A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution. However, distinguishing mud and ores in a real scene is challenging for their similarity in shape, color, and texture. Moreover, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time consuming. Aiming at the challenging problem, this paper proposed a novel weakly supervised method based on deep active learning (AL), named YOLO-AL. The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset. Then, an AL framework-embedded YOLO-v3 model is constructed. In the AL process, it iteratively fine-tunes the last few layers of the YOLO-v3 model with the most valuable samples, which is selected by a Less Confident (LC) strategy. Experimental results show that the proposed method can effectively detect mud in ores. More importantly, the proposed method can obviously reduce the labeled samples without decreasing the detection accuracy

An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection

A Resource Allocation Scheme for Multi-D2D Communications Underlying Cellular Networks with Multi-Subcarrier Reusing

Device-to-device (D2D) communication is proposed as a promising technique of future cellular networks which fulfills its potential in terms of high resource utilization. In this paper, in order to improve the achievable rate of D2D communication and the spectrum utilization, we consider the scenario that multiple D2D pairs can share uplink spectrum resources with multiple cellular users (CUs). We aim to maximize the overall system spectrum efficiency while satisfying the rate requirements of all CUs and guaranteeing that the system gain is positive. We formulate the joint optimization problem of subcarrier assignment and power allocation which falls naturally into a mixed integer non-linear programming form that is a difficult problem to solve. Hence, we propose a two-stage resource allocation scheme which comprises a subcarrier assignment by employing a heuristic greedy strategy, as well as a power allocation algorithm based on the Lagrangian dual method. Numerical results demonstrate the advantageous performance of our scheme in greatly increasing the system sum spectrum efficiency

A Robust and Device-Free System for the Recognition and Classification of Elderly Activities

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise