Approximate Constant-Coefficient Multiplication Using Hybrid Binary-Unary Computing for FPGAs

Multipliers are used in virtually all Digital Signal Processing (DSP) applications such as image and video processing. Multiplier efficiency has a direct impact on the overall performance of such applications, especially when real-time processing is needed, as in 4K video processing, or where hardware resources are limited, as in mobile and IoT devices. We propose a novel, low-cost, low energy, and high-speed approximate constant coefficient multiplier (CCM) using a hybrid binary-unary encoding method. The proposed method implements a CCM using simple routing networks with no logic gates in the unary domain, which results in more efficient multipliers compared to Xilinx LogiCORE IP CCMs and table-based KCM CCMs (Flopoco) on average. We evaluate the proposed multipliers on 2-D discrete cosine transform algorithm as a common DSP module. Post-routing FPGA results show that the proposed multipliers can improve the {area, area × delay, power consumption, and energy-delay product} of a 2-D discrete cosine transform on average by {30%, 33%, 30%, 31%}. Moreover, the throughput of the proposed 2-D discrete cosine transform is on average 5% more than that of the binary architecture implemented using table-based KCM CCMs. We will show that our method has fewer routability issues compared to binary implementations when implementing a DCT core.

THE USE OF DATA MINING IN DETECTING CREDIT CARD FRAUD

Abstract— Credit card fraud is a serious problem for e-commerce retailers with UK merchants reporting losses of $574.2M in 2020. As a result, effective fraud detection systems must be in place to ensure that payments are processed securely in an online environment. From the literature, the detection of credit card fraud is challenging due to dataset imbalance (genuine versus fraudulent transactions), real-time processing requirements, and the dynamic behavior of fraudsters and customers. It is proposed in this paper that the use of machine learning could be an effective solution for combating credit card fraud.According to research, machine learning techniques can play a role in overcoming the identified challenges while ensuring a high detection rate of fraudulent transactions, both directly and indirectly. Even though both supervised and unsupervised machine learning algorithms have been suggested, the flaws in both methods point to the necessity for hybrid approaches.

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

A Novel Real-Time Echo Separation Processing Architecture for Space–Time Waveform-Encoding SAR Based on Elevation Digital Beamforming

Space–time waveform-encoding (STWE) SAR can receive echoes from multiple sub-swaths simultaneously with a single receive window. The echoes overlap each other in the time domain. To separate the echoes from different directions, traditional schemes adapt single-null steering techniques for digital receive beam patterns. However, the problems of spaceborne DBF-SAR, in practice, such as null extension loss, terrain undulation, elevation angle of arrival extension, and spaceborne antenna beam control, make the conventional scheme unable to effectively separate the echoes from different sub-swaths, which overlap each other in the time domain.A novel multi-null constrained echo separation scheme is proposed to overcome the shortcomings of the conventional scheme. The proposed algorithm can flexibly adjust the width of the notch to track the time-varying pulse extension angle with less resource consumption. Moreover, the hardware implementation details of the corresponding real-time processing architecture are discussed. The two-dimensional simulation results indicate that the proposed scheme can effectively improve the performance of echo separation. The effectiveness of the proposed method is verified by raw data processing instance of an X-band 16-channel DBF-SAR airborne system.

A GPU based single-pulse search pipeline (GSP) with database and its application to the Commensal Radio Astronomy FAST Survey (CRAFTS)

We developed a GPU based single-pulse search pipeline (GSP) with a candidate-archiving database. Largely based upon the infrastructure of the open source PulsaR Exploration and Search Toolkit (PRESTO), GSP implements GPU acceleration of the de-dispersion and integrates a candidate-archiving database. We applied GSP to the data streams from the Commensal Radio Astronomy FAST Survey (CRAFTS), which resulted in quasi-real-time processing. The integrated candidate database facilitates synergistic usage of multiple machine-learning tools and thus improves efficient identification of radio pulsars such as rotating radio transients (RRATs) and fast radio bursts (FRBs). We first tested GSP on pilot CRAFTS observations with the FAST Ultra-Wide Band (UWB) receiver. GSP detected all pulsars known from the the Parkes multibeam pulsar survey in the corresponding sky area covered by the FAST-UWB. GSP also discovered 13 new pulsars. We measured the computational efficiency of GSP to be ∼120 times faster than the original PRESTO and ∼60 times faster than an MPI-parallelized version of PRESTO.

Consistent Hashing and Real-Time Task Scheduling in Fog Computing

Distributed caching is one such system used by dynamic high-traffic websites to process the incoming user requests to perform the required tasks in an efficient way. Distributed caching is currently employing hashing algorithm in order to serve its purpose. A significant drawback of hashing in this circumstance is the addition of new servers that would result in a change in the previous hashing method (rehashing), hence, goes into a rigmarole. Thus, we need an effective algorithm to address the problem. This technique has served as a solution for distributed and rehashing problems. Most of upcoming internet of things will have to be latency aware and will not afford the data transmission and computation time in the cloud servers. The real-time processing in proximal distance device would be much needed. Hence, the authors aim to employ a real-time task scheduling algorithm. Computations referring to the user requests that are to be handled by the servers can be efficiently handled by consistent hashing algorithms.

Camera-based OCR scene text detection issues: A review

Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images (signboards), documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the traditional scanner whose usability is limited as they are not portable in size and cannot be used on images captured by cameras. However, characters captured by traditional scanners pose fewer computational difficulties as compared to camera captured images that are associated with divers’ challenges with consequences of high computational complexity and recognition difficulties. This paper, therefore, reviews the various factors that increase the computational difficulties of Camera-Based OCR, and made some recommendations as per the best practices for Camera-Based OCR systems.

Online Discrete Anchor Graph Hashing for Mobile Person Re-Identification

With the advance of mobile technologies, mobile devices such as unmanned aerial vehicle (UAV) become more important in video surveillance. By applying mobile person re-identification (re-id), mobile devices can monitor pedestrians in the transportation system from complex environments. Since the computing and storage resources of mobile devices are limited, traditional person re-id methods are not appropriate for mobile condition. Besides, mobile person re-id task also requires real-time processing. In this paper, we propose a novel hashing method: online discrete anchor graph hashing (ODAGH) for mobile person re-id. ODAGH integrates the advantages of online learning and hashing technology. In ODAGH, we propose an online discrete optimization algorithm to improve the efficiency of anchor graph learning in the online scenario. Experimental results demonstrate the superiority of ODAGH in terms of both effect and efficiency.

MFNet algorithm based on indoor scene segmentation

With the advancement of computer performance, deep learning is playing a vital role on hardware platforms. Indoor scene segmentation is a challenging deep learning task because indoor objects tend to obscure each other, and the dense layout increases the difficulty of segmentation. Still, current networks pursue accuracy improvement, sacrifice speed, and augment memory resource usage. To solve this problem, achieve a compromise between accuracy, speed, and model size. This paper proposes Multichannel Fusion Network (MFNet) for indoor scene segmentation, which mainly consists of Dense Residual Module(DRM) and Multi-scale Feature Extraction Module(MFEM). MFEM uses depthwise separable convolution to cut the number of parameters, matches different sizes of convolution kernels and dilation rates to achieve optimal receptive field; DRM fuses feature maps at several levels of resolution to optimize segmentation details. Experimental results on the NYU V2 dataset show that the proposed method achieves very competitive results compared with other advanced algorithms, with a segmentation speed of 38.47 fps, nearly twice that of Deeplab v3+, but only 1/5 of the number of parameters of Deeplab v3 + . Its segmentation results were close to those of advanced segmentation networks, making it beneficial for the real-time processing of images.