An Optimized and Fast Scheme for Real-time Human Detection using Raspberry Pi

This paper has been presented at : The International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016)Real-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number of scales have been considered for computing Histogram of Oriented Gradients (HOG) features for the selected ROI. Support vector machine (SVM) is employed for classification of HOG feature vectors into detected and non-detected human regions. Detected human regions are further filtered by analyzing the area of overlapping regions. Considering the limited capabilities of Raspberry Pi, the proposed scheme is evaluated using six different testing schemes on Town Centre and CAVIAR datasets. Out of these six testing schemes, Single Window with two Scales (SW2S) processes 3 frames per second with acceptable less accuracy than the original HOG. The proposed algorithm is about 8 times faster than the original multi-scale HOG and recommended to be used for real-time human detection on a Raspberry Pi

Recurrent Cervical Neurofibrosarcoma: A Rare Case of Malignant Peripheral Nerve Sheath Tumor of Head and Neck Region

Neurofibrosarcoma is a malignant peripheral nerve sheath tumor (MPNST). The cervical location of the neurofibrosarcoma is very rare and is less than 1% in the literature. MPNSTs are often associated with neurofibromatosis type 1 (NF1).  We are presenting a case report of 31 years old female, with huge recurrent cervical neurofibrosarcoma on the right side of the neck.  To date, surgical excision followed by chemotherapy and radiotherapy is the treatment of choice which requires a multidisciplinary approach. We excised the above-mentioned cervical neurofibrosarcoma in a piecemeal fashion and discharged the patient on follow-up with the oncology department

Remote Sensing Change Detection With Transformers Trained from Scratch

Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark. This current strategy is driven by the fact that transformers typically require a large amount of training data to learn inductive biases, which is insufficient in standard CD datasets due to their small size. We develop an end-to-end CD approach with transformers that is trained from scratch and yet achieves state-of-the-art performance on four public benchmarks. Instead of using conventional self-attention that struggles to capture inductive biases when trained from scratch, our architecture utilizes a shuffled sparse-attention operation that focuses on selected sparse informative regions to capture the inherent characteristics of the CD data. Moreover, we introduce a change-enhanced feature fusion (CEFF) module to fuse the features from input image pairs by performing a per-channel re-weighting. Our CEFF module aids in enhancing the relevant semantic changes while suppressing the noisy ones. Extensive experiments on four CD datasets reveal the merits of the proposed contributions, achieving gains as high as 14.27\% in intersection-over-union (IoU) score, compared to the best-published results in the literature. Code is available at \url{https://github.com/mustansarfiaz/ScratchFormer}.Comment: 5 figures and 4 table

Association between perioperative hypothermia and surgical site infection after elective abdominal surgery: A prospective cohort study

Introduction: Surgical site infections (SSIs) account for 14-16% of nosocomial infections and are one of the major causes of increased morbidity, hospital stay, cost of care, and even mortality. Hypothermia as a risk factor for SSI is debated but there is lack of conclusive evidence. The present study explores the association of hypothermia with SSI.Methodology: This is a prospective cohort study conducted on adult patients who underwent elective laparotomy. Patients were divided into two cohorts, the Hypothermia Cohort and the Normothermia Cohort, based upon episodes of hypothermia of \u3c360C in the perioperative period. SSI was diagnosed based upon criteria defined by the Center for Disease Control and Prevention (CDC). Postoperative follow-up to detect SSI was done until 30 days after the operation.Results: A total of 183 patients met the selection criteria and were included in the study. Ninety patients (49%) had perioperative hypothermia and were followed in the Hypothermia Cohort, while 93 patients (51%) who remained normothermic in the perioperative period were followed in the Normothermia Cohort. Mean age of the patients was 49.77 +/- 14.82 years. Almost two-thirds of the participants were females (63.9%). Patients who developed hypothermia were significantly older and had lower BMI. Also the proportion of female patients was significantly higher in the Normothermic Cohort.Rate of SSI was similar in both groups (10% versus 10.8%) with p-value of 0.867. Multivariable regression analysis also failed to show any significant association between hypothermia and SSI.Conclusion: Our study failed to show any statistically significant association between hypothermia and surgical site infection

Foliar applications of bio-fabricated selenium nanoparticles to improve the growth of wheat plants under drought stress

The present study was aimed to biosynthesize selenium nanoparticles (SeNPs) and assess their foliar applications to improve the growth of wheat plants under controlled irrigation and drought stress. Bud aqueous extract of Allium sativum L. was used as a reducing and stabilizing agent of SeNPs followed by their optical and morphological characterization by using ultraviolet-visible spectroscopy, scanning electron microscopy, Fourier-transform infrared, and energy dispersive X-ray analysis. Various concentrations of SeNPs (10, 20, 30, and 40 mg/L) were applied exogenously to drought-tolerant (V1) and drought-susceptible (V2) wheat varieties at the trifoliate stage. Under the positive control conditions, plants were irrigated with 450 mL of water/pot (100% field capacity); and under water-deficit environment, plants were irrigated with 160 mL of water/pot (35% field capacity). Remarkable increase in plant height, shoot length, shoot fresh weight, shoot dry weight, root length, root fresh weight, root dry weight, leaf area, leaf number, and leaf length has been observed when 30 mg/L concentration of SeNPs was used. However, the plant morphological parameters decreased gradually at higher concentrations (40 mg/L) in both selected wheat varieties. Therefore, 30 mg/L concentration of SeNPs was found most preferable to enhance the growth of selected wheat varieties under normal and water-deficient conditions