Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76 ) and key strength (entropy approximately 15 bits).
Device pairing by shaking was first presented in Smart-Its Friends'' @cite_0 . The drawback of this technique was lacking of authentication of the involved devices in the interaction. Another closely related work included accelerometer based analysis to determine whether the devices are carried by the same person or not @cite_8 . The feature extraction algorithms used in @cite_2 includes coherence measure, which was originally introduced in @cite_8 , and quantized FFT coefficients. In @cite_1 , a similar approach in which acceleration signal was used for key generation was presented with a difference in using time domain acceleration features contrary to @cite_2 where only frequency domain features were used. They claim that the same key would rather likely be generated for different shaking processes if the key generation was based on a frequency-based technique. They concluded that the frequency domain is not suitable for key generation.
@cite_0 @cite_1 @cite_2 @cite_8
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A challenge in facilitating spontaneous mobile interactions is to provide pairing methods that are both intuitive and secure. Simultaneous shaking is proposed as a novel and easy-to-use mechanism for pairing of small mobile devices. The underlying principle is to use common movement as a secret that the involved devices share for mutual authentication. We present two concrete methods, ShaVe and ShaCK, in which sensing and analysis of shaking movement is combined with cryptographic protocols for secure authentication. ShaVe is based on initial key exchange followed by exchange and comparison of sensor data for verification of key authenticity. ShaCK, in contrast, is based on matching features extracted from the sensor data to construct a cryptographic key. The classification algorithms used in our approach are shown to robustly separate simultaneous shaking of two devices from other concurrent movement of a pair of devices, with a false negative rate of under 12 percent. A user study confirms that the method is intuitive and easy to use, as users can shake devices in an arbitrary pattern. In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatch's accelerometer sensor, the second design uses the rotation of arms captured by a smartwatch's gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data collection were separated by at least ten minutes, whereas the phases of data collection were separated by at least three months. Under the intra-session environment, all of the twelve authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0 and dynamic false reject rate (DFRR) of 0 . For the inter-session environment, feature level fusion-based design with classifier k-NN achieves the best error rates that are a mean DFAR of 2.2 and DFRR of 4.2 . The DFAR and DFRR increased from 5.68 and 4.23 to 15.03 and 14.62 respectively when feature level fusion-based design with classifier k-NN was tested under the inter-phase environment on a dataset of 12 users. Standard Mel frequency cepstrum coefficient (MFCC) computation technique utilizes discrete cosine transform (DCT) for decorrelating log energies of filter bank output. The use of DCT is reasonable here as the covariance matrix of Mel filter bank log energy (MFLE) can be compared with that of highly correlated Markov-I process. This full-band based MFCC computation technique where each of the filter bank output has contribution to all coefficients, has two main disadvantages. First, the covariance matrix of the log energies does not exactly follow Markov-I property. Second, full-band based MFCC feature gets severely degraded when speech signal is corrupted with narrow-band channel noise, though few filter bank outputs may remain unaffected. In this work, we have studied a class of linear transformation techniques based on block wise transformation of MFLE which effectively decorrelate the filter bank log energies and also capture speech information in an efficient manner. A thorough study has been carried out on the block based transformation approach by investigating a new partitioning technique that highlights associated advantages. This article also reports a novel feature extraction scheme which captures complementary information to wide band information; that otherwise remains undetected by standard MFCC and proposed block transform (BT) techniques. The proposed features are evaluated on NIST SRE databases using Gaussian mixture model-universal background model (GMM-UBM) based speaker recognition system. We have obtained significant performance improvement over baseline features for both matched and mismatched condition, also for standard and narrow-band noises. The proposed method achieves significant performance improvement in presence of narrow-band noise when clubbed with missing feature theory based score computation scheme. The ubiquity of wearable and implantable devices has sparked a new set of mobile computing applications that leverage the abundant information from sensors. For many of these applications, ensuring the security of communication between legitimate devices is a crucial problem. In this paper, we design Walkie-Talkie, a shared secret key generation scheme that allows two legitimate devices to establish a common cryptographic key by exploiting users' walking characteristics (gait). The intuition is that the sensors on different locations on the same body experience similar accelerometer signals when the user is walking. However, the accelerometer also captures motion signals produced by other body parts (e.g., swinging arms). It is shown that a Blind Source Separation (BSS) technique can extract the informative signal produced by the unique gait patterns. Our experimental results show that the keys generated by two independent devices on the same body are able to achieve up to a 100 bit agreement rate. To demonstrate the feasibility, the proposed key generation scheme is implemented on modern smartphones. The