This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted- and autonomous driving applications. Due to strict constraints in terms of the latency, dependability, and security of autonomous driving, machine perception (i.e., detection and decision tasks) based on DNNs cannot be implemented by relying on remote cloud access. These tasks must be performed in real time in embedded systems on board the vehicle, particularly for the inference phase (considering the use of DNNs pretrained during an offline step). When developing a DNN computing platform, the choice of the computing arithmetic matters. Moreover, functional safe applications, such as autonomous driving, impose severe constraints on the effect that signal processing accuracy has on the final rate of wrong detection/decisions. Hence, after reviewing the different choices and tradeoffs concerning arithmetic, both in academia and industry, we highlight the issues in implementing DNN accelerators to achieve accurate and lowcomplexity processing of automotive sensor signals (the latter coming from diverse sources, such as cameras, radar, lidar, and ultrasonics). The focus is on both general-purpose operations massively used in DNNs, such as multiplying, accumulating, and comparing, and on specific functions, including, for example, sigmoid or hyperbolic tangents used for neuron activation.

/pdf/novel-arithmetics-in-deep-neural-networks-signal-processing-53ydtnxnqs.pdf

Novel Arithmetics in Deep Neural Networks Signal Processing for Autonomous Driving: Challenges and Opportunities

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.

https://www.mdpi.com/2078-2489/11/6/312/pdf

Malicious Text Identification: Deep Learning from Public Comments and Emails

In this research we have proposed the implementation of a Lio-Sam algorithm in the navigation system of an autonomous terrestrial robot. This algorithm is based on the following sensors: Ouster OS1-64 Light Detection and Ranging device (LIDAR) and an XSens MTI 1 Inertial Measurement Unit (IMU). We have opted for this algorithm due to the fact that for the swarm and emergency intervention robots it constitutes a very good solution, especially because it can help us generate a 3D point cloud map of the surroundings. The significant advantage of this solution is that it allows a fast identification of the location of the robot.

LIDAR based distance estimation for emergency use terrestrial autonomous robot

This paper’s core objective is to develop and validate a new neurocomputing model to classify document images in particularly demanding hard conditions such as image distortions, image size variance and scale, a huge number of classes, etc. Document classification is a special machine vision task in which document images are categorized according to their likelihood. Document classification is by itself an important topic for the digital office and it has several usages. Additionally, different methods for solving this problem have been presented in various studies; their respectively reached performance is however not yet good enough. This task is very tough and challenging. Thus, a novel, more accurate and precise model is needed. Although the related works do reach acceptable accuracy values for less hard conditions, they generally fully fail in the face of those above-mentioned hard, real-world conditions, including, amongst others, distortions such as noise, blur, low contrast, and shadows. In this paper, a novel deep CNN model is developed, validated and benchmarked with a selection of the most relevant recent document classification models. Additionally, the model’s sensitivity was significantly improved by injecting different artifacts during the training process. In the benchmarking, it does clearly outperform all others by at least 4%, thus reaching more than 96% accuracy.

/pdf/a-deep-learning-based-visual-sensing-concept-for-a-robust-3sspywbv0j.pdf

A Deep-Learning Based Visual Sensing Concept for a Robust Classification of Document Images under Real-World Hard Conditions.

Gear Grinding Monitoring based on Deep Convolutional Neural Networks

The effective distinction between normal email and spam, so as to maximize the possible of filtering spam has become a research hotspot currently. Naive bayes algorithm is a kind of frequently-used email classification and it is a statistical-based classification algorithm. It assumes that the attributes are independent of each other when given the target value. This hypothesis is apparently impossible in the email classification, so the accuracy of email classification based on naive bayes algorithm is low. In allusion to the problem of poor accuracy of email classification based on naive bayes algorithm, scholars have proposed some new email classification algorithms. The email classification algorithm based on deep neural network is one kind of them. The deep neural network is an artificial neural network with full connection between layer and layer. The algorithm extracted the email feature from the training email samples and constructed a DNN with multiple hidden layers, the DNN classifier was generated by training samples, and finally the testing emails were classified, and they were marked whether they were spam or not. In order to verify the effect of the email classification algorithm based on DNN, in this paper we constructed a DNN with 2 hidden layers. The number of nodes in each hidden layer was 30. When the training set was trained, we set up 2000 batches, and each batch has 3 trained data. We used the famous Spam Base dataset as the data set. The experiment result showed that DNN was higher than naive Bayes in the accuracy of email classification when the proportion of the training set was 10%, 20%, 30%, 40% and 50% respectively, and DNN showed a good classification effect. With the development of science and technology, spam manifests in many forms and the damage of it is more serious, this puts forward higher requirements for the accuracy of spam recognition. The focus of next research will be combining various algorithms to further improve the effect of email classification. Keywords-Deep Neural Networks; Spam Email; Classification; Naive Bayes; SpamBase Data Set

/pdf/research-of-email-classification-based-on-deep-neural-1027a2n30k.pdf

Research of Email Classification based on Deep Neural Network

This paper presents a new method of recognition of lidar cloud point images based on convolutional neural network. This experiment uses 3D CAD ModelNet, and generates 3D point cloud data by simulating the scanning process of lidar. The data is divided into cells, and the distance is represented by gray values. Finally, the data is stored as grayscale images. Changing the number of cells dividing point cloud results in different experimental results. Experiments show that the proposed method has higher accuracy when dividing the cloud with 27&#xd7;35 cells. Comparison of point cloud cell image method with VoxNet method, experimental results show that the classification method based on gray image and convolutional neural network has more advantages than the most advanced point cloud recognition network Voxnet.

Lidar Image Classification Based on Convolutional Neural Networks

Vision-based SLAM has an outstanding problem is not work when the camera fast motion, or camera operating environment characterized by scarce. Aiming at this problem, this paper proposes a SLAM method of IMU and vision fusion. This article uses a stereo camera to extract the image ORB feature points. During the camera movement, if the number of extracted feature points is less than a certain threshold and the camera movement cannot be estimated or the estimated camera rotation and translation is greater than a certain threshold, the camera pose is estimated by fusing IMU , Otherwise use feature points to estimate camera pose. This paper uses non-linear optimization methods to perform pose estimation of pure feature points and pose estimation of fused IMU, respectively. The experimental results show that binocular vision SLAM with IMU information can estimate the camera pose more accurately. Keyword-Robot; IMU; Stereo Vision; SLAM

/pdf/improved-stereo-vision-robot-locating-and-mapping-method-47e7qlx8q4.pdf

Improved Stereo Vision Robot Locating and Mapping Method

Target recognition has always been a hot research topic in computer image and pattern recognition. This paper proposes a target recognition method based on decision layer fusion. ModelNet[1]—The 3D CAD model library,which is used to be identified. Features are extracted from the model's point cloud data and multi-view images. The image is identified using the AlexNet[2] network, the point cloud is identified by the VoxNet[3] network. The fusion algorithm is used in the decision layer to complete the fusion of features. The results show that the proposed method improves the accuracy of object recognition. Keywords-Target Recognition; Convolutional Neural Network; Decision Fusion

3D Target Recognition Based on Decision Layer Fusion

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Research of Email Classification based on Deep Neural Network

Lidar Image Classification Based on Convolutional Neural Networks

Improved Stereo Vision Robot Locating and Mapping Method

3D Target Recognition Based on Decision Layer Fusion