In this paper, a computer-based method for defining tumor region in the brain using MRI images is presented. A classification of brain into healthy brain or a brain having a tumor is first done which is then followed by further classification into begnin or malignant tumor. The algorithm incorporates steps for preprocessing, image segmentation, feature extraction and image classification using neural network techniques. Finally the tumor area is specified by region of interest technique as confirmation step. A user friendly Matlab GUI program has been constructed to test the proposed algorithm.

A major drawback of orthogonal frequency-division multiplexing (OFDM) signals is their high peak-to-average power ratio (PAPR), which causes serious degradation in performance when a nonlinear power amplifier (PA) is used. Companding transform (CT) is a well-known method to reduce PAPR without restrictions on system parameters such as number of subcarriers, frame format and constellation type. Recently, a linear nonsymmetrical companding transform (LNST) that has better performance than logarithmic-based transforms such as mu-law companding was proposed. In this paper, a new linear companding transform (LCT) with more design flexibility than LNST is proposed. Computer simulations show that the proposed transform has a better PAPR reduction and bit error rate (BER) performance than LNST with better power spectral density (PSD).

In the era of advanced computer vision and natural language processing, the use of social media as a source of information has become even more valuable in directing aid and rescuing victims. Consequently, millions of texts and images can be processed in real-time, allowing emergency responders to efficiently assess evolving crises and appropriately allocate resources. The majority of the previous detection studies are text-only or image-only based, overlooking the potential benefits of integrating both modalities. In this paper, we propose Multimodal Channel Attention (MCA) block, which employs an adaptive attention mechanism, learning to assign varying importance to each modality. We then propose a novel Deep Multimodal Crisis Categorization (DMCC) framework, which employs a two-level fusion strategy for better integration of textual and visual information. The DMCC framework consists of feature-level fusion, which is accomplished through the MCA block, and score-level fusion, whereby the decisions made by the individual modalities are integrated with those of the MCA model. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed framework. Through a comprehensive evaluation, it was found that the proposed framework achieves a performance enhancement compared to unimodal methods. Furthermore, it outperforms the current state-of-the-art methods on crisis-related categorization tasks.

In this letter, a novel semi-blind selected mapping (SLM) technique is proposed for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) system. No explicit side information is sent; instead the selected signal index is embedded in transmitted data. The proposed SLM technique maintains the same PAPR as classical SLM technique, while increases the overall throughput. Simulations results of several OFDM systems employing the proposed semi-blind SLM technique using either QPSK or 16-QAM modulation show that it performs very well in in both the SI index detection error rate and bit error rate (BER).