A parallel convolutional coder including embedded puncturing with application to consumer devices

As consumers demand more functionality) from their electronic devices and manufacturers supply the demand then electrical power and clock requirements tend to increase, however reassessing system architecture can fortunately lead to suitable counter reductions. To maintain low clock rates and therefore reduce electrical power, this paper presents a parallel convolutional coder for the transmit side in many wireless consumer devices. The coder accepts a parallel data input and directly computes punctured convolutional codes without the need for a separate puncturing operation while the coded bits are available at the output of the coder in a parallel fashion. Also as the computation is in parallel then the coder can be clocked at 7 times slower than the conventional shift-register based convolutional coder (using DVB 7/8 rate). The presented coder is directly relevant to the design of modern low-power consumer device

Efficient biometric and password based mutual authentication for consumer USB mass storage devices

A Universal Serial Bus (USB) Mass Storage Device (MSD), often termed a USB flash drive, is ubiquitously used to store important information in unencrypted binary format. This low cost consumer device is incredibly popular due to its size, large storage capacity and relatively high transfer speed. However, if the device is lost or stolen an unauthorized person can easily retrieve all the information. Therefore, it is advantageous in many applications to provide security protection so that only authorized users can access the stored information. In order to provide security protection for a USB MSD, this paper proposes a session key agreement protocol after secure user authentication. The main aim of this protocol is to establish session key negotiation through which all the information retrieved, stored and transferred to the USB MSD is encrypted. This paper not only contributes an efficient protocol, but also does not suffer from the forgery attack and the password guessing attack as compared to other protocols in the literature. This paper analyses the security of the proposed protocol through a formal analysis which proves that the information is stored confidentially and is protected offering strong resilience to relevant security attacks. The computational cost and communication cost of the proposed scheme is analyzed and compared to related work to show that the proposed scheme has an improved tradeoff for computational cost, communication cost and security

Enhanced three-factor security protocol for consumer USB mass storage devices

The Universal Serial Bus (USB) is an extremely popular interface standard for computer peripheral connections and is widely used in consumer Mass Storage Devices (MSDs). While current consumer USB MSDs provide relatively high transmission speed and are convenient to carry, the use of USB MSDs has been prohibited in many commercial and everyday environments primarily due to security concerns. Security protocols have been previously proposed and a recent approach for the USB MSDs is to utilize multi-factor authentication. This paper proposes significant enhancements to the three-factor control protocol that now makes it secure under many types of attacks including the password guessing attack, the denial-of-service attack, and the replay attack. The proposed solution is presented with a rigorous security analysis and practical computational cost analysis to demonstrate the usefulness of this new security protocol for consumer USB MSDs

A software agent enabled biometric security algorithm for secure file access in consumer storage devices

In order to resist unauthorized access, consumer storage devices are typically protected using a low entropy password. However, storage devices are not fully protected against an adversary because the adversary can utilize an off-line dictionary attack to find the correct password and/or run an existing algorithm for resetting the existing password. In addition, a password protected device may also be stolen or misplaced allowing an adversary to easily retrieve all the stored confidential information from a removable storage device. In order to protect the consumer’s confidential information that has been stored, this paper proposes a mutual authentication and key negotiation protocol that can be used to protect the confidential information in the device. The functionality of the protocol enables the storage device to be secure against relevant security attacks. A formal security analysis using Burrows-Abadi-Needham (BAN) logic is presented to verify the presented algorithm. In addition, a performance analysis of the proposed protocol reveals a significantly reduced communication overhead compared to the relevant literature

Developing residential wireless sensor networks for ECG healthcare monitoring

Wireless technology development has increased rapidly due to it’s convenience and cost effectiveness compared to wired applications, particularly considering the advantages offered by Wireless Sensor Network (WSN) based applications. Such applications exist in several domains including healthcare, medical, industrial and home automation. In the present study, a home-based wireless ECG monitoring system using Zigbee technology is considered. Such systems can be useful for monitoring people in their own home as well as for periodic monitoring by physicians for appropriate healthcare, allowing people to live in their home for longer. Health monitoring systems can continuously monitor many physiological signals and offer further analysis and interpretation. The characteristics and drawbacks of these systems may affect the wearer’s mobility during monitoring the vital signs. Real-time monitoring systems record, measure, and monitor the heart electrical activity while maintaining the consumer’s comfort. Zigbee devices can offer low-power, small size, and a low-cost suitable solution for monitoring the ECG signal in the home, but such systems are often designed in isolation, with no consideration of existing home control networks and smart home solutions. The present study offers a state of the art review and then introduces the main concepts and contents of the wireless ECG monitoring systems. In addition, models of the ECG signal and the power consumption formulas are highlighted. Challenges and future perspectives are also reported. The paper concludes that such mass-market health monitoring systems will only be prevalent when implemented together with home environmental monitoring and control systems