evaluation results show that the proposed scheme can run in real-time on modern mobile devices and incurs low system overhead. Existing secret key extraction techniques use quantization to map wireless channel amplitudes to secret bits. This paper shows that such techniques are highly prone to environment and local noise effects: They have very high mismatch rates between the two nodes that measure the channel between them. This paper advocates using the shape of the channel instead of the size (or amplitude) of the channel. It shows that this new paradigm shift is significantly robust against environmental and local noises. We refer to this shape-based technique as Puzzle. Implementation in a software-defined radio (SDR) platform demonstrates that Puzzle has a 63 reduction in bit mismatch rate than the state-of-art frequency domain approach (CSI-2bit). Experiments also show that unlike the state-of-the-art received signal strength (RSS)-based methods like ASBG, Puzzle is robust against an attack in which an eavesdropper can predict the secret bits using planned movements. A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers. Machine learning research has, to a great extent, ignored an important aspect of many real world applications: time. Existing concept learners predominantly operate on a static set of attributes; for example, classifying flowers described by leaf size, pet al colour and pet al count. However, many real datasets are not static; they cannot sensibly be represented as a fixed set of attributes. Rather, the examples are expressed as features that vary temporally, and it is the temporal variation itself that is used for classification. Consider a simple gesture recognition domain, in which the temporal features are the position of the hands, finger bends, and so on. Looking at the position of the hand at one point in time is not likely to lead to a successful classification; it is only by analysing changes in position that recognition is possible. This thesis presents a new technique for temporal classification. By extracting sub-events from the training instances and parameterising them to allow feature construction for a subsequent learning process, it is able to employ background knowledge and express learnt concepts in terms of the background knowledge. The novel results of the thesis are: a temporal learner capable of producing comprehensible and accurate classifiers for multivariate time series that can learn from a small number of instances and can integrate non-temporal features; a feature construction technique that parameterises sub-events of the training set and clusters them to construct features for a propositional learner; and a technique for post-processing classification rules produced by the learner to give a comprehensible description expressed in the same form as the original background knowledge. The thesis discusses the implementation of TClass, a temporal learner. Results show rules that are comprehensible in many cases and accuracy results close to or better than existing technique—over 98 per cent for sign language and 72 per cent for ECGs (equivalent to the accuracy of a human cardiologist). One further surprising result is that a small set of very primitive sub-events proves to be functional, avoiding the need for labour-intensive background knowledge if it is not available. As the proliferation of pervasive and ubiquitous computing devices continues, users will carry more devices. Without the ability for these devices to unobtrusively interact with one another, the user’s attention will be spent on coordinating, rather than using, these devices. We present a method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain. We also show that these low-cost sensors perform similarly to more expensive accelerometers for the frequency range of human motion, 0 to 10Hz. We also present results from a large test group illustrating the algorithm’s robustness and its ability to withstand real world time delays, crucial for wireless technologies like Bluetooth and 802.11. We present results that show that our technique is 100 accurate using a sliding window of 8 seconds of data when the devices are carried in the same location on the body, is tolerant to inter-device communication latencies, and requires little communication bandwidth. In addition we present results for when devices are carried on different parts of the body. Abstract Inspired by the theory of Leitner׳s learning box from the field of psychology, we propose DropSample , a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR). According to the principle of DropSample , each training sample is associated with a quota function that is dynamically adjusted on the basis of the classification confidence given by the DCNN softmax output. After a learning iteration, samples with low confidence will have a higher frequency of being selected as training data; in contrast, well-trained and well-recognized samples with very high confidence will have a lower frequency of being involved in the ongoing training and can be gradually eliminated. As a result, the learning process becomes more efficient as it progresses. Furthermore, we investigate the use of domain-specific knowledge to enhance the performance of DCNN by adding a domain knowledge layer before the traditional CNN. By adopting DropSample together with different types of domain-specific knowledge, the accuracy of HCCR can be improved efficiently. Experiments on the CASIA-OLHDWB 1.0, CASIA-OLHWDB 1.1, and ICDAR 2013 online HCCR competition datasets yield outstanding recognition rates of 97.33 , 97.06 , and 97.51 respectively, all of which are significantly better than the previous best results reported in the literature. A new technique for classifying patterns of movement via electromyographic (EMG) signals is presented. Two methods (conventional autoregressive (AR) coefficients and cepstral coefficients) for extracting features from EMG signals and three classification algorithms (Euclidean Distance Measure (EDM), Weighted Distance Measure (WDM), and Maximum