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Herein, we report a minireview to give a brief introduction of applications of nanomaterials in the field of forensic science. The materials that have their size in nanoscale (1 - 100 nm) comes under the category of nanomaterials. Nanomaterials possess various applications in different fields like cosmetic production, medical, photoconductivity etc. because of their physio-chemical, electrical and magnetic properties. Due to the different characteristic property that nanomaterials have, they are widely employed in diverse domains. In various fields of forensic science such as fingerprints, toxicology, medicine, serology, nanomaterials are being used extensively. Large surface area to volume ratio of the materials in nano-regime makes the nanomaterials suitable for all these application with high efficiency. This review article briefs about the nanomaterials, their advantages and their novel applications in various fields, focusing especially in the field of forensic science. The basic idea of different areas of forensic science such as development of fingerprints, detection of drugs, estimating the time since death, analysis of GSR, detection of various explosives and for the extraction of DNA etc. has also been provided., Comment: 16 Pages, 3 Figures, 42 References

Brain Tumors are abnormal mass of clustered cells penetrating regions of brain. Their timely identification and classification help doctors to provide appropriate treatment. However, Classifi-cation of Brain Tumors is quite intricate because of high-intra class similarity and low-inter class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. This all leads to hampering generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables deep metric learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embeddings to enhance differentiability between the classes. With utilization of SphereFace loss based deep metric learning it is ensured that samples from class clustered together while the different ones are pushed apart. Results reflects the promi-nence in the approach, the proposed framework achieved state-of-the-art 98.69% validation accu-racy using k-NN (k=1) and this is significantly higher than normal SoftMax Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class separability and intra-class closeness. Experimental analysis done over various classifiers and loss function set-tings suggests potential in the approach.

Most of the quadrupeds developed are highly actuated, and their control is hence quite cumbersome. They need advanced electronics equipment to solve convoluted inverse kinematic equations continuously. In addition, they demand special and costly sensors to autonomously navigate through the environment as traditional distance sensors usually fail because of the continuous perturbation due to the motion of the robot. Another challenge is maintaining the continuous dynamic stability of the robot while walking, which requires complicated and state-of-the-art control algorithms. This paper presents a thorough description of the hardware design and control architecture of our in-house prismatic joint quadruped robot called the PRISMA. We aim to forge a robust and kinematically stable quadruped robot that can use elementary control algorithms and utilize conventional sensors to navigate an unknown environment. We discuss the benefits and limitations of the robot in terms of its motion, different foot trajectories, manufacturability, and controls., Comment: 14 pages, 16 figures, 4 tables