Abstract-This paper presents a 512-point feedforward FFT architecture for wireless personal area network (WPAN). The architecture processes a continuous flow of 8 samples in parallel, leading to a throughput of 2.64 GSamples/s. The FFT is computed in three stages that use radix-8 butterflies. This radix reduces significantly the number of rotators with respect to previous approaches based on radix-2. Besides, the proposed architecture uses the minimum memory that is required for a 512-point 8-parallel FFT.Experimental results show that besides its high throughput, the design is efficient in area and power consumption, improving the results of previous approaches. Specifically, for a wordlength of 16 bits, the proposed design consumes 61.5 mW and its area is 1.43 mm 2 .

Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.

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Road crack detection and road damage assessment are necessary to support driving safety in a route network. Several unexpected incidents (e.g. road accidents) take place all over the world due to unhealthy road infrastructure. This paper proposes a deep learning approach for road crack detection and road damage assessment which will contribute to the transport sector of a country like Bangladesh where a plethora of roads undergo the crack problem. The proposed model consists of two phases. In the first phase, the model is trained using transfer learning (VGG16) to detect the existence of crack on the road surface. In the second phase, an integrated framework, combining CNN(VGG16) and RNN(LSTM), is trained to classify the crack in one of the two categories-severe and slight. After experiments, the validation accuracies obtained by the proposed models (VGG16 and VGG16-LSTM) are respectively 99.67% and 97.66%.

Abstract-Finding the dense locations in large indoor spaces is very useful for getting overloaded locations, security, crowd management, indoor navigation, and guidance. Indoor tracking data can be very large and are not readily available for finding dense locations. This paper presents a graph-based model for semi-constrained indoor movement, and then uses this to map raw tracking records into mapping records representing object entry and exit times in particular locations. Then, an efficient indexing structure, the Dense Location Time Index (DLT -Index) is proposed for indexing the time intervals of the mapping table, along with associated construction, query processing, and pruning techniques. The DLT -Index supports very efficient aggregate point queries, interval queries, and dense location queries. A comprehensive experimental study with real data shows that the proposed techniques can efficiently find dense locations in large amounts of indoor tracking data.