Likelihood Method (MLM)) for discriminating signals representative of broad classes of movements are described and compared. These three classifiers are derived from Bayes classifier with some assumptions, the relationship among them is discussed. The conventional MLM is modified to avoid heavy matrix inversion. Six able-bodied subjects with two pairs of surface electrodes located on bilateral sternocleidomastoid and upper trapezius muscles were studied in the experiment. The EMG signals of 20 repetitions of 10 motions were analyzed for each subject. Experimental results showed that mean recognition rate of the cepstral coefficients was at least 5 superior to that of the AR coefficients. The improvement achieved by the cepstral method was statistically significant for all the three classifiers. Reasons for the superiority of cepstral features were investigated from the feature space and frequency domain, respectively. The cepstral coefficients owned better cluster separability in feature space and they emphasized the more informative part in the frequency domain. The discrimination rate of the MLM was the highest among three classifiers. Incorporation of the cepstral features with the MLM could reduce the misclassification rate by 10.6 when compared with the combination of AR coefficients and EDM. Proper choice of five of ten motions could further raise the recognition rate to more than 95 . > Up to now, there has only been little work on using features from temporal approximations of signals for audio recognition. Time-frequency tradeoffs are an important issue in signal processing; sparse representations using overcomplete dictionaries may (or may not, depending on the dictionary) have more time-frequency flexibility than standard short-time Fourier transform. Also, the precise temporal structure of signals cannot be captured by spectral-based feature methods. Here, we present a biologically inspired three-step process for audio classification: 1) Efficient atomic functions are learned in an unsupervised manner on mixtures of percussion sounds (drum phrases), optimizing the length as well as the shape of the atoms. 2) An analog spike model is used to sparsely approximate percussion sound signals (bass drum, snare drum, hi-hat). The spike model consists of temporally shifted versions of the learned atomic functions, each having a precise temporal position and amplitude. To obtain the decomposition given a set of atomic functions, matching pursuit is used. 3) Features are extracted from the resulting spike representation of the signal. The classification accuracy of our method using a support vector machine (SVM) in a 3-class database transfer task is 87.8 . Using gammatone functions instead of the learned sparse functions yields an even better classification rate of 97.6 . Testing the features on sounds containing additive white Gaussian noise reveals that sparse approximation features are far more robust to such distortions than our benchmark feature set of timbre descriptor (TD) features. New approaches that generate secret keys from radio channel characteristics have been recently proposed. The security of these schemes usually relies on the reciprocity principle which states that the channel fluctuations can be a source of common randomness for two communicating peers and these fluctuations can't be measured by any eavesdropper. A validation of these properties for indoor environments is presented in this work. The shared keys are created by measuring the reciprocal channel features and converting this information to binary vectors using a quantization algorithm. This paper addresses the problem of quantization. It identifies an important weakness of existing key generation algorithms and it shows that the secret bits extraction has a significant impact on the robustness and security of these algorithms. A new adaptive quantization algorithm for secret key generation is presented. This method has the advantages to create sufficient long secret keys with a high key agreement ratio between authorized users without revealing information to the attacker. The new scheme is experimentally validated using Ultra Wide Band technology. Millimeter wave (mmWave) has great potential in realizing high data rates, thanks to the large spectral channels. It is considered as a key technology for fifth-generation (5G) wireless networks and is already used in wireless LAN (e.g., IEEE 802.11ad). Using mmWave for vehicular communications, however, is often viewed with some skepticism due to a misconception that the Doppler spread would become too large at these high frequencies. This is not necessarily true when directional beams are employed. In this paper, closed-form expressions relating the channel coherence time and beamwidth are derived. Unlike prior work that assumed perfect beam pointing, the pointing error due to the receiver motion is incorporated to show that there exists a nonzero optimal beamwidth that maximizes the coherence time. We define a novel concept of beam coherence time, which is an effective measure of beam alignment frequency. Using the derived correlation function, the channel coherence time, and the beam coherence time, an overall performance metric considering both the channel time variation and the beam alignment overhead is derived. Using this metric, it is shown that beam realignment in every beam coherence time performs better than beam realignment in every channel coherence time. The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94 in EER is observed on the FVC2002 DB3 database. We propose to establish a secure communication channel among devices based on similar audio patterns. Features from ambient audio are used to generate a shared cryptographic key between devices without exchanging information about the ambient audio itself or the features utilized for the key generation process. We explore a common audio-fingerprinting approach and account for the noise in the derived fingerprints by employing error correcting codes. This fuzzy-cryptography scheme enables the adaptation of a specific value for the tolerated noise among fingerprints based on environmental conditions by altering the parameters of the error correction and the length of the audio samples utilized. In this paper, we experimentally verify the feasibility of the protocol in four different realistic settings and a laboratory experiment. The case studies include an office setting, a scenario where an attacker is capable of reproducing parts of the audio context, a setting near a traffic loaded road, and a crowded canteen environment. We apply statistical tests to show that the entropy of fingerprints based on ambient audio is high. The proposed scheme constitutes a totally unobtrusive but cryptographically strong security mechanism based on contextual information. This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network. Previous approaches, rather than computing a loss on the reconstructed signal, used a surrogate loss based on the target STFT magnitudes. This ignores reconstruction error introduced by phase inconsistency. In our approach, the loss function is directly defined on the reconstructed signals, which are optimized for best separation. In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers. While mask values are typically limited to lie between zero and one for approaches using the mixture phase for reconstruction, this limitation is less relevant if the estimated magnitudes are to be used together with phase reconstruction. We thus propose several novel activation functions for the output layer of the T-F masking, to allow mask values beyond one. On the publicly-available wsj0-2mix dataset, our approach achieves state-of-the-art 12.6 dB scale-invariant signal-to-distortion ratio (SI-SDR) and 13.1 dB SDR, revealing new possibilities for deep learning based phase reconstruction and representing a fundamental progress towards solving the notoriously-hard cocktail party problem. We design and analyze a method to extract secret keys from the randomness inherent to wireless channels. We study a channel model for a multipath wireless channel and exploit the channel diversity in generating secret key bits. We compare the key extraction methods based both on entire channel state information (CSI) and on single channel parameter such as the received signal strength indicators (RSSI). Due to the reduction in the degree-of-freedom when going from CSI to RSSI, the rate of key extraction based on CSI is far higher than that based on RSSI. This suggests that exploiting channel diversity and making CSI information available to higher layers would greatly benefit the secret key generation. We propose a key generation system based on low-density parity-check (LDPC) codes and describe the design and performance of two systems: one based on binary LDPC codes and the other (useful at higher signal-to-noise ratios) based on four-ary LDPC codes. With the rapid development of wearable sensors and pervasive computing technologies including smartphones, acceleration-based human activity recognition is receiving increased attention for medical research applications. Motivated by the “weightlessness” feature, here we apply a bidirectional feature during the feature extraction phase of activity recognition; however, since the bidirectional feature has two components, they cannot simply be concatenated into a long vector, but can be naturally treated as a second-order tensor. Therefore, we propose a new tensor-based feature selection method termed tensor manifold discriminant projections (TMDP). TMDP simultaneously considers: 1) applying an optimization criterion that can directly process the tensor spectral analysis problem, thereby decreasing the computational cost compared to traditional tensor-based feature selection methods; 2) extracting local rank information by finding a tensor subspace that preserves the rank order information of the within-class input samples; and 3) extracting discriminant information by maximizing the sum of distances between every sample and their interclass sample mean. Experiments on the naturalistic mobile devices-based human activity 2.0 dataset are performed to demonstrate the effectiveness and robustness of TMDP. In this paper we present a signal processing methodology for sharing symmetric keys in personal area networks. Symmetric encryption and decryption are commonly used because of limitations in computing power and energy consumption. However, key sharing still imposes challenges regarding usability, computational complexity of algebraic key exchange algorithms, and security. Our approach is that keys are generated locally on devices by shaking them, and that the keys are equal if and only if the devices are shaken together. Based on practical assessments, we show that the key generation algorithm is able to generate keys from acceleration data with an average entropy of 13bit key in 70 of the cases. It is well known that at low-bit-rate block discrete cosine transform compressed image exhibits visually annoying blocking and ringing artifacts. In this paper, we propose a noniterative, wavelet-based deblocking algorithm to reduce both types of artifacts. The algorithm exploits the fact that block discontinuities are constrained by the dc quantization interval of the quantization table, as well as the behavior of wavelet modulus maxima evolution across wavelet scales to derive appropriate threshold maps at different wavelet scales. Since ringing artifacts occur near strong edges, which can be located either along block boundaries or within blocks, suppression of block discontinuities does not always reduce ringing artifacts. By exploiting the behavior of ringing artifacts in the wavelet domain, we propose a simple yet effective method for the suppression of such artifacts. The proposed algorithm can suppress both block discontinuities and ringing artifacts effectively while preserving true edges and textural information. Simulation results and extensive comparative study with both iterative and noniterative methods reported in the literature have shown the effectiveness of our algorithm.