and inefficient feature fusion. This study presents ROI-based ViT Fusion (RVF3D), a multimodal 3D object detector combining sparse 3D and dense 2D data, outperforming current methods. The RVF3D enhances the understanding of rich LiDAR and camera representations through improved query generation, feature sampling, and multimodality cross-ViT fusion. We propose an ROI-based Query Sampling (RQS) multimodal 3D object identification pipeline, eliminating laborious non-maximum suppression (NMS) postprocessing and complex prior box configurations. The RQS module uses learnable filters to aggregate image and point representations, retaining foreground characteristics for object localization and recognition while removing background noise. Our RVF3D leverages a hierarchical vision transformer-based approach, ViT-Fusion, which includes CameraViT and LidarViT components for embedding representations of input data in each modality. These representations are fused to enable hierarchical learning and deeper feature extraction. Comprehensive tests on the KITTI and nuScenes datasets demonstrate that RVF3D achieves 89.88% mAP in BEV and 75.2% mAP in 3D, respectively.

The Urdu language is used by approximately 200 million people for spoken and written communications on a daily basis. There is a substantial amount of unstructured Urdu textual data that is available worldwide. Data mining techniques can be used to extract meaningful knowledge from such a large, potentially informative source of data. There are many text processing systems available to process unstructured textual data. However, these systems are mostly language specific and developed for a variety of languages such as English, Spanish, Chinese, and so on. Unfortunately, there are not as many language processing resources available for Urdu. Stemming is one of the most important preprocessing steps in the text mining process and its goal is to reduce grammatical words form, e.g., parts of speech, gender, tense, and so on, to their root form. In this work, we have extended the stemming capabilities of our existing pattern-based comprehensive stemming system for Urdu text. In addition to the existing stemming rules in previous work, we introduce novel stemming rules for prefix, and infix stemming. We also optimize the existing suffix removal rules and extend the add character lists for word normalization. These stemming rules are generic and have the ability to generate the stem of Urdu words as well as loan words (words belonging to other languages i.e., Arabic, Persian, Turkish). In the experimental evaluation, we have observed a significant improvement in the overall stemming accuracy of our proposed pattern-based Urud stemmer, which demonstrates the adoptability of the proposed stemming approach for a variety of text-processing applications.

An electroencephalogram (EEG) based non-invasive 2-channel System on Chip (SoC) is presented to detect and report the seizure event of the epileptic patient. The SoC incorporates an area and power-efficient dual-channel analog front-end (AFE) and machine learning-based differential difference approximate computing seizure detection ($\text{D}^{2}$ACSD) processor. The $\text{D}^{2}$ACSD processor integrates approximate computing feature extraction and fixed-point linear support vector machine (LSVM) classifier to minimize the area-and-power utilization. The AFE comprises of two duty-cycled resistive MOSFET (DCRM) capacitively coupled instrumentation amplifier ($\text{C}^{2}$IA), a programmable gain amplifier, and multiplexed SAR-ADC. The DCRM-C2IA utilizes proposed DCRM technique to boost the equivalent resistance of the integrator of the DC servo loop. The 5m$\text{m}^{2}$SoC is implemented in 0.18$\mu$m, CMOS process while achieving an average accuracy of 89.19%, sensitivity 92.18% and specificity 89.13% for the random and block-wise splitting of data in train/test sets. The implemented DCRM-C2IA achieves an integrated noise of 0.80$\mu$Vrms over 0.5-100Hz frequency band. The realized system consumes $2.7\mu \text{J}/$classification to continuously detect seizure onset for timely suppression.

Abstract Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions. The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning‐based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K‐Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni‐gram feature set provides best classification results and attains 85.80% accuracy. For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni‐gram feature set proved to be the most suitable.

In this article implementation of carrier frequency offset estimate for 20MHz LTE baseband processing is discussed. LTE (Long Term Evolution) is a wireless communication standard that makes use of some innovative techniques to gain very high data rates (>100Mbps). This goal for such a high throughput also imposes design challenges for the industry and academia such as in the case of handheld mobile devices where the power budget is very limited. Implicitly high throughput means we need more computation power and more energy. On the other hand industry is also struggling for a flexible hardware solution, or software defined a radio (SDR), to amortize the huge cost of required hardware changes as the wireless standards have kept evolving. Design innovations are now needed to confront those challenges of low power and flexible design without changing the hardware. The implementation is made on Transport Triggered Architecture (TTA), which is a unique concept in computer architecture design, based on the single instruction, “MOVE”. The power consumption of the architecture when synthesized on 180nm technology at 180MHz and 1.8V is 18.39mW. The total area occupied excluding memory is 0.6mm 2 . The proposed TTA solution has been compared with, a more ASIC (application specific integrated circuits), like ASIP (application specific instruction processor) solution and a coprocessor accelerator-based solution. The proposed solution is more flexible: easily programmable due to high level language support, easily scalable, and still efficient in energy consumption needed to complete the CFO (carrier frequency offset) estimation task. Because of these attractive characteristics, TTA is also a potential candidate for SDR platforms.