This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments.

Gait partitioning methods: a systematic review

http://romisatriawahono.net/lecture/rm/survey/computer%20vision/Subhedar%20-%20Image%20Steganography%20-%202014.pdf

Current status and key issues in image steganography

Fog computing is a recently proposed computing paradigm that extends Cloud computing and services to the edge of the network. The new features offered by fog computing (e.g., distributed analytics and edge intelligence), if successfully applied for pervasive health monitoring applications, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. While promising, how to design and develop real-word fog computing-based pervasive health monitoring system is still an open question. As a first step to answer this question, in this paper, we employ pervasive fall detection for stroke mitigation as a case in study. There are four major contributions in this paper: (1) to investigate and develop a set of new fall detection algorithms, including new fall detection algorithms based on acceleration magnitude values and non-linear time series analysis techniques, as well as new filtering techniques to facilitate fall detection process; (2) to design and employ a real-time fall detection system employing fog computing paradigm, which distribute the analytics throughout the network by splitting the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud); (3) we carefully exam the special needs and constraints of stroke patients and propose patient-centered design that is minimal intrusive to patients. This type of patient-centered design is currently lacking in most of the existing work; and (4) our experiments with real-word data show that our proposed system achieves the high sensitivity (low missing rate) while it also achieves the high specificity (low false alarm rate). At the same time, the response time and energy consumption of our system are close to the minimum of the existing approaches.

FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation

A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2014.0193

Brain tumour classification using two-tier classifier with adaptive segmentation technique

Computer aided detection of ischemic stroke using segmentation and texture features

Magnetic resonance imaging (MRI) is often the medical imaging method of choice when soft tissue delineation is necessary. This paper presents a new approach for automated diagnosis based on classification of the magnetic resonance images (MRI). The proposed method consists of two stages namely feature extraction and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). Wavelet transform based methods are a well known tool for extracting frequency space information from non-stationary signals. The features extracted using DWT of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier is based on feed forward back propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. A classification with a success of 90% and 99% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.

Classification of MRI brain images using k-nearest neighbor and artificial neural network

Nowadays, Digital video is one of the popular multimedia data exchanged in the internet. Commercial activity on the internet and media require protection to enhance security. The 2D Barcode with a digital watermark is a widely interesting research in the security field. In this paper propose a video watermarking with text data (verification message) by using the Quick Response (QR) Code technique. The QR Code is prepared to be watermarked via a robust video watermarking scheme based on the (singular value decomposition)SVD and (Discrete Wavelet Transform)DWT. In addition to that logo (or) watermark gives the authorized ownership of video document. SVD is an attractive algebraic transform for watermarking applications. SVD is applied to the cover I-frame. The extracted diagonal value is fused with logo (or) watermark. DWT is applied on SVD cover image and QR code image. The inverse transform on watermarked image and add the frame into video this watermarked (include logo and QR code image) the video file sends to authorized customers. In the reverse process check the logo and QR code for authorized ownership. These experimental results can achieved acceptable imperceptibility and certain robustness in video processing.

A robust QR-Code video watermarking scheme based on SVD and DWT composite domain

Steganography is the discipline of exchanging top secret information by embedding it into a multimedia carrier. The ultimate aim, here is to hide the very existence of the embedded information within seemingly innocuous carriers. The proposed method extracts either Discrete Wavelet Transform (DWT) or Integer Wavelet Transform (IWT) coefficients of both cover image and secret image. After that two extracted coefficient values are embedded by fusion processing technique. Then the stego image is obtained by applying various combinations of DWT and IWT on both images. In this method, we concentrated for perfecting the visual effect of the stego image and robustness against the various attacks by using different wavelet families. Finally performance evaluation is done on dual transform steganography using wavelet families and statistical methods. In our method achieved acceptable imperceptibility and certain robustness.

Dual transform based steganography using wavelet families and statistical methods

Information fusion offers a promising solution to the development of a high performance classification system. In this paper multiple gait components such as spatial, temporal and wavelet are fused for enhancing the classification rate. Initially background modeling is done from a video sequence and the foreground moving objects in the individual frames are segmented using the background subtraction algorithm. Then gait representing features are extracted for training and testing the multi_class k Nearest Neighbor models (kNN) and multi_class support vector machine models (SVM). We have successfully achieved our objective with only two gait cycles and our experimental results demonstrate that the classification ability of SVM is better than kNN. The proposed system is evaluated using side view videos of NLPR database.

R. Bhavani

Papers

Computer aided detection of ischemic stroke using segmentation and texture features

Classification of MRI brain images using k-nearest neighbor and artificial neural network

A robust QR-Code video watermarking scheme based on SVD and DWT composite domain

Dual transform based steganography using wavelet families and statistical methods

Performance Comparison of SVM and kNN in Automatic Classification of Human Gait Patterns