Filters: Keyword is White noise  [Clear All Filters]
Khatod, V., Manolova, A..  2020.  Effects of Man in the Middle (MITM) Attack on Bit Error Rate of Bluetooth System. 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT NCON). :153—157.
The ad-hoc network formed by Bluetooth works on radio frequency links. The security aspect of Bluetooth has to be handled more carefully. The radio frequency waves have a characteristic that the waves can pierce the obstructions in the communication path, get rid of the requirement of line of sight between the communicating devices. We propose a software model of man-in-the-middle attack along with unauthorized and authorized transmitter and receiver. Advanced White Gaussian Noise channel is simulated in the designed architecture. The transmitter uses Gaussian Frequency Shift Keying (GFSK) modulation like in Bluetooth. The receiver uses GFSK demodulation. In order to validate the performance of the designed system, bit error rate (BER) measurements are taken with respect to different time intervals. We found that BER drops roughly 18% if hopping duration of 150 seconds is chosen. We propose that a Bluetooth system with hopping rate of 0.006 Hz is used instead of 10Hz.
Thoen, B., Wielandt, S., Strycker, L. De.  2018.  Fingerprinting Method for Acoustic Localization Using Low-Profile Microphone Arrays. 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN). :1–7.

Indoor localization of unknown acoustic events with MEMS microphone arrays have a huge potential in applications like home assisted living and surveillance. This article presents an Angle of Arrival (AoA) fingerprinting method for use in Wireless Acoustic Sensor Networks (WASNs) with low-profile microphone arrays. In a first research phase, acoustic measurements are performed in an anechoic room to evaluate two computationally efficient time domain delay-based AoA algorithms: one based on dot product calculations and another based on dot products with a PHAse Transform (PHAT). The evaluation of the algorithms is conducted with two sound events: white noise and a female voice. The algorithms are able to calculate the AoA with Root Mean Square Errors (RMSEs) of 3.5° for white noise and 9.8° to 16° for female vocal sounds. In the second research phase, an AoA fingerprinting algorithm is developed for acoustic event localization. The proposed solution is experimentally verified in a room of 4.25 m by 9.20 m with 4 acoustic sensor nodes. Acoustic fingerprints of white noise, recorded along a predefined grid in the room, are used to localize white noise and vocal sounds. The localization errors are evaluated using one node at a time, resulting in mean localization errors between 0.65 m and 0.98 m for white noise and between 1.18 m and 1.52 m for vocal sounds.

Sima, Mihai, Brisson, André.  2017.  Whitenoise encryption implementation with increased robustness to side-channel attacks. 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1–4.
Two design techniques improve the robustness of Whitenoise encryption algorithm implementation to side-channel attacks based on dynamic and/or static power consumption. The first technique conceals the power consumption and has linear cost. The second technique randomizes the power consumption and has quadratic cost. These techniques are not mutually exclusive; their synergy provides a good robustness to power analysis attacks. Other circuit-level protection can be applied on top of the proposed techniques, opening the avenue for generating very robust implementations.
Shen, G., Tang, Y., Li, S., Chen, J., Yang, B..  2017.  A General Framework of Hardware Trojan Detection: Two-Level Temperature Difference Based Thermal Map Analysis. 2017 11th IEEE International Conference on Anti-Counterfeiting, Security, and Identification (ASID). :172–178.

With the globalization of integrated circuit design and manufacturing, Hardware Trojan have posed serious threats to the security of commercial chips. In this paper, we propose the framework of two-level temperature difference based thermal map analysis detection method. In our proposed method, thermal maps of an operating chip during a period are captured, and they are differentiated with the thermal maps of a golden model. Then every pixel's differential temperature of differential thermal maps is extracted and compared with other pixel's. To mitigate the Gaussian white noise and to differentiate the information of Hardware Trojan from the information of normal circuits, Kalman filter algorithm is involved. In our experiment, FPGAs configured with equivalent circuits are utilized to simulate the real chips to validate our proposed approach. The experimental result reveals that our proposed framework can detect Hardware Trojan whose power proportion magnitude is 10''3.

Khan, M., Reza, M. Q., Sirdeshmukh, S. P. S. M. A..  2017.  A prototype model development for classification of material using acoustic resonance spectroscopy. 2017 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). :128–131.

In this work, a measurement system is developed based on acoustic resonance which can be used for classification of materials. Basically, the inspection methods based on acoustic, utilized for containers screening in the field, identification of defective pills hold high significance in the fields of health, security and protection. However, such techniques are constrained by costly instrumentation, offline analysis and complexities identified with transducer holder physical coupling. So a simple, non-destructive and amazingly cost effective technique in view of acoustic resonance has been formulated here for quick data acquisition and analysis of acoustic signature of liquids for their constituent identification and classification. In this system, there are two ceramic coated piezoelectric transducers attached at both ends of V-shaped glass, one is act as transmitter and another as receiver. The transmitter generates sound with the help of white noise generator. The pick up transducer on another end of the V-shaped glass rod detects the transmitted signal. The recording is being done with arduino interfaced to computer. The FFTs of recorded signals are being analyzed and the resulted resonant frequency observed for water, water+salt and water+sugar are 4.8 KHz, 6.8 KHz and 3.2 KHz respectively. The different resonant frequency in case different sample is being observed which shows that the developed prototype model effectively classifying the materials.

Jialing Mo, Qiang He, Weiping Hu.  2014.  An adaptive threshold de-noising method based on EEMD. Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on. :209-214.

In view of the difficulty in selecting wavelet base and decomposition level for wavelet-based de-noising method, this paper proposes an adaptive de-noising method based on Ensemble Empirical Mode Decomposition (EEMD). The autocorrelation, cross-correlation method is used to adaptively find the signal-to-noise boundary layer of the EEMD in this method. Then the noise dominant layer is filtered directly and the signal dominant layer is threshold de-noised. Finally, the de-noising signal is reconstructed by each layer component which is de-noised. This method solves the problem of mode mixing in Empirical Mode Decomposition (EMD) by using EEMD and combines the advantage of wavelet threshold. In this paper, we focus on the analysis and verification of the correctness of the adaptive determination of the noise dominant layer. The simulation experiment results prove that this de-noising method is efficient and has good adaptability.