Case-based reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables

Key form features are relative to the style of a product and the expression style features depict the product description and are a measurement of attribute knowledge. The uncertainty definition leads to an improved and effective product style retrieval when combined with fuzzy sets. Firstly, a style knowledge and features database are constructed using fuzzy case based reasoning technology (FCBR). A similarity measurement method based on case-based reasoning and fuzzy model of the fuzzy proximity method may be defined by the Fuzzy Nearest-Neighbor (FNN) algorithm obtaining the style knowledge extraction. Secondly, the Linguistic Variables (LV) are used to assess the product characteristics to establish the product style evaluation database for simplifying the style presentation and decreasing the computational complexity. Thirdly, the model of product style feature set, extracted by FAHP and the final style related form features set, are acquired using LV. This research involves a case study for extracting the key form features of the style of high heel shoes. The proposed algorithms are generated by calculating the weights of each component of high heel shoes using FAHP with LV. The case study and results established that the proposed method is feasible and effective for extracting the style of the product

Diabetic plantar pressure analysis using image fusion

Plantar pressure images analysis is the key issue of designing comfortable shoe products through last customizing system, which has attracted the researchers’ curiosity toward image fusion as an application of medical and industrial imaging. In the current work, image fusion has been applied using wavelet transform and compared with Laplace Pyramid. Using image fusion rules of Mean-Max, we presented a plantar pressure image fusion method employing haar wavelet transform. It was compared in different composition layers with the Laplace pyramid transform. The experimental studies deployed the haar, db2, sym4, coif2, and bior5.5 wavelet basis functions for image fusion under decomposition layers of 3, 4, and 5. Evaluation metrics were measured in the case of the different layer number of wavelet decomposition to determine the best decomposition level and to evaluate the fused image quality using with different wavelet functions. The best wavelet basis function and decomposition layers were selected through the analysis and the evaluation measurements. This study established that haar wavelet transform with five decomposition levels on plantar pressure image achieved superior performance of 89.2817% mean, 89.4913% standard deviation, 5.4196 average gradient, 14.3364 spatial frequency, 5.9323 information entropy and 0.2206 cross entropy

A mathematical model for fibro-proliferative wound healing disorders

The normal process of dermal wound healing fails in some cases, due to fibro-proliferative disorders such as keloid and hypertrophic scars. These types of abnormal healing may be regarded as pathologically excessive responses to wounding in terms of fibroblastic cell profiles and their inflammatory growth-factor mediators. Biologically, these conditions are poorly understood and current medical treatments are thus unreliable. In this paper, the authors apply an existing deterministic mathematical model for fibroplasia and wound contraction in adult mammalian dermis (Olsenet al., J. theor. Biol. 177, 113–128, 1995) to investigate key clinical problems concerning these healing disorders. A caricature model is proposed which retains the fundamental cellular and chemical components of the full model, in order to analyse the spatiotemporal dynamics of the initiation, progression, cessation and regression of fibro-contractive diseases in relation to normal healing. This model accounts for fibroblastic cell migration, proliferation and death and growth-factor diffusion, production by cells and tissue removal/decay. Explicit results are obtained in terms of the model processes and parameters. The rate of cellular production of the chemical is shown to be critical to the development of a stable pathological state. Further, cessation and/or regression of the disease depend on appropriate spatiotemporally varying forms for this production rate, which can be understood in terms of the bistability of the normal dermal and pathological steady states—a central property of the model, which is evident from stability and bifurcation analyses. The work predicts novel, biologically realistic and testable pathogenic and control mechanisms, the understanding of which will lead toward more effective strategies for clinical therapy of fibro-proliferative disorders

Discrete wavelet transform based freezing of gait detection in Parkinson's disease

Wearable on body sensors have been employed in many applications including ambulatory monitoring and pervasive computing systems. In this work, a wearable assistant has been created for people suffering from Parkinson’s disease (PD), specifically with the Freezing of Gait (FoG) symptom. Wearable accelerometers were placed on the person’s body and used for movement measure. When FoG is detected, a rhythmic audio signal was given from the wearable assistant to motivate the wearer to continue walking. Long term monitoring results in collecting huge amounts of complex raw data; therefore, data analysis becomes impractical or infeasible resulting in the need for data reduction. In the present study, Discrete Wavelet Transform (DWT) has been used to extract the main features inherent in the key movement indicators for FoG detection. The discrimination capacities of these features were assessed using, i) Support Vector Machine (SVM) using a linear kernel function, and ii) Artificial Neural Network (ANN) with a two-layer feed-forward with hidden layer of 20 neurons that trained with conjugate gradient back- propagation. Using these two different machine learning techniques, we were capable of detecting FoG with an accuracy of 87.50% and 93.8%, respectively. Additionally, the comparison between the extracted features from DWT coefficients with those using Fast Fourier Transform (FFT) established accuracies of 93.8% and 81.3%, respectively. Finally, the discriminative features extracted from DWT yield to a robust multidimensional classification model compared to models in the literature based on a single feature. The work presented paves the way for reliable, real-time wearable sensors to aid people with PD