Comparison of Different Resource Allocation Strategy for Device-to-Device Communication
Abstract: The introduction of device-to-device communication (D2D) in future cellular networks will be subject to a considerable degradation of the performance of existing traditional applications called human-to-human (H2H). In this paper, we consider simultaneous access to radio resources in coexistence scenario through device-to-device (D2D) technology. First, we formulate the problem of resource sharing, then, we propose a genetically optimized solution for optimal resource sharing in uplink cellular communications. The performance is evaluated using the throughput analysis.

Authors: Dr. Dilip Sharma, Shiwani Kumawat

File Name: Shiwani_61000-18-107.pdf
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Facial Expression Recognition using LBP, DWT and SVM Classifier
Abstract: The face though seems an easy object to be recognized by retina but the artificial intelligence is not yet intelligent enough to do the task easily. As the source of a face is generally an image capturing object, there are a lot of variations and complexions that persists with the image like (for example noise, rotation etc.). There are many techniques that use some algorithm to find similarity in the face model and the test image and most of them are successful on their part to attain better test similarities. However, considering the diverse scale of applications and mode of image sourcing, a single algorithm cannot get maximum efficiency everywhere. Even after using the best algorithm for a particular task, an application has to counter with challenges of face recognition. This paper analyzes the hybrid approach of Local Binary Pattern (LBP) and DWT (Discrete Wavelet Transform) features for facial expression recognition. A subspace is created by this algorithm for training of feature vectors and Support Vector Machine (SVM) Classifier calculates the similarity score for performance evaluation which will provide improved results in terms of recognition accuracy.

Authors: Kavita Suthar, Komal Paliwal

File Name: Kavita_61100-18-101.pdf
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Genetically Optimized Adaptive Threshold Based Energy Detection Spectrum Sensing Algorithm for Cognitive Radio Networks
Abstract: Cognitive radio is an important technique of dynamic spectrum allocation. By using the cognitive radio (CR) spectrum utilization can be improved. Spectrum sensing plays an influential role in detecting white spaces present in the spectrum. So, spectrum sensing algorithm will help the secondary user (SU) to detect spectrum holes precisely. Here energy detection (ED) spectrum sensing technique is used. Energy detection spectrum sensing with single threshold has been widely researched in the past. The interference between primary user (PU) and secondary user (SU) was more in energy detection with single threshold therefore the collision rate was much high. So, to minimize collision rate and improve the probability of detection (Pd) this paper proposes a genetically optimized double threshold based energy detection spectrum sensing algorithm to optimize the performance of cognitive radio networks. Simulations based on probability of detection (Pd), probability of miss detection (Pm), and collision rate has been presented in this paper.

Authors: Neetu Sharma, S. V. Charhate, Preeti Trivedi

File Name: Neetu_61000-18-106.pdf
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Spectrum Sensing in Cognitive Radio for Maximum Probability of Detection using PSO
Abstract:

Authors: Prateeksha Saxena, Professor Praveen Patidar

File Name: Prateeksha_61100-18-102.pdf